docs: Linux updates, other distributions and WSL; the 0.1.14 stall fix; the full expert profile

serve: the prompt progress line says how far the engine is (N of M tokens), not only the prompt's size (#29 )
Engine 0.1.14; setup requires it (the PCIe share without host CUDA calls in a verify window, #31 )
2026-11-07 14:21:06 -10:00 · 2026-09-28 11:46:37 +02:00 · 2026-09-28 11:22:55 +02:00 · 2026-09-28 11:21:01 +02:00 · 2026-09-28 11:04:17 +02:00 · 2026-09-28 11:02:44 +02:00
80 changed files with 8582 additions and 765 deletions
@@ -46,3 +46,4 @@ build-vision.bat
 /dist/
 *.done
 /third_party/*.zip
+
@@ -8,7 +8,14 @@
 # written against sm_120 (RTX 5000 / Blackwell) and silently building for an older arch would produce a
 # binary that runs and is wrong about its own performance, which is the worst of both outcomes.
 cmake_minimum_required(VERSION 3.24)
-project(strata VERSION 0.1.0 LANGUAGES CXX)
+project(strata VERSION 0.1.14 LANGUAGES CXX)
+add_compile_definitions(STRATA_VERSION="${PROJECT_VERSION}")
+# issue #31: release builds keep a PDB beside the exe (not shipped in it), so a minidump from a stalled engine can
+# be read back to the exact source line
+if(MSVC AND CMAKE_BUILD_TYPE STREQUAL "Release")
+  add_compile_options($<$<COMPILE_LANGUAGE:CXX>:/Zi>)
+  add_link_options(/DEBUG /OPT:REF /OPT:ICF)
+endif()

 # The prebuilt release (setup.py downloads it, so users need no compiler): ggml's CPU backend for any AVX2 CPU
 # instead of this machine's (Strata's own AVX-512 kernels are per-file and chosen at run time either way), and
@@ -178,7 +185,9 @@ if(STRATA_ENABLE_CUDA)
                          src/kernels/cuda/ple.cu
                          src/kernels/cuda/kv_q8.cu
                          src/kernels/cuda/kv_stream.cu
+                          src/kernels/cuda/kv_q4.cu
                          src/kernels/cuda/fused_gr.cu
+                          src/kernels/cuda/cvec.cu
                          src/kernels/cuda/qsa_decode_attn.cu
                          src/kernels/cuda/fused_gdn.cu
                          src/kernels/cuda/dequant_bf16.cu
@@ -363,6 +372,16 @@ if(STRATA_ENABLE_CUDA)
  target_link_libraries(kv_stream_parity PRIVATE strata_kernels CUDA::cudart)
  add_test(NAME kv_stream_parity COMMAND kv_stream_parity)

+  # PR #21: Q4_0 KV with Walsh-Hadamard rotation vs a host reference (GPU, synthetic, no model).
+  add_executable(kv_q4_parity src/kernels/kv_q4_parity.cpp)
+  target_include_directories(kv_q4_parity PRIVATE ${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES})
+  target_link_libraries(kv_q4_parity PRIVATE strata_kernels CUDA::cudart)
+  add_test(NAME kv_q4_parity COMMAND kv_q4_parity)
+  add_executable(cvec_parity src/kernels/cvec_parity.cpp)
+  target_include_directories(cvec_parity PRIVATE ${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES})
+  target_link_libraries(cvec_parity PRIVATE strata_kernels CUDA::cudart)
+  add_test(NAME cvec_parity COMMAND cvec_parity)
+
  # Plan v0.3 P3: native MMVQ for 1..8 tokens (bitwise selftest + --bench timing); synthetic data, no model.
  if(STRATA_BUILD_TESTS)
  add_executable(native_mmvq_multi bench/micro/native_mmvq_multi.cpp)
@@ -424,7 +443,7 @@ endif()
 #     worse opt-out.  `cpu_require_expert_support()` therefore refuses at startup on a CPU without the
 #     features, and the fallback exists for tests, exactly as P2.S3 specifies.
 # Plan v0.3 P6: speculation components that need no GPU.
-add_library(strata_spec STATIC src/spec/suffix_drafter.cpp src/spec/controller.cpp)
+add_library(strata_spec STATIC src/spec/suffix_drafter.cpp src/spec/controller.cpp src/spec/draft_policy.cpp)
 target_include_directories(strata_spec PUBLIC include)
 if(STRATA_BUILD_TESTS)
  add_executable(suffix_drafter_test src/spec/suffix_drafter_test.cpp)
@@ -433,6 +452,9 @@ if(STRATA_BUILD_TESTS)
  add_executable(controller_test src/spec/controller_test.cpp)
  target_link_libraries(controller_test PRIVATE strata_spec)
  add_test(NAME controller_test COMMAND controller_test)
+  add_executable(draft_policy_test src/spec/draft_policy_test.cpp)
+  target_link_libraries(draft_policy_test PRIVATE strata_spec)
+  add_test(NAME draft_policy_test COMMAND draft_policy_test)
 endif()

 add_library(strata_kernels_cpu STATIC src/kernels/cpu/expert.cpp src/kernels/cpu/pool.cpp
@@ -492,16 +514,43 @@ if(STRATA_NATIVE_EXPERTS)
  endif()
  # part of strata_kernels_cpu (the pool calls these and they call its CPU probe: one library, so GNU ld's
  # one-pass static linking has no cycle to resolve)
-  target_sources(strata_kernels_cpu PRIVATE src/kernels/cpu/native_expert.cpp src/kernels/cpu/iq_avx512.cpp)
+  target_sources(strata_kernels_cpu PRIVATE src/kernels/cpu/native_expert.cpp src/kernels/cpu/iq_avx512.cpp
+                                           src/kernels/cpu/iq_avx2.cpp)
  target_include_directories(strata_kernels_cpu PRIVATE third_party/ggml)
  if(MSVC)
    set_source_files_properties(src/kernels/cpu/iq_avx512.cpp PROPERTIES COMPILE_OPTIONS "/arch:AVX512")
+    set_source_files_properties(src/kernels/cpu/iq_avx2.cpp PROPERTIES COMPILE_OPTIONS "/arch:AVX2")
  else()
    set_source_files_properties(src/kernels/cpu/iq_avx512.cpp PROPERTIES COMPILE_OPTIONS
                                "-mavx512f;-mavx512bw;-mavx512vl;-mavx512dq;-mavx512vnni;-mavx512vbmi;-mf16c;-mfma")
+    set_source_files_properties(src/kernels/cpu/iq_avx2.cpp PROPERTIES COMPILE_OPTIONS "-mavx2;-mfma;-mf16c")
  endif()
  target_link_libraries(strata_kernels_cpu PUBLIC ggml-cpu ggml-base)
  target_compile_definitions(strata_kernels_cpu PUBLIC STRATA_NATIVE_EXPERTS=1)
+  if(STRATA_ENABLE_CUDA)
+    # prompt-speed plan step 2b: llama.cpp's MMQ kernels (ggml-cuda, MIT) for the prompt path's experts, compiled
+    # from the same checkout as ggml above - only mmq.cuh's instances for the packs' types and the quantizer, with
+    # the few host symbols of ggml-cuda.cu they use in src/prefill/ggml_cuda_host.cu
+    if(STRATA_GGML_DIR)
+      set(_strata_gg ${STRATA_GGML_DIR}/ggml)
+    else()
+      set(_strata_gg ${strata_llamacpp_SOURCE_DIR}/ggml)
+    endif()
+    set(_strata_mmq ${_strata_gg}/src/ggml-cuda)
+    add_library(strata_mmq STATIC src/prefill/moe_mmq.cu src/prefill/ggml_cuda_host.cu ${_strata_mmq}/quantize.cu)
+    foreach(_t q2_0 iq2_xxs iq2_xs iq2_s iq3_xxs iq3_s iq4_nl iq4_xs)
+      target_sources(strata_mmq PRIVATE ${_strata_mmq}/template-instances/mmq-instance-${_t}.cu)
+    endforeach()
+    target_include_directories(strata_mmq PRIVATE ${_strata_gg}/include ${_strata_gg}/src ${_strata_mmq}
+                               PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/include)
+    target_compile_options(strata_mmq PRIVATE $<$<COMPILE_LANGUAGE:CUDA>:--extended-lambda -use_fast_math>)
+    if(MSVC)
+      target_compile_options(strata_mmq PRIVATE $<$<COMPILE_LANGUAGE:CUDA>:-Xcompiler=/bigobj>)
+    endif()
+    target_link_libraries(strata_mmq PUBLIC ggml-base CUDA::cudart)
+    target_link_libraries(strata_prefill PUBLIC strata_mmq)
+    target_compile_definitions(strata_prefill PUBLIC STRATA_PREFILL_MMQ=1)
+  endif()
  if(STRATA_ENABLE_CUDA)
    add_executable(native_expert_parity src/kernels/native_expert_parity.cpp)
    target_include_directories(native_expert_parity PRIVATE ${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES})
@@ -535,6 +584,10 @@ if(STRATA_BUILD_TESTS)
  add_executable(pool_test src/kernels/cpu/pool_test.cpp)
  target_link_libraries(pool_test PRIVATE strata_kernels_cpu)
  add_test(NAME pool_test COMMAND pool_test --selftest WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
+  # issue #29: tiny batches with sleeping workers, as on a big-VRAM card; a hang fails it
+  add_executable(pool_stress src/kernels/cpu/pool_stress.cpp)
+  target_link_libraries(pool_stress PRIVATE strata_kernels_cpu)
+  add_test(NAME pool_stress COMMAND pool_stress 20)
 endif()

 if(STRATA_BUILD_TESTS)
@@ -3,54 +3,22 @@
 <p align="center"><b>Run a 125-billion-parameter AI model on a normal gaming PC</b><br>
 one NVIDIA card (12-24 GB) + 64 GB of RAM · Windows or Linux · one click to install</p>

+<p align="center"><a href="https://github.com/Niko1221/Strata/releases/download/v0.1.10/Pagoda.mp4"><img src="docs/media/pagoda-preview.webp" width="720" alt="A voxel pagoda garden that Strata's model wrote, running in the browser"></a><br>
+<sub>A voxel pagoda garden, 1 shot prompt running on an RTX 5070 with Strata (IQ3_S, 128K context) ·
+<a href="https://github.com/Niko1221/Strata/releases/download/v0.1.10/Pagoda.mp4">full video (49 s)</a></sub></p>
+
 Strata runs **[Qwen3.8-Flash-Next](https://huggingface.co/Qwen/Qwen3.8-Flash-Next)** - a large, smart AI model that
 normally needs a server - on your own PC. It writes its answers at **60-95 tokens per second** (a token is about ¾
 of a word): faster than you can read.

- **Private:** everything runs on your PC. Nothing is sent anywhere.
- **Works with your apps:** chat apps, coding agents and scripts that speak the OpenAI or Anthropic API just work.
- **Sees pictures** too, if you want (screenshots, photos, scanned pages).
 - **Free and open source.**

-> **Jump to:** [Is my PC enough?](#is-my-pc-enough) · [Install](#install-3-steps) · [How fast?](#how-fast-is-it) ·
-> [Which model?](#which-model-should-i-pick) · [Using it](#using-it) · [Problems?](#something-went-wrong) ·
-> [How it works](#how-does-it-work) · [All the details](docs/DETAILS.md)
+> **Jump to:** [How fast?](#how-fast-is-it) · [Which model?](#which-model-should-i-pick) · [Install](#install) ·
+> [Using it](#using-it) · [Problems?](#something-went-wrong) · [How it works](#how-does-it-work) ·
+> [All the details](docs/DETAILS.md)

 ---

-## Is my PC enough?
-
-| You need | |
-| --- | --- |
-| **Graphics card** | NVIDIA RTX 30, 40 or 50 series with **12 GB of VRAM or more** |
-| **Memory (RAM)** | **64 GB** |
-| **Free disk space** | ~80 GB (an SSD makes the first start much faster) |
-| **System** | Windows 10/11, or Linux |
-
-That's it. The only thing you install yourself is a current **NVIDIA driver**
-([nvidia.com/drivers](https://www.nvidia.com/drivers) or the NVIDIA App). Everything else - Python, the engine, the
-model - is set up for you.
-
-## Install (3 steps)
-
-**Windows**
-
-1. [Download this project](https://github.com/Niko1221/Strata/archive/refs/heads/main.zip) and unzip it (or `git clone` it).
-2. Double-click **`START-HERE.bat`**.
-3. Answer 4 questions - or just press Enter each time for the recommended choice:
-   - **Which model?** The original, or Swift 1.5 (a version that thinks shorter and answers sooner)
-   - **Which size?** Q2_0, IQ2_XS, IQ3_XXS or IQ3_S - see [which model](#which-model-should-i-pick)
-   - **How much context?** How much text it can keep in mind at once (it suggests one for your card)
-   - **Images?** Whether it should also read pictures
-
-Then it downloads everything (the model is ~70 GB, so the first time takes a while - you can stop and it picks up
-where it left off) and **starts the model**. Your browser opens a chat page at `http://127.0.0.1:8080`.
-
-**Next time**, just double-click `START-HERE.bat` again: it starts right away, nothing is downloaded twice. Close its
-window to stop the model.
-
-**Linux:** run `./setup.sh` - same questions, same result.
-
 ## How fast is it?

 Measured on an RTX 5070 (12 GB), a Ryzen 5 7600 and 64 GB of RAM:
@@ -73,49 +41,105 @@ A card with more VRAM is faster, because more of the model fits on the GPU: an R

 **The size** (the same model, compressed more or less):

-| Size | Download | Speed | Quality | Pick it if... |
-| --- | ---: | --- | --- | --- |
-| **Q2_0** | 66 GB | fastest | good | you want speed |
-| **IQ2_XS** | 68 GB | fast | better | you want a good all-rounder (**recommended**) |
-| **IQ3_XXS** | 76 GB | slower | great | you want better answers (uses 43 GB of your 64 GB RAM) |
-| **IQ3_S** | 84 GB | slowest | best: matches the full model on the published tests | you want the very best answers (original model only; uses 50 GB of your 64 GB RAM, so close other big programs) |
+| Model | RAM+VRAM Requirements | Speed | Quality |
+| --- | ---: | --- | --- |
+| **Q2_0** | 37.6 GB | fastest | good |
+| **IQ2_XS** | 39.2 GB | fast | better (**recommended**) |
+| **IQ3_XXS** | 47.0 GB | slower | great |
+| **IQ3_S** | 54.8 GB | slowest | best: matches the full model on the published tests (original model only) |
+
+**Will it fit?** Shard 1 is the part of the model that gets loaded when it starts: its experts go into your **RAM**,
+the rest onto your graphics card (the second shard, a 29 GB lookup table, stays on the SSD). So it fits when your
+**RAM is at least shard 1 + about 10 GB** for Windows and your other programs. With 64 GB of RAM every size fits
+(IQ3_S with little else open); with 48 GB, Q2_0 and IQ2_XS. A bigger graphics card makes it faster, but it doesn't
+lower the RAM needed.

 **The version:**

 - **Qwen3.8-Flash-Next** - the original.
 - **[Swift 1.5](https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF)** - a fine-tune by UkisAI
  that thinks much shorter before answering, so you get the answer sooner, with about the same quality. Same speed per
-  token. Its own license applies (see its page).
+  token, and about the same RAM as the same size of the original (no IQ3_S). Its own license applies (see its page).

 Not sure? Take **IQ2_XS**. You can add another one later with `START-HERE.bat --setup`.

+## Install
+
+**You need:** an NVIDIA RTX 30, 40 or 50 card with 12 GB of VRAM or more, enough RAM for the size you pick (above),
+~80 GB of free disk space (an SSD makes the first start much faster), and Windows 10/11 or Linux. The only thing you
+install yourself is a current **NVIDIA driver** ([nvidia.com/drivers](https://www.nvidia.com/drivers) or the NVIDIA
+App). Everything else - Python, the engine, the model - is set up for you.
+
+**Windows**
+
+1. [Download this project](https://github.com/Niko1221/Strata/archive/refs/heads/main.zip) and unzip it (or `git clone` it).
+2. Double-click **`START-HERE.bat`**.
+3. Answer a few questions - or just press Enter each time for the recommended choice:
+   - **Which model and size?** The original or Swift 1.5, and Q2_0, IQ2_XS, IQ3_XXS or IQ3_S - see [above](#which-model-should-i-pick)
+   - **How much context?** How much text it can keep in mind at once (it suggests one for your card)
+   - **Images?** Whether it should also read pictures
+   - **Experimental speed projection?** Off unless you say yes - [read what it does](docs/DETAILS.md#experimental-speed-projection-experimental-off-by-default) first
+
+Then it downloads everything (the model is ~70 GB, so the first time takes a while - you can stop and it picks up
+where it left off) and **starts the model**. Your browser opens the Strata app at `http://127.0.0.1:8080`.
+
+**Next time**, just double-click `START-HERE.bat` again: it starts right away, nothing is downloaded twice. Close its
+window to stop the model.
+
+**Linux:** run `./setup.sh` - same questions, same result.
+
 ## Using it

- **Chat in the browser:** `http://127.0.0.1:8080` - a simple chat page (it opens by itself when the model starts).
+<p align="center"><img src="docs/media/runpagoda.png" width="900" alt="The Strata app's Monitor tab next to a coding agent"><br>
+<sub>The Strata app's <b>Monitor</b> (left) while a coding agent writes the pagoda garden from the video (right)</sub></p>
+
+- **In the browser:** `http://127.0.0.1:8080` - the Strata app (it opens by itself when the model starts): **Chat**, a
+  live **Monitor** of the model and your GPU/CPU/RAM, and **About** with the settings and addresses.
 - **Chat in the terminal:** `.venv\Scripts\python chat.py`
 - **Your apps and coding agents:** add it as an "OpenAI-compatible" provider with base URL
  **`http://127.0.0.1:8080/v1`**, any API key and any model name. Apps that use Anthropic's API: `http://127.0.0.1:8080/v1/messages`.
 - **Thinking:** the model thinks before it answers. Choose **off, low, medium or high** - in the chat page menu, with
  `/think low` in `chat.py`, or with your app's "reasoning effort" setting. Off is fastest; high is best for hard questions.
 - **Pictures:** in the chat page click **Picture**; in `chat.py` type `/image <path>`; in apps just attach them.
- **From your phone or another PC:** see the [details](docs/DETAILS.md#using-it) (set an API key first).
+- **From your phone or another PC:** `START-HERE.bat --setup --host 0.0.0.0 --api-key <secret>`, then open the
+  address the server window prints; see the [details](docs/DETAILS.md#using-it).
+- **Experimental speed projection (off by default):** an experimental control vector that setup can turn on; it
+  changes how the model answers - read [what it does](docs/DETAILS.md#experimental-speed-projection-experimental-off-by-default) first.

 **Good to know:** it answers one request at a time. The first message of a chat is read in full (about 1 minute per
 30,000 tokens); after that it keeps the conversation and reads only what is new, so follow-ups start in seconds.

 ## Something went wrong?

-| What you see | What to do |
-| --- | --- |
-| `the NVIDIA driver is too old` | Update the driver (NVIDIA App or nvidia.com/drivers), restart the PC, run `START-HERE.bat` again. |
-| It stopped during download or setup | Run `START-HERE.bat` again - it continues where it stopped. |
-| `port 8080 is already in use` | Strata is already running - look for its window. |
-| The first start takes minutes | Normal: it loads 35-43 GB into RAM. The next start is faster. |
-| Slow, and the disk light is busy | Not enough free RAM: close other programs (browsers use a lot), or pick Q2_0 / IQ2_XS. |
-| "prompt exceeds the context" | The conversation is longer than the context you chose: run `START-HERE.bat --setup` and pick more. |
+**My PC froze, or got very slow, the first time Strata started.**
+That's normal the first time. Strata loads 35-55 GB into your RAM, locks part of it for the graphics card, and works
+out how much of the model fits on your GPU. The mouse can freeze for a few minutes. **Wait, and don't close the
+window.** The next starts are much faster. Still frozen after 10 minutes? Restart the PC, close other programs
+(browsers use a lot of RAM) and try again. If it keeps happening, pick a smaller size (Q2_0 or IQ2_XS).

-More in the [full troubleshooting table](docs/DETAILS.md#troubleshooting). Still stuck? Open an issue and attach
-`strata-<model>.log` from this folder.
+**It stopped while downloading or installing.**
+Run `START-HERE.bat` again. It continues where it stopped.
+
+**It says the NVIDIA driver is too old.**
+Update it (NVIDIA App or [nvidia.com/drivers](https://www.nvidia.com/drivers)), restart the PC, and run
+`START-HERE.bat` again.
+
+**It says port 8080 is already in use.**
+Strata is already running. Look for its window.
+
+**It's very slow and the disk light keeps blinking.**
+Your PC is out of free RAM. Close other programs, or pick a smaller size (Q2_0 or IQ2_XS).
+
+**An answer stopped with "the engine stopped unexpectedly".**
+Usually not enough RAM (on Linux the system then stops the engine). Just send your message again: Strata starts the
+engine by itself. If it keeps happening, close other programs or pick a smaller size.
+
+**It says the prompt exceeds the context.**
+The conversation is longer than the context you chose. Start a new chat, or run `START-HERE.bat --setup` and pick more
+context.
+
+**Still stuck?** Look in the [full troubleshooting table](docs/DETAILS.md#troubleshooting), or open an issue and
+attach `strata-<model>.log` from the Strata folder.

 ## How does it work?

@@ -0,0 +1,29 @@
+# 2026-09-27: expert cache on vs off (issue #23)
+
+Question: the engine used to warn at start that "the GPU hit path is NOT CORRECT ... any OUTPUT from it is not". Does
+the expert cache make the output worse?
+
+Method: teacher-forced. `strata --dump-logits` runs a text one token at a time and writes the logits at every
+position, so both runs see the same text and each position's next-token prediction can be compared:
+- cache off: no `--expert-cache`, every routed expert computed by the CPU pool;
+- cache on: `--expert-profile pack/profile-decode-8k.bin --expert-cache auto`, 5,130 of the most-used experts on the GPU.
+
+Q2_0, RTX 5070, the first 512 or 1,024 tokens of three frozen prompts.
+
+| Text | Same top-1 | Median KL | Perplexity off | Perplexity on | On - off (nats per token) |
+|---|---:|---:|---:|---:|---:|
+| Code agent (512) | 97.7% | 3.0e-04 | 2.672 | 2.630 | -0.016 +- 0.019 |
+| Long document (1,024) | 96.2% | 4.5e-03 | 29.844 | 29.843 | -0.000 +- 0.005 |
+| Chat (1,024) | 95.0% | 4.4e-03 | 18.380 | 18.301 | -0.004 +- 0.006 |
+| **All 2,557 tokens** | | | | | **-0.005 +- 0.005** |
+
+Reading:
+- The cache changes which of two nearly equal candidates wins at 2-5% of positions. Most of the flips have a top-2
+  margin under 0.5 logits (`logits-compare.txt`).
+- Neither side is more accurate: the perplexity of the actual text is the same, within 1 standard error.
+- A GPU expert and the CPU compute the same quantized expert with a different floating-point order. Both are an
+  equally good approximation of the model.
+
+The start-up warning dates from round 328, when the GPU hit path had a real fault (divergence from token 0). That fault
+was fixed long ago and is covered by `expert_parity`, `native_expert_parity` and the grouped kernels' tests. Since
+engine 0.1.8 the warning is replaced by one accurate line.
@@ -0,0 +1,36 @@
+# 2026-09-27: the experimental-speed-projection control vector
+
+The package's `Qwen3.8-Flash-Next-experimental-speed-projection.gguf` (47 unit directions of 2,560, one per layer),
+applied as `--control-vector-scaled FILE:1.0 --control-vector-layer-range 4 44 --cvec-mode project --cvec-dir
+per-layer`: after layers 4-44, every hyper-connection stream `h -= (h . v) v`. The package describes it as a
+refusal-direction projection. Q2_0, RTX 5070, fixed experts (`--expert-cache 4000 --pcie-frac 0 --adapt-swaps 0`).
+
+## Kernel (`cvec_parity`)
+
+Project and add against a double-precision reference (max error 5e-7); with the switch off, the kernel's pending
+residual write is bitwise the fused hyper-connection read's own, so a loaded-but-off vector is the stock engine.
+
+## Teacher-forced, stock vs projection (`esp_kl.py`, per-token path)
+
+| Text | Tokens | Same top-1 | Mean KL | p99 KL | Perplexity stock | With the vector |
+|---|---:|---:|---:|---:|---:|---:|
+| Code | 511 | 92.8% | 0.098 | 1.69 | 2.594 | 2.982 |
+| Document | 1,023 | 89.8% | 0.058 | 0.84 | 30.299 | 30.984 |
+| Chat | 1,023 | 89.6% | 0.050 | 0.33 | 18.409 | 18.485 |
+| **All** | 2,557 | | **0.063** (max 4.10) | 1.00 | | |
+
+The package reports a mean KL of 0.186 on its own neutral text with UD-Q2_K_XL in llama.cpp.
+
+Cost on the same tokens: 44.34 -> 44.26, 46.74 -> 46.56, 47.02 -> 46.83 tok/s (0.2-0.4%).
+
+## Serve (`esp_serve.py`)
+
+- Loaded and switched off (`cvec=0`) vs an engine without it: 65 / 65 first tokens after prefixes (verify windows
+  and the batched prompt path) and three 256-token generations **token-identical**.
+- On, off, on again: identical (the switch drops the conversation cache, so nothing computed one way is reused the
+  other way).
+- The prompt paths steer like the per-token path: served-with-vector first tokens agree with the per-token path's
+  teacher-forced argmax with the vector 62 / 65, and with the stock one 57 / 65 (stock served: 64 / 65 and 57 / 65).
+- Decode tok/s of 3 x 256-token generations: stock 66.4, on 76.7 / 81.2. **Not a like-for-like comparison:** the
+  model writes different text with it on (on the code prompt it copied a long stretch of the source, which prompt
+  lookup drafts at 91% acceptance, where stock stopped after 78 tokens). On the same tokens it is 0.2-0.4% slower.
@@ -0,0 +1,85 @@
+"""Teacher-forced logits, stock vs the experimental-speed-projection vector, on the same texts and fixed experts.
+
+    python esp_kl.py run      # the dumps (per-token path)
+    python esp_kl.py compare  # same top-1, mean/median KL(stock || projection), perplexities
+"""
+import json
+import os
+import subprocess
+import sys
+
+import numpy as np
+
+S = os.path.dirname(os.path.abspath(__file__))
+E = r"C:\Users\AI-Server\Desktop\Strata\Public\Engine"
+EXE = E + r"\build-dev\strata.exe"
+VEC = r"C:\Users\AI-Server\Desktop\Strata\experimental-speed-projection\Qwen3.8-Flash-Next-experimental-speed-projection.gguf"
+TEXTS = {"code": S + r"\p512.ids", "doc": S + r"\4k-02-long-doc-1k.ids", "chat": S + r"\4k-04-chat-1k.ids"}
+OUT = S + r"\esp-kl"
+ARMS = {"stock": [],
+        "proj": ["--control-vector-scaled", VEC + ":1.0", "--control-vector-layer-range", "4", "44",
+                 "--cvec-mode", "project", "--cvec-dir", "per-layer"]}
+
+
+def base_args():
+    c = json.load(open(S + r"\cfg-rel.json"))
+    a = list(c["args"])
+    i = a.index("--expert-cache")
+    a[i + 1] = "4000"
+    j = a.index("--prefill")          # the prompt one token at a time: a logits row per position
+    del a[j:j + 2]
+    return a + ["--pcie-frac", "0", "--adapt-swaps", "0"], c
+
+
+def run():
+    os.makedirs(OUT, exist_ok=True)
+    args, c = base_args()
+    env = dict(os.environ)
+    env["PATH"] = os.pathsep.join(c["lib_dirs"]) + os.pathsep + env["PATH"]
+    for name, path in TEXTS.items():
+        for arm, extra in ARMS.items():
+            out = f"{OUT}\\{name}-{arm}.bin"
+            if os.path.exists(out):
+                continue
+            cmd = [EXE, *args, *extra, "--tokens-file", path, "--max-new", "1", "--dump-logits", out]
+            with open(f"{OUT}\\{name}-{arm}.log", "w") as log:
+                r = subprocess.run(cmd, cwd=c["cwd"], stdout=log, stderr=subprocess.STDOUT, env=env)
+            print(name, arm, "exit", r.returncode, flush=True)
+
+
+def load(p):
+    h = np.fromfile(p, dtype=np.int32, count=2)
+    return np.memmap(p, dtype=np.float32, mode="r", offset=8, shape=(int(h[1]), int(h[0])))
+
+
+def lsm(x):
+    x = x.astype(np.float64)
+    m = x.max(axis=-1, keepdims=True)
+    return x - (m + np.log(np.exp(x - m).sum(axis=-1, keepdims=True)))
+
+
+def compare():
+    print(f"{'text':<6}{'tokens':>7}{'same top-1':>11}{'mean KL':>9}{'median KL':>11}{'p99 KL':>8}{'ppl stock':>10}{'ppl proj':>9}")
+    allkl = []
+    for name, path in TEXTS.items():
+        ids = [int(x) for x in open(path).read().replace(",", " ").split()]
+        a, b = load(f"{OUT}\\{name}-stock.bin"), load(f"{OUT}\\{name}-proj.bin")
+        n = min(a.shape[0], b.shape[0], len(ids) - 1)
+        kl, same, la, lb = [], 0, [], []
+        for r in range(0, n, 64):
+            pa, pb = lsm(a[r:min(n, r + 64)]), lsm(b[r:min(n, r + 64)])
+            kl += list((np.exp(pa) * (pa - pb)).sum(axis=1))
+            same += int((pa.argmax(1) == pb.argmax(1)).sum())
+            nxt = np.array(ids[r + 1:min(n, r + 64) + 1])
+            la += list(pa[np.arange(len(nxt)), nxt])
+            lb += list(pb[np.arange(len(nxt)), nxt])
+        kl = np.array(kl)
+        allkl += list(kl)
+        print(f"{name:<6}{n:>7}{100 * same / n:>10.1f}%{kl.mean():>9.3f}{np.median(kl):>11.4f}{np.percentile(kl, 99):>8.2f}"
+              f"{np.exp(-np.mean(la)):>10.3f}{np.exp(-np.mean(lb)):>9.3f}")
+    allkl = np.array(allkl)
+    print(f"all {len(allkl)} tokens: mean KL {allkl.mean():.3f}, max {allkl.max():.2f}, p99 {np.percentile(allkl, 99):.2f}")
+
+
+if __name__ == "__main__":
+    {"run": run, "compare": compare}[sys.argv[1]]()
@@ -0,0 +1,129 @@
+"""Serve-mode checks of the experimental-speed-projection vector (exact mode: fixed experts, no PCIe share).
+
+    python esp_serve.py run ARM      # ARM = stock | vec; writes esp-serve/<arm>.json
+    python esp_serve.py compare
+
+Per arm: the first greedy token after many prefixes of three texts (short ones go through the verify windows, long
+ones through the batched prompt path; the conversation cache reuses the earlier prefix), and three 256-token greedy
+generations.  The vec arm runs everything with cvec=1, then cvec=0, then cvec=1 again.
+"""
+import json
+import os
+import sys
+import threading
+
+S = os.path.dirname(os.path.abspath(__file__))
+E = r"C:\Users\AI-Server\Desktop\Strata\Public\Engine"
+sys.path.insert(0, E)
+from serve.server import StrataEngine  # noqa: E402
+
+EXE = E + r"\build-dev\strata.exe"
+VEC = r"C:\Users\AI-Server\Desktop\Strata\experimental-speed-projection\Qwen3.8-Flash-Next-experimental-speed-projection.gguf"
+TEXTS = {"code": S + r"\p512.ids", "doc": S + r"\4k-02-long-doc-1k.ids", "chat": S + r"\4k-04-chat-1k.ids"}
+OUT = S + r"\esp-serve"
+VEC_ARGS = ["--control-vector-scaled", VEC + ":1.0", "--control-vector-layer-range", "4", "44",
+            "--cvec-mode", "project", "--cvec-dir", "per-layer"]
+
+
+def ids_of(name):
+    return [int(x) for x in open(TEXTS[name]).read().replace(",", " ").split()]
+
+
+def engine(extra):
+    c = json.load(open(S + r"\cfg-rel.json"))
+    a = list(c["args"])
+    a[a.index("--expert-cache") + 1] = "4000"
+    env = dict(os.environ)
+    env["PATH"] = os.pathsep.join(c["lib_dirs"]) + os.pathsep + env["PATH"]
+    return StrataEngine(EXE, a + ["--pcie-frac", "0", "--adapt-swaps", "0"] + extra, cwd=c["cwd"],
+                        log=OUT + r"\engine.log", env=env)
+
+
+def gen(eng, ids, n, sampling):
+    toks = [t for t in eng.generate(ids, n, sampling, threading.Event()) if t is not None]
+    return toks, dict(eng.last)
+
+
+def suite(eng, sampling):
+    out = {"first": {}, "long": {}}
+    for name in TEXTS:
+        ids = ids_of(name)
+        # short prefixes growing by 4 (each continues the live session through the verify windows), then long ones
+        # SHRINKING (no cached prefix, so each is read from 0 by the batched prompt path)
+        lens = list(range(32, 72, 4)) + list(range(min(len(ids) - 1, 700), 95, -48))
+        out["first"][name] = {L: gen(eng, ids[:L], 1, sampling)[0][0] for L in lens}
+    for name in TEXTS:
+        toks, last = gen(eng, ids_of(name)[:300], 256, sampling)
+        out["long"][name] = {"tokens": toks, "last": last}
+    return out
+
+
+def run(arm):
+    os.makedirs(OUT, exist_ok=True)
+    eng = engine(VEC_ARGS if arm == "vec" else [])
+    res = {"info": eng.info}
+    if arm == "stock":
+        res["stock"] = suite(eng, {})
+    else:
+        res["on"] = suite(eng, {"experimental_speed_projection": True})
+        res["off"] = suite(eng, {"experimental_speed_projection": False})
+        res["on2"] = suite(eng, {"experimental_speed_projection": True})
+    eng.close()
+    json.dump(res, open(f"{OUT}\\{arm}.json", "w"))
+    print(arm, "done", eng.info, flush=True)
+
+
+def compare():
+    import numpy as np
+    sys.path.insert(0, S)
+    from esp_kl import load, OUT as KL
+    st = json.load(open(OUT + r"\stock.json"))
+    ve = json.load(open(OUT + r"\vec.json"))
+    print("engine INFO (vec):", ve["info"])
+
+    def eq_first(a, b):
+        tot = same = 0
+        for name in a:
+            for L, t in a[name].items():
+                tot += 1
+                same += int(b[name][L] == t)
+        return same, tot
+
+    def eq_long(a, b):
+        return all(a[n]["tokens"] == b[n]["tokens"] for n in a), \
+            [(n, next((i for i, (x, y) in enumerate(zip(a[n]["tokens"], b[n]["tokens"])) if x != y), None)) for n in a]
+
+    print("1. vector loaded, switched off == no vector")
+    print("   first tokens: %d / %d identical" % eq_first(st["stock"]["first"], ve["off"]["first"]))
+    print("   256-token generations identical:", *eq_long(st["stock"]["long"], ve["off"]["long"]))
+    print("2. repeatable: on, off, on again")
+    print("   first tokens: %d / %d identical" % eq_first(ve["on"]["first"], ve["on2"]["first"]))
+    print("   256-token generations identical:", *eq_long(ve["on"]["long"], ve["on2"]["long"]))
+    print("3. the prompt paths against the per-token path's teacher-forced argmax (row L-1 predicts token L)")
+    for arm, first in (("stock", st["stock"]["first"]), ("on", ve["on"]["first"])):
+        for ref in ("stock", "proj"):
+            agree = tot = 0
+            for name in first:
+                d = load(f"{KL}\\{name}-{ref}.bin")
+                for L, t in first[name].items():
+                    L = int(L)
+                    if L - 1 < d.shape[0]:
+                        tot += 1
+                        agree += int(int(np.argmax(d[L - 1])) == t)
+            print(f"   served {arm:<5} vs per-token {ref:<5}: {agree} / {tot}")
+    print("4. how different the text is: 256-token generations, on vs stock, first difference")
+    print("  ", eq_long(st["stock"]["long"], ve["on"]["long"])[1])
+    print("5. decode speed (exact mode: fixed experts, no PCIe share - not the normal setup)")
+    for label, r in (("stock", st["stock"]), ("vec off", ve["off"]), ("vec on", ve["on"]), ("vec on 2", ve["on2"])):
+        g = sum(v["last"]["generated"] for v in r["long"].values())
+        ms = sum(v["last"]["decode_ms"] for v in r["long"].values())
+        acc = sum(v["last"].get("drafts_accepted", 0) for v in r["long"].values())
+        off = sum(v["last"].get("drafts_offered", 0) for v in r["long"].values())
+        print(f"   {label:<9} {g / ms * 1000:6.1f} tok/s  drafts accepted {acc}/{off}")
+
+
+if __name__ == "__main__":
+    if sys.argv[1] == "run":
+        run(sys.argv[2])
+    else:
+        compare()
@@ -0,0 +1,20 @@
+1. vector loaded, switched off == no vector
+   first tokens: 65 / 65 identical
+   256-token generations identical: True [('code', None), ('doc', None), ('chat', None)]
+2. repeatable: on, off, on again
+   first tokens: 65 / 65 identical
+   256-token generations identical: True [('code', None), ('doc', None), ('chat', None)]
+3. the prompt paths against the per-token path's teacher-forced argmax (row L-1 predicts token L)
+   served stock vs per-token stock: 64 / 65
+   served stock vs per-token proj : 57 / 65
+   served on    vs per-token stock: 57 / 65
+   served on    vs per-token proj : 62 / 65
+4. how different the text is: 256-token generations, on vs stock, first difference
+   [('code', 11), ('doc', 6), ('chat', 3)]
+5. decode speed (exact mode: fixed experts, no PCIe share - not the normal setup)
+   stock       66.4 tok/s  drafts accepted 256/309
+   vec off     65.7 tok/s  drafts accepted 251/318
+   vec on      76.7 tok/s  drafts accepted 366/425
+   vec on 2    81.2 tok/s  drafts accepted 366/423
+
+[exited with code 0]
@@ -0,0 +1,6 @@
+text   tokens same top-1  mean KL  median KL  p99 KL ppl stock ppl proj
+code      511      92.8%    0.098     0.0031    1.69     2.594    2.982
+doc      1023      89.8%    0.058     0.0271    0.84    30.299   30.984
+chat     1023      89.6%    0.050     0.0265    0.33    18.409   18.485
+all 2557 tokens: mean KL 0.063, max 4.10, p99 1.00
+
@@ -0,0 +1,60 @@
+# 2026-09-27: Q4_0 KV with Hadamard rotation (PR #21, `--kv q4_0`)
+
+PR #21 (code-martin) adds a 4-bit K/V format: an orthonormal 256-point Walsh-Hadamard rotation of K, V and Q, then
+ggml `q4_0` blocks (576 B per cell and layer, vs 1,056 B in int8).
+
+## Integration fixes made on merge
+
+- **Q4 + KV streaming (`--kv-resident`).** The PR had no host copy for Q4 and skipped the staging, so with streaming
+  on, the prompt path wrote K/V through the residency map's -1 entries (out-of-bounds writes).
+  - Q4 is now a third format in `kv_stream`: host copy, block residency, prompt-path staging, MTP ring and STATE_HASH.
+  - `kv_stream_parity` checks it bitwise, streamed vs resident, with ring restore.
+- **Duplicate attention.** The per-token decode path ran attention twice, the first time before the selection's
+  blocks were made resident; it now runs once.
+- **Tie-break.** The group maximum's tie-break is now deterministic across lanes: equal |x| with opposite signs
+  could give one block two scales.
+- **Unrelated changes dropped:** duplicate path fixes (already in 0.1.7), personal `.gitignore` entries, and a
+  re-encoded CMakeLists.
+
+`kv_q4_parity` (the PR's test) and all 70 ctest tests pass.
+
+## Quality (teacher-forced, `tools/kv_precision_compare.py`)
+
+Same text, same fixed expert set (Q2_0, 3,000 slots, `--adapt-swaps 0`); only `--kv` differs. fp16 is the reference.
+
+| Text | KV | Same top-1 as fp16 | Mean KL | Perplexity | vs fp16 (nats/token) |
+|---|---|---:|---:|---:|---:|
+| code agent (512) | int8 | 98.4% | 0.014 | 2.649 | -0.011 +- 0.006 |
+| | q4_0 | 95.7% | 0.030 | 2.598 | -0.031 +- 0.014 |
+| document (1,024) | int8 | 95.6% | 0.009 | 30.18 | +0.012 +- 0.005 |
+| | q4_0 | 91.3% | 0.039 | 32.26 | **+0.078 +- 0.011** |
+| chat (1,024) | int8 | 95.5% | 0.009 | 18.24 | -0.003 +- 0.005 |
+| | q4_0 | 90.7% | 0.028 | 18.52 | +0.012 +- 0.008 |
+| document, 8K (every 8th position) | int8 | 92.3% | 0.114 | 7.63 | -0.013 +- 0.020 |
+| | q4_0 | 88.0% | 0.297 | 8.65 | **+0.112 +- 0.037** |
+
+- **int8 is indistinguishable from fp16** (within about 2 standard errors everywhere).
+- **q4_0 costs measurable precision, and more as the context grows:**
+  - document perplexity +8% at 1K and +12% at 8K;
+  - the KL from fp16 is 2.6-4.2x int8's;
+  - code and chat are near-neutral.
+- **Needles with `--kv q4_0 --kv-resident 32768`: 5/5** (1K, 32K, 128K at 10% and 90% depth, 262K;
+  `needles-q4.*`). Retrieval holds.
+
+## Speed and memory (Q2_0, 128K, matrix bench, `--kv-resident 32768`)
+
+| KV | Expert slots | Decode tok/s | Tokens/round | Rounds/s | K/V in RAM |
+|---|---:|---:|---:|---:|---:|
+| int8 (`kv-stream/matrix-stream`) | 4,044 | 62.4 | 2.52 | 24.8 | 1.52 GiB |
+| q4_0 (`matrix-q4`) | 4,190 | 67.4 | 2.62 | 25.7 (+3.7%) | 0.83 GiB |
+
+Without streaming (contexts up to 64K, or too little RAM for it) q4_0 halves the whole KV cache in VRAM instead.
+
+## Decision
+
+`--kv q4_0` is a user option:
+- `START-HERE.bat --kv q4_0`, or the question setup asks above 8K context;
+- or `--kv q4_0` in a `strata-*.json`.
+
+int8 stays the default: every published quality number was measured with it, and q4_0's precision cost is real,
+though it passes the needles.
@@ -0,0 +1,47 @@
+# Faster prompt reading (engine 0.1.13)
+
+RTX 5070 12 GB, Ryzen 5 7600, 64 GB DDR5. 32K-token prompt (`bench/prompts/long/32k-01-code-agent.ids`), one-shot
+engine, `--expert-cache auto`, greedy. Numbers are prompt tokens/s.
+
+## What changed, step by step
+
+| Step | What | Result |
+|---|---|---|
+| Buffers | The attention half and the MoE half of a layer share one scratch region (~260 KB less per prompt token) | bit-identical; chunk 4096 borrows 1.78 GiB instead of 2.84 |
+| PLE rows | The next chunk's PLE rows are read from the model file on a thread while the GPU works | bit-identical; 762 -> 790 at chunk 4096 |
+| Chunk size | `--prefill auto`: the largest of 8192 / 6144 / 4096 / ... whose buffers fit in the cache slots it may borrow; a request borrows only what its own prompt needs | Q2_0: 4096 791, 6144 878, 8192 973 |
+| Host copies | Experts the arena could not pin are copied to pinned buffers by helper threads instead of the launching thread | bit-identical; IQ3_S at 4096: 551 -> 652 |
+| PLE block | Computed for the whole chunk (two GEMMs) instead of token by token | Q2_0 981 -> 1053, IQ3_S 831 -> 940 |
+| Experts | llama.cpp's MMQ kernels (weights stay quantized, int8 tensor cores) instead of dequantize-to-FP16 + cuBLAS | Q2_0 1052 -> 1130, IQ3_S (8192) 932 -> 1014 |
+| Streaming | At chunks of 2048+ every non-resident expert streams in a fixed order through a ring, so the next layer's arrive while the current layer's attention runs | bit-identical; Q2_0 1130 -> 1290, IQ3_S -> 1208 |
+
+Tried and dropped: cuBLAS grouped GEMMs for the experts (faster in isolation, slower inside the engine: 770 vs 790).
+
+## Before and after
+
+| | engine 0.1.12 (`--prefill 2048`) | engine 0.1.13 (`--prefill auto`) |
+|---|---:|---:|
+| Q2_0, 32K prompt | 572 | 1,290 |
+| IQ3_S, 32K prompt | 383 | 1,208 |
+| Server, Q2_0, 128K context, 999-token prompt | 353 | 438 |
+| Server, 6,927-token prompt | 529 | 1,077 |
+| Server, 28,584-token prompt | 584 | 1,249 |
+
+Output speed is unchanged (the decode path is the same). Short prompts (a few hundred tokens) go through the verify
+windows as before.
+
+## Quality
+
+- "Bit-identical" steps were checked with the GDN state hash after the 32K prompt (all 36 recurrent layers).
+- The steps that change rounding (the batched PLE block, MMQ's 8-bit activations) were compared teacher-forced: the
+  32K prompt is read by the prompt path up to its last 256 tokens, those go through the token path with their logits
+  dumped. At 32K every rounding change moves the predictions about this much - even a different chunk size of the
+  unchanged engine - so that is the yardstick:
+
+| Against the 8192-chunk FP16 path | Same top-1 | Mean KL | Perplexity (reference 9.21) |
+|---|---:|---:|---:|
+| Chunk 6144, same code (the yardstick) | 89.8% | 0.33 | 9.18 |
+| MMQ experts | 85.9% | 0.38 | 8.63 |
+
+- Needles (the answer hidden at a given depth): 5 of 5 at 1K, 32K, 128K (two depths) and 262K, Q2_0, KV streaming.
+- A rerun of the same configuration is bit-identical.
@@ -0,0 +1,26 @@
+{
+ "mode": "project",
+ "dir": "per-layer",
+ "scale": 1.0,
+ "layer_range": [
+  4,
+  44
+ ],
+ "refusal_harmful_thinkoff": 2.0,
+ "refusal_harmful_thinkon": 2.0,
+ "refusal_harmless_thinkoff": 4.0,
+ "refusal_harmless_thinkon": 6.0,
+ "broken": 0,
+ "kl_mean": 0.18598,
+ "kl_max": 18.26549,
+ "kl_p99": 3.143571,
+ "decode_tps": 51.19,
+ "vector_file": "Qwen3.8-Flash-Next-experimental-speed-projection.gguf",
+ "base_model_gguf": "unsloth/Qwen3.8-Flash-Next-GGUF UD-Q2_K_XL",
+ "base_model_sha256_shard1": "a4f3b21e77353999829f2f767e9ac21ce9c71d29a74f2cc9eda48c9bf23c8b86",
+ "worktree_commit": "37e026e3b",
+ "criterion_status": "PASS",
+ "dirty_direction": false,
+ "note": "projection ablation h -= s*(h.v)*v, v unit-normalized per layer. dir=per-layer",
+ "branch": "cvec-hooks (github.com/Cudila/llama.cpp)"
+}
@@ -0,0 +1,21 @@
+# experimental-speed-projection (EXPERIMENTAL, off by default)
+
+`Qwen3.8-Flash-Next-experimental-speed-projection.gguf` is a 480 KB control vector for Qwen3.8-Flash-Next: one unit
+direction of 2,560 values per layer (`direction.1` .. `direction.47`, GGUF `controlvector` format). The JSON beside it
+is the configuration its authors selected and their scores.
+
+**What it does.** Turned on, Strata removes each direction from the residual stream after layers 4-44
+(`h -= (h . v) v` on every hyper-connection stream), the same operation as llama.cpp's `--cvec-mode project` in the
+package's patches. The package's own documentation describes the vector as a **refusal-direction projection**: the
+model declines far fewer requests (it reports 1 of 50 vs 50 of 50 on its test set), and removing refusals removes a
+safety behaviour - you are responsible for what the model writes with it on. It also shifts the model's predictions
+slightly on ordinary text (measured here: `bench/results/2026-09-27-esp/`). It is not an optimization in the engine:
+on the same text it costs 0.2-0.4% per token; a chat's speed with it on depends on the text the model writes.
+
+**Turning it on.** `START-HERE.bat --setup` asks (default: off), or pass `--experimental-speed-projection on`. With it
+loaded, the web app's Sampling drawer and the API field `"experimental_speed_projection": false` switch it off per
+request. See `docs/DETAILS.md`, "Experimental speed projection".
+
+**Origin and license.** A local package of the vector (the published original uses filenames containing "refusal";
+the bytes are the same), made from Qwen3.8-Flash-Next activations, so the Qwen Community License 1.0 of the model
+applies to it. Made for the original Qwen3.8-Flash-Next (not Swift 1.5).
@@ -42,8 +42,25 @@ attention reads in VRAM (`--kv-resident 32768`), so more experts fit on the GPU.
 costs ~13.7 KB of RAM per context token (1.7 GB at 128K). Existing installs: run `START-HERE.bat --setup` once to turn
 it on.

+**4-bit KV cache (engine 0.1.8, optional):** `START-HERE.bat --setup` asks above 8K context (or pass `--kv q4_0`). It
+halves the KV cache's memory with a Hadamard rotation before 4-bit rounding (PR #21), about 4% faster at 128K, but it
+is measurably less precise on long documents (perplexity +8-12%; needle tests still pass). 8-bit stays the default.
+Details: [`bench/results/2026-09-27-kv-q4`](../bench/results/2026-09-27-kv-q4/README.md).
+
 Time to first token is prompt length / prompt speed: about 7 s at 4K, 55 s at 32K, 4 minutes at 128K and 9 minutes at
-262K. The raw numbers: [`bench/results/`](../bench/results/). The [paper](paper/Strata-Paper.pdf) explains every number.
+262K (engine 0.1.12; long prompts are about twice as fast since 0.1.13, below).
+
+**Faster prompts (engine 0.1.13):** the prompt is read in chunks of up to 8,192 tokens instead of 2,048 (`--prefill
+auto`: the largest chunk whose buffers fit in the expert-cache slots it borrows, and a request borrows only what its
+prompt needs); the experts are multiplied by llama.cpp's quantized MMQ kernels instead of being expanded to FP16
+first; the next layer's experts stream over PCIe while the current layer's attention runs; the PLE block runs for the
+whole chunk at once; unpinned experts are copied by helper threads. Measured on the RTX 5070 12 GB, 64 GB RAM,
+32K-token prompt: Q2_0 572 -> 1,290 tokens/s, IQ3_S 383 -> 1,208. Through the server (Q2_0, 128K context): 999 tokens
+353 -> 438 tokens/s, 6,927 tokens 529 -> 1,077, 28,584 tokens 584 -> 1,249. Output speed is unchanged. Needles 5/5
+(1K-262K). Details and the quality check:
+[`bench/results/2026-09-28-prefill-speed`](../bench/results/2026-09-28-prefill-speed/README.md). Existing installs
+switch to `--prefill auto` the next time START-HERE / setup.sh starts them. The raw numbers:
+[`bench/results/`](../bench/results/). The [paper](paper/Strata-Paper.pdf) explains every number.

 ## Other GPUs (estimated)

@@ -60,7 +77,9 @@ part by how many more experts the card's VRAM holds. Treat as **±20%**. Numbers
 |  | IQ3_XXS | ~260 / ~106 | ~374 / ~103 | ~396 / ~89 | ~390 / ~85 | ~378 / ~71 | - |

 More VRAM matters more than a faster GPU: every extra GB holds ~700 more experts, and every expert on the GPU is one the
-CPU does not have to compute. A 3090's 24 GB takes most of the CPU work away.
+CPU does not have to compute. A 3090's 24 GB takes most of the CPU work away. (Since 0.1.14 the expert profile ranks
+all 24,576 experts; before, the cache stopped at 8,000, about 10-14 GB. `tools/make_profile.py` builds a profile from
+your own prompts: run the engine once with `--dump-routing trace.bin`, see the tool's help.)

 ## Which model?

@@ -125,8 +144,16 @@ Windows, `build-essential` + CUDA on Ubuntu) and compiles the engine for your GP
 4. **Images?** yes / no (see [Images](#images-vision)).

 Then it downloads and prepares everything (the model is 66-76 GB, so the first start takes a while; an interrupted
-download continues where it stopped) and **starts the model**: your browser opens `http://127.0.0.1:8080`, a small
-page that shows it is running and lets you chat. The API is at `http://127.0.0.1:8080/v1` for your apps.
+download continues where it stopped) and **starts the model**: your browser opens `http://127.0.0.1:8080`, the Strata
+app. It has three tabs:
+- **Chat:** streaming answers, the model's thinking (folded away once it answers), code with a copy button, pictures when
+  images are on, and sampling and thinking-level settings. Chats stay in your browser.
+- **Monitor:** what the model is doing (reading the prompt, with progress, or writing, at how many tokens/s); GPU load,
+  VRAM, temperature, power and PCIe traffic; CPU, RAM and disk; the context in use; the last requests.
+- **About:** the model and engine settings, and the addresses to connect other apps.
+
+`http://127.0.0.1:8080/?q=your question` opens it with a new chat already asking. The API is at
+`http://127.0.0.1:8080/v1` for your apps.

 **Every time after that**, `START-HERE.bat` just starts the model (30-90 s to load 34-43 GB into RAM). Nothing is
 downloaded again. Closing the window stops the model.
@@ -159,6 +186,15 @@ for the build tools), and the same start: `http://127.0.0.1:8080`. Later runs of
 start the model directly. Options as on Windows (`./setup.sh --setup`, `--model Q2_0 --yes`, `--gguf-dir /data/Q2_0`).
 Terminal chat: `.venv/bin/python chat.py`.

+- **Updating:** `git pull`, then `./setup.sh`: it compiles the engine again when its source changed (a minute or
+  two for the changed files). If that compile fails, it says so and starts the engine you had.
+- **Other distributions** (Arch, Fedora, ...): install the C++ compiler and the CUDA Toolkit 13 with your package
+  manager first (Arch: `sudo pacman -S base-devel cuda`); setup finds `nvcc` on PATH, in `/usr/local/cuda*` and in
+  `/opt/cuda*`, and does the rest.
+- **WSL** works (Ubuntu 24.04 tested), with one limit: the NVIDIA driver pins only about 1 GB of RAM there, so KV
+  streaming (`--kv-resident`) is off and the KV cache stays in VRAM, and the experts are copied to the GPU from
+  unpinned RAM (slower prompts than native Linux).
+
 ---

 ## Using it
@@ -171,6 +207,7 @@ The server listens on `http://127.0.0.1:8080` (change with `--port` in setup, or
 | Anthropic Messages (stream and non-stream, tools) | `POST /v1/messages` |
 | Model list / health | `GET /v1/models`, `GET /health` |
 | What the model is doing right now | `GET /status` |
+| Everything the Monitor tab shows (engine, live state, last requests, hardware) | `GET /metrics` |

 ```bash
 curl http://127.0.0.1:8080/v1/chat/completions -H "Content-Type: application/json" -d '{
@@ -202,9 +239,18 @@ print(r.choices[0].message.content)
  15 s, and `GET /status` says what it is doing (`reading the prompt`, `answering`, tokens so far). Closing the
  connection or pressing stop in your app really stops the model, so the next request starts at once.
 - **Chat apps.** Any app with an "OpenAI-compatible" provider works: base URL `http://127.0.0.1:8080/v1`, any API key.
- **Context.** Chosen in setup (8K-262K). Requests longer than that are refused, never silently cut.
- **From other devices / the internet.** The server listens on your PC only (`127.0.0.1`). To reach it from elsewhere,
-  put a tunnel in front of it, for example [cloudflared](https://developers.cloudflare.com/cloudflare-one/connections/connect-networks/do-more-with-tunnels/trycloudflare/):
+- **Context.** Chosen in setup (8K-262K). Requests longer than that are refused, never silently cut. A request whose
+  `max_tokens` would run past the context is refused too (400); agents that always ask for their full output cap
+  can instead get it shortened to the room left: add `"fit_max_tokens": true` to `strata-<model>.json` (or pass
+  `--fit-max-tokens` to `serve/server.py`). A prompt that leaves no room at all is still refused.
+- **From other devices on your network.** The server listens on your PC only (`127.0.0.1`) unless you say otherwise:
+  run setup with `START-HERE.bat --setup --host 0.0.0.0 --api-key some-long-secret` (or add `"host": "0.0.0.0"` and
+  `"api_key": "..."` to `strata-<model>.json`). The server window then prints this PC's addresses
+  (`from other devices: http://192.168.x.x:8080/`); open that on the other device, or use `.../v1` as an API base URL.
+  On Windows the firewall blocks it until you allow it: accept its prompt for Python (private networks), or run
+  `New-NetFirewallRule -DisplayName "Strata 8080" -Direction Inbound -Protocol TCP -LocalPort 8080 -Action Allow -Profile Private`
+  in an admin PowerShell, and make sure the network is set to Private.
+- **From the internet.** Put a tunnel in front of it, for example [cloudflared](https://developers.cloudflare.com/cloudflare-one/connections/connect-networks/do-more-with-tunnels/trycloudflare/):
  `cloudflared tunnel --url http://127.0.0.1:8080`. **Set a key first**, or anyone with the link can use your PC:
  add `"api_key": "some-long-secret"` to `strata-<model>.json` (or set the `STRATA_API_KEY` environment variable);
  clients then send it as their API key.
@@ -215,8 +261,15 @@ assistant turn and every 16K prompt tokens). A checkpoint is used only when the
 and pictures. Engine options: `--prompt-cache N` (0 = off), `--prompt-cache-every N`, `--turn-token ID`.

 **Current limits (v1):** one request at a time, and one conversation cached at a time (switching between two chats
-re-reads the other one); greedy decoding (temperature is ignored); images only when set up with them (below); no
-video.
+re-reads the other one); images only when set up with them (below); no video. **Temperature / top_p / top_k / min_p /
+seed** are honored per request (OpenAI and Anthropic fields); with the default adaptive expert tier a sampled result
+is not reproducible run to run - for seed-reproducible output add `--adapt-every 100000` (static residency) to the
+engine arguments. The run config's optional `sampling` block sets the defaults for requests that leave the fields out
+(`"sampling": {"temperature": 1.0, "top_p": 0.95, "top_k": 20}`); a request's own fields always win, and with no
+block at all a request without sampling keys decodes greedy. The penalties (`presence_penalty`, `frequency_penalty`,
+`repetition_penalty`, with `penalty_last_n` capping how many recent tokens they count over, default 64 when any
+penalty is set) ride the same path; they count the tokens the request has consumed, so a repetition penalty
+suppresses what the model itself just said, not the prompt alone.

 ---

@@ -294,6 +347,45 @@ test images.

 ---

+## Experimental speed projection (EXPERIMENTAL, off by default)
+
+**This is an experiment, not a finished feature.** It ships with Strata but stays off unless you turn it on.
+
+A 480 KB control vector for Qwen3.8-Flash-Next (`data/experimental-speed-projection/`, see its README). After each
+of layers 4-44 the engine removes one direction from every hyper-connection stream of the residual: `h -= (h . v) v`,
+one unit vector `v` per layer, exactly as llama.cpp does with the package's `--cvec-mode project` patches.
+
+**What it changes.** The vector's own package describes it as a **refusal-direction projection**: with it the model
+declines far fewer requests (it reports 1 of 50 vs 50 of 50 on its test set), and removing refusals removes a safety
+behaviour - you are responsible for what the model writes with it on. It also shifts ordinary answers a little
+(measured below). It is not an optimization in the engine: on the same text it costs 0.2-0.4% per token. What a
+chat's tokens/s does with it on depends on the text the model writes (length, repetition, how well the drafts land),
+so measure it on your own prompts; the Monitor marks every request ESP or stock.
+
+**Turning it on (at setup).** `START-HERE.bat --setup` asks "Turn on the experimental speed projection?" (default:
+no), or pass `--experimental-speed-projection on` (`off`, or a path to another vector GGUF). Only for the original
+Qwen3.8-Flash-Next, not Swift 1.5. It writes these engine flags (llama.cpp's) into `strata-<model>.json`:
+
+```
+--control-vector-scaled <Strata>\data\experimental-speed-projection\Qwen3.8-Flash-Next-experimental-speed-projection.gguf:1.0
+--control-vector-layer-range 4 44 --cvec-mode project --cvec-dir per-layer
+```
+
+The engine log then says `control vector mode = project, dir = per-layer, layers 4..44 (41 steered)`, and the web
+app's About tab lists it. (`--cvec-mode add` is llama.cpp's stock additive mode, for additive vectors.)
+
+**Per request.** A loaded vector is on for every request unless it says otherwise: the web app's Sampling drawer has
+a switch, and the API takes `"experimental_speed_projection": false` in the request body (OpenAI and Anthropic; a
+config default goes in `"sampling": {"experimental_speed_projection": false}`). Switching drops the conversation
+cache once, since the model state was computed the other way. Switched off, the output is token-for-token the stock
+model's.
+
+**Measured here** (Q2_0, fixed experts, 2,557 teacher-forced tokens of code, a document and a chat): the top-1 token
+changes at 10% of positions, mean KL from the stock model 0.063 nats (max 4.1), perplexity +15% on code, +2.3% on
+the document, +0.4% on the chat. Details: `bench/results/2026-09-27-esp/`.
+
+---
+
 ## Troubleshooting

 | Symptom | What to do |
@@ -302,14 +394,17 @@ test images.
 | Python or the build tools could not be installed | Install what it names (links are printed), then run it again. Everything already done is kept. |
 | `port 8080 is already in use` | Strata is already running (look for its window), or another program uses the port: `START-HERE.bat --port 8081`. |
 | `cudaHostRegister ... out of memory` in the log | Normal on Windows: the engine pins the experts in per-layer slices instead. Only a problem if the load then fails. |
-| The first start takes minutes | It is reading 34-43 GB into RAM; the second start is faster while the files are in the OS cache. |
+| The first start takes minutes | It is reading 34-55 GB into RAM; the second start is faster while the files are in the OS cache. |
+| The PC freezes for a few minutes at the first start | Normal the first time: the engine loads the experts into RAM, pins part of it for the GPU and sizes the expert cache. Wait; don't close the window. Still frozen after 10 minutes: restart the PC, close other programs, try again, or pick a smaller size. |
+| `the engine stopped unexpectedly (exit code ...)` | The engine process ended mid-answer - usually out of RAM (Linux ends the biggest program: `sudo dmesg \| grep -i -E 'killed process\|out of memory'`). The next request starts it again by itself. If it repeats: close other programs or pick a smaller size. The server also warns at start when the model's experts leave less than ~6 GB of RAM for everything else. |
 | Slow output, disk light busy | Not enough free RAM: close other programs, or choose Q2_0 / IQ2_XS. |
 | `prompt ... exceeds the context` | The request is longer than the context you chose: run setup again with a bigger `--context`. |
 | Slower than the tables | The monitor plugged into the GPU and other GPU programs take VRAM from the expert cache; RAM running below its rated speed (enable EXPO/XMP in the BIOS) slows the CPU half. |
 | `this server was started without the vision encoder` | The model was set up for text only: run setup again with `--vision gpu`. |
 | A picture is refused or `cannot read the image` | The file is not a picture Pillow can open (JPEG, PNG, WebP, GIF, BMP, TIFF, AVIF work). |
 | Pictures are slow (10-30 s) | The encoder runs on the CPU: run setup again with `--vision gpu` (needs ~1.4 GB of VRAM). |
-| A request never finishes (older engines, mostly with pictures) | Run `START-HERE.bat` once to get engine 0.1.2 or newer. It keeps a real margin of VRAM free: the log says `... MiB of VRAM free with everything loaded` and warns when it is close to 0. |
+| A request never finishes: "reading the prompt", GPU "100%" at low power | The GPU ran out of VRAM (engines before 0.1.9 could end with ~30 MiB free at large contexts). Run `START-HERE.bat` once to get engine 0.1.9 or newer; the log then says `... MiB of VRAM free with everything loaded` (a few hundred) and names the `--vram-reserve-mib` to add if it is low. |
+| Generation stops mid-answer, GPU "100%", one CPU core busy | Fixed in engine 0.1.12 (issue #29, a race in the CPU expert pool on big-VRAM cards). Since then a request that stops moving ends with an error instead of hanging (after 2 minutes; 1 minute from 0.1.13): the log says `no progress for ... s ... (issue #29)` with where it stopped, and the next request starts the engine again. If you see that line, please open an issue with it. Engine 0.1.13 adds a stall report under it (what every expert-pool thread and the GPU handshake were doing, memory and page faults) and, on Windows, a `strata-stall-<pid>.dmp` file with every thread's stack: attach both. (`STRATA_WATCHDOG_S` sets the time in seconds; 0 turns it off.) Engine 0.1.14 fixes the stall those reports found (issue #31: with the IQ packs the host could wait forever inside the NVIDIA driver while copying experts in a verify window; the experts are now copied by a GPU kernel, `--pcie-mode dma` restores the old way). |
 | Anything else | The engine log is `strata-<model>.log` in this folder. |

 ---
@@ -325,7 +420,9 @@ test images.
  as the GPU works on the cached ones (AVX-512 / AVX2 kernels, ggml's for the i-quants).
 - **SSD:** the 28.8 GB n-gram table, read a few rows per token through the OS cache.
 - **Speculation:** the model's own MTP layer drafts up to 3 tokens; one pass over all 48 layers checks them. 2.4-3.2
-  tokens per pass on average.
+  tokens per pass on average. When the reply repeats the context (code edits, quoted text), **prompt lookup** (engine
+  0.1.7) drafts up to 5 tokens from the earlier copy, but only where its measured acceptance and cost say it pays:
+  code edits 6-11% faster, other text unchanged. The drafts are checked like the MTP's, so the output is the same.
 - **Prompts** are processed in 2,048-token chunks with the experts streamed to the GPU over PCIe.

 The full story, with measurements, bottlenecks and what comes next: **[docs/paper/Strata-Paper.pdf](paper/Strata-Paper.pdf)**.
@@ -344,3 +441,5 @@ The full story, with measurements, bottlenecks and what comes next: **[docs/pape
  `third_party/ggml/LICENSE`.
 - Ideas from [Splash](https://github.com/incoai/splash), [ninfer](https://github.com/Neroued/ninfer) and
  [HyperQwen](https://github.com/syv-ai/HyperQwen); references in the paper.
+- The web app's font: [Outfit](https://github.com/Outfitio/Outfit-Fonts) (SIL Open Font License 1.1, see
+  `serve/web/fonts/OFL.txt`). Its Monitor tab started from @code-martin's dashboard idea (PR #22).
@@ -207,6 +207,10 @@ struct QsaState {
    int8_t* v_q = nullptr;
    uint16_t* k_scale = nullptr;
    uint16_t* v_scale = nullptr;
+    /// PR #21: Q4_0 KV with Walsh-Hadamard rotation (qsa_set_kv_q4, kv_q4.hpp): 144 B per cell and head
+    bool kv_q4 = false;
+    uint8_t* k_q4 = nullptr;
+    uint8_t* v_q4 = nullptr;
    int32_t* page_table = nullptr;   ///< (n_pages,) logical page -> physical page (-1: not resident, streamed)
    int64_t n_pages = 0;
    int64_t max_cells = 0;
@@ -269,6 +273,13 @@ uint64_t qsa_kv_host_bytes();
 /// initializing the session; default off until gate G-C accepts it.
 void qsa_set_kv_int8(bool enabled);
 bool qsa_kv_int8();
+/// PR #21: store K/V as Q4_0 after a Hadamard rotation (`--kv q4_0`): 576 B per cell, vs 1,056 in INT8.
+void qsa_set_kv_q4(bool enabled);
+bool qsa_kv_q4();
+/// The state's KV format for the block-moving functions of kv_stream.hpp (kKvF16 / kKvInt8 / kKvQ4).
+inline int qsa_kv_format(const QsaState& st) {
+    return st.kv_q4 ? strata::kernels::kKvQ4 : st.kv_int8 ? strata::kernels::kKvInt8 : strata::kernels::kKvF16;
+}
 uint64_t qsa_state_init(const ModelGeometry& g, int64_t max_cells, void* base, QsaState& st,
                        const QsaState* share_rope = nullptr, int64_t ring_cells = 0);
 /// KV streaming: the pools a reader sees (the VRAM slots) and, when streamed, make the selection's blocks resident.
@@ -0,0 +1,51 @@
+// include/strata/core/progress.hpp - issue #29: whether a request is still moving, and where it is.
+//
+// The engine's host loop and the GPU wait on each other through flags; a protocol bug there does not crash, it
+// spins forever (the GPU "100%", one CPU core busy, no tokens).  `--serve` runs a watchdog thread over this: a
+// request whose heartbeat stops for `STRATA_WATCHDOG_S` seconds (default 60; 0 = off) ends the engine with the
+// stage it was stuck in, and the server starts it again instead of hanging.  Tokens, prompt chunks and verify
+// windows beat; the stage is two relaxed stores per layer, which the token path does not notice.
+#pragma once
+
+#include <atomic>
+#include <chrono>
+#include <cstdint>
+#include <cstdio>
+
+namespace strata::core {
+
+struct Progress {
+    std::atomic<uint64_t> beats{0};
+    std::atomic<bool> busy{false};
+    std::atomic<const char*> where{"idle"};
+    std::atomic<int64_t> detail{-1};
+    std::atomic<int64_t> since_ms{0};    ///< when `where` was set (steady clock): how long a stage has lasted
+    std::atomic<uint64_t> ticks{0};      ///< layers served: a window that still moves, slowly, against one that stopped
+};
+
+inline int64_t progress_now_ms() {
+    return std::chrono::duration_cast<std::chrono::milliseconds>(std::chrono::steady_clock::now().time_since_epoch())
+        .count();
+}
+
+/// What the watchdog prints about a part of the engine when it fires (the expert pool, the verify window).
+using DiagFn = void (*)(std::FILE*);
+inline std::atomic<DiagFn>& diag_pool_fn() { static std::atomic<DiagFn> f{nullptr}; return f; }
+inline std::atomic<DiagFn>& diag_verify_fn() { static std::atomic<DiagFn> f{nullptr}; return f; }
+
+inline Progress& progress() {
+    static Progress p;
+    return p;
+}
+
+inline void progress_at(const char* where, int64_t detail = -1) {
+    Progress& p = progress();
+    p.where.store(where, std::memory_order_relaxed);
+    p.detail.store(detail, std::memory_order_relaxed);
+    p.since_ms.store(progress_now_ms(), std::memory_order_relaxed);
+}
+
+inline void progress_beat() { progress().beats.fetch_add(1, std::memory_order_relaxed); }
+inline void progress_tick() { progress().ticks.fetch_add(1, std::memory_order_relaxed); }
+
+}  // namespace strata::core
@@ -21,9 +21,12 @@
 // selection, native indexer) and a profile-filled VRAM expert tier with its residency table on the device.
 #pragma once

+#include <cstdio>
+
 #include "strata/core/expert_source.hpp"
 #include "strata/core/layer.hpp"
 #include "strata/core/session.hpp"
+#include "strata/kernels/sampler.hpp"

 #include <cuda_runtime.h>

@@ -51,6 +54,9 @@ public:
    Verifier(const Verifier&) = delete;
    Verifier& operator=(const Verifier&) = delete;

+    /// The watchdog's view of the window in flight (issue #31): the layer, the GPU's sequence, the flags.
+    void diag(std::FILE* f) const;
+
    /// `max_t` <= kVerifyMaxT.  `head` may be null (the canonical head is then run per token).
    bool init(const WeightTable& wt, const ModelGeometry& g, SessionState& ss, const VerifyHits& hits,
              const NativeHead* head, int max_t, std::string& err);
@@ -58,6 +64,21 @@ public:
    /// One window: `tokens[0..T)` at positions pos0.., the pool served per layer; `out[t]` = argmax after token t.
    /// The PLE rows are gathered here from `ss.ple_prev` and the tokens.  Captures the T-token graph on first use.
    bool run(int T, const int32_t* tokens, int64_t pos0, PoolMultiFn pool, void* user, int32_t* out, std::string& err);
+    /// The sampling the verify window's head applies (temperature / top_p / top_k / seed).  Set per
+    /// request; greedy by default.  The sampling itself runs OUTSIDE the captured graph - its
+    /// parameters would otherwise be baked forever - so this can change between requests freely.
+    void set_sampling(const strata::kernels::SamplerParams& sp) {
+        sampling_ = sp;   // row t of a window at pos0 draws Philox(seed, pos0 + t): see run()
+    }
+
+    /// The penalty-history row for `sampling_.penalty_last_n`: ONE row of `history_len` int32 slots, most
+    /// recent token LAST, unused front slots -1 (the kernel reads only the tail window).  Null disables the
+    /// penalties entirely - the neutral run's sampling call is byte-for-byte what it was.  The engine
+    /// re-uploads the request's tail before every window; the pointer must stay alive across the request.
+    void set_history(const int32_t* history, int history_len) {
+        hist_d_ = history;
+        hist_len_ = history_len;
+    }

    /// Keep the first `n_keep` (1..T) tokens of the last window; advances `ss.ple_prev` by them.
    bool commit(int n_keep, std::string& err);
@@ -83,6 +104,14 @@ public:

 private:
    bool capture(int T, std::string& err);
+    strata::kernels::SamplerParams sampling_ = [] {
+        strata::kernels::SamplerParams s;
+        s.greedy = true;
+        s.temperature = 0.0f;
+        return s;
+    }();   ///< greedy by default; per-request via set_sampling
+    const int32_t* hist_d_ = nullptr;   ///< penalty-history row (set_history); null = no penalties apply
+    int hist_len_ = 0;
    bool capture_commit(std::string& err);
    bool record_window(int T, cudaStream_t cs, std::string& err);

@@ -0,0 +1,26 @@
+// include/strata/kernels/cpu/iq_avx2.hpp - AVX-2 multi-token dot products for the i-quant expert
+// formats (IQ2_XXS, IQ2_XS, IQ2_S, IQ3_XXS, IQ3_S) against Q8_K activations (ggml's block_q8_K).
+//
+// The AVX-512 kernel's scheme on the CPUs without it (AMD Zen 2/3, Intel Core 12th-14th gen): the weights of a
+// 32-value chunk are decoded once per verify window and every token applies them with five instructions.
+#pragma once
+
+#include <cstddef>
+#include <cstdint>
+
+namespace strata::kernels::cpu {
+
+bool iq256_supported(int ggml_type) noexcept;
+/// ff[t][r] = silu(gate_r . a[t]) * (up_r . a[t]), rows [r0, r1); gate rows at blob, up rows at blob + up_off.
+void iq256_gu_rows(int ggml_type, const uint8_t* blob, size_t gu_row, size_t up_off, int n, const void* const* act,
+                   int nt, float* const* ff, int r0, int r1);
+/// out[t][r] = w_r . a[t], rows [r0, r1).
+void iq256_rows(int ggml_type, const uint8_t* w, size_t row_bytes, int n, const void* const* act, int nt,
+                float* const* out, int r0, int r1);
+
+/// out[t][r] = w_r . h[t] for IQ4_NL (type 20) rows against Q8_0 activations (ggml's block_q8_0).
+/// IQ4_NL is a 32-value-block format, so this does not go through iq256_rows (QK_K blocks, Q8_K acts).
+void iq4nl256_down_rows(const uint8_t* w, size_t row_bytes, int n, const void* const* hq, int nt,
+                        float* const* out, int r0, int r1);
+
+}  // namespace strata::kernels::cpu
@@ -14,15 +14,25 @@
 // THE COMPLETION PROTOCOL, because this is where a pool usually goes wrong.  `run()` waits for `done == n`
 // AND for every worker to PARK.  Waiting only for `done` is not enough: a worker can still be inside the
 // drain loop after its last `done` increment, and the host resetting `head` underneath it would let that
-// worker claim a job from the NEXT batch before the next batch has been published.  The
-// `done`-then-`parked` pair makes the handover unambiguous, and the second wait costs a few hundred cycles
-// against a layer that takes milliseconds.
+// worker claim a job from the NEXT batch before the next batch has been published.
+//
+// **AND `parked` IS NOT ENOUGH EITHER (issue #29).**  A worker that went to sleep (after `kSpinBeforeSleep`) is
+// still counted as parked when it wakes, so for a moment after it has seen a new epoch the host believes it is
+// idle.  On a card with most experts in VRAM the workers sleep in the middle of a request, tiny batches finish
+// before a sleeper is awake, and that moment comes round constantly: a late worker could claim from the NEXT
+// batch while the host was still writing it, run a job twice, or add to `done` after the host had reset it -
+// and `done != n` then never ended, with the GPU waiting on the pool forever.  So every claim carries its
+// batch: `head` is one 64-bit word `epoch | njobs | index`, a claim is a CAS that only succeeds for the epoch
+// the worker woke for, and a late worker's claim simply fails.  A claim that succeeds belongs to the current
+// batch, whose description the host cannot change until that job's `done` has landed.
 #pragma once

 #include "strata/kernels/cpu/expert.hpp"
 #include "strata/kernels/cpu/native_expert.hpp"

 #include <atomic>
+#include <cstdio>
+#include <memory>
 #include <chrono>
 #include <condition_variable>
 #include <cstdint>
@@ -92,6 +102,8 @@ public:
    /// threads on six cores. `false` is the A/B arm and exists so the change is measurable rather than
    /// asserted - the counter it moves is `pool phases ... drain`, which is host-side and needs no profiler.
    explicit ExpertPool(int n_workers = 0, bool pin = true, bool host_works = true);
+    /// The watchdog's view of the pool (issue #31): the batch, the counters, every thread's state.
+    void diag(std::FILE* f) const;
    ~ExpertPool();
    ExpertPool(const ExpertPool&) = delete;
    ExpertPool& operator=(const ExpertPool&) = delete;
@@ -142,14 +154,26 @@ public:
    /// **A PARKED WORKER SPINS FOR THIS LONG, THEN SLEEPS.**  The park is a `_mm_pause` spin because a layer's
    /// batches are microseconds apart and a wake-up from the OS costs more than that.  But a spin that never ends
    /// keeps every worker's core at 100% while the engine waits for a request - issue #4, "CPU 50% even when
-    /// doing nothing" (7 of the 5700X's 16 threads).  Inside a request the gaps between batches are far below
-    /// this, so the token path still never sleeps; between requests the workers block on `sleep_cv_`.
+    /// doing nothing" (7 of the 5700X's 16 threads).  Between requests the workers block on `sleep_cv_`.  NOT only
+    /// between requests: with most experts in VRAM (a 24 GB card) many layers have no CPU work, so the workers
+    /// also sleep mid-request - which the claim protocol above must survive (issue #29).
+    /// `STRATA_POOL_SPIN_US` overrides it (a test knob: a short spin makes the workers sleep constantly).
    static constexpr std::chrono::milliseconds kSpinBeforeSleep{20};
+    /// A pool wait that sees no completion for this long is a bug; the engine stops with a message instead of
+    /// spinning forever, and the server starts it again (issue #29).
+    static constexpr std::chrono::seconds kStall{60};

 private:
    void worker(int id);
-    void drain(int id, ExpertScratch& scratch);
+    void drain(int id, ExpertScratch& scratch, uint32_t epoch);
    void run_phase(int mode, int n_tasks);
+    /// Claim the next job of batch `epoch`, or -1 (that batch is exhausted, or it is not the current one).
+    int claim(uint32_t epoch);
+    /// Publish the batch whose description the caller has just written: reset `done`, then `head`, then the epoch.
+    uint32_t begin_batch(int n);
+    /// The host's waits, bounded by `kStall`.
+    void wait_parked(const char* what);
+    void wait_done(int n);
    /// Bump `epoch_`, and wake the workers that went to sleep.  Every publish goes through here.
    void publish();

@@ -176,7 +200,14 @@ private:
    //
    // The counter was diagnostic only - `pauses()` was read in one place, to print a number nothing branched on
    // - so it is deleted rather than amortised.  `alignas(64)` then stops the remaining four sharing.
-    alignas(64) std::atomic<uint32_t> head_{0};
+    // issue #31 diagnostics: each worker's state (kParked, kSleeping, kBetween, or the job it runs) and the host's
+    // (kIdle, kWaitParked, kWaitDone, or its job), printed by the serve watchdog through `diag`
+    static constexpr int32_t kParked = -1, kSleeping = -2, kBetween = -3, kIdle = -10, kWaitParked = -11,
+                             kWaitDone = -12;
+    std::unique_ptr<std::atomic<int32_t>[]> wstate_;
+    std::atomic<int32_t> hstate_{kIdle};
+    std::atomic<int64_t> hstate_ms_{0};
+    alignas(64) std::atomic<uint64_t> head_{0};   // epoch << 32 | njobs << 16 | next index (issue #29)
    alignas(64) std::atomic<uint32_t> done_{0};
    alignas(64) std::atomic<uint32_t> parked_{0};
    alignas(64) std::atomic<uint32_t> epoch_{0};
@@ -186,6 +217,7 @@ private:
    alignas(64) std::atomic<uint32_t> sleepers_{0};
    std::mutex sleep_mu_;
    std::condition_variable sleep_cv_;
+    std::chrono::microseconds spin_before_sleep_{kSpinBeforeSleep};
    std::vector<std::thread> threads_;
    std::vector<ExpertScratch> scratch_;   // one per worker: no allocation, no false sharing of the hot data
    // run_split state: mode 0 = whole experts, 1 = gate/up row parts, 2 = down row parts
@@ -0,0 +1,56 @@
+// include/strata/kernels/cvec.hpp - a control vector on the residual stream: the engine's side of llama.cpp's
+// `llama_adapter_cvec` with the `--cvec-mode` / `--cvec-dir` extension of the `experimental-speed-projection`
+// package (its `02-cvec-projection-mode.patch`, and `01-qwen4exp-cvec-hooks.patch` for WHERE it applies).
+//
+// After layer l's FFN write - llama.cpp's `build_cvec(res_hc, il)` right after the layer's second
+// `build_hc_combine` - every one of the hc residual streams h of every token becomes
+//
+//   project:  h <- h - s_l (h . v_l) v_l        v_l = d / |d|, s_l = |d|, d = the scaled direction
+//   add:      h <- h + d                        llama.cpp's stock additive control vector
+//
+// for l in [first, last].  `d` is the GGUF's `direction.<src>` times the file's scale (summed over files), with
+// src = l (per-layer) or one fixed layer (single:L, project mode only), exactly as llama.cpp's loader places it;
+// layer 0 has no direction.  Off unless a vector is loaded (--control-vector-scaled); a loaded one is switched
+// per request through a device flag, so the captured graphs are the same either way.  With the flag off the
+// result is bit-identical to the engine without a vector: the kernel then only applies the pending residual
+// write, with the fused read's own arithmetic.
+#pragma once
+
+#include <cstdint>
+#include <string>
+#include <vector>
+
+namespace strata::kernels {
+
+struct Cvec {
+    const float* dir = nullptr;   ///< device, n_layers * n_embd: project = the unit v_l, add = d_l; zero rows elsewhere
+    const float* s = nullptr;     ///< device, n_layers: project = s_l, add = 1; 0 = the layer is not steered
+    const int* on = nullptr;      ///< device int: 0 = this request runs the stock model
+    int mode = 0;                 ///< 0 = project, 1 = add
+    int first = 0, last = -1;     ///< the steered layers, inclusive
+    int64_t n_embd = 0, hc = 0;
+    bool loaded() const { return dir != nullptr; }
+    /// Layer l's FFN write is followed by the vector (with a real direction there).  Decided at load time, never
+    /// per request: it changes where the residual write happens, so the graphs depend on it.
+    bool covers(int64_t l) const { return loaded() && l >= first && l <= last && l < (int64_t) steered.size() && steered[(size_t) l]; }
+    std::vector<bool> steered;    ///< host copy of s_l != 0
+};
+
+/// The loaded vector (empty until `cvec_upload`).
+const Cvec& cvec();
+
+/// Upload a vector built by the loader: `dir` is n_layers * n_embd, `s` n_layers (see `Cvec`).  Starts ON.
+bool cvec_upload(const std::vector<float>& dir, const std::vector<float>& s, int mode, int first, int last,
+                 int64_t n_embd, int64_t hc, std::string& err);
+
+/// The per-request switch.  Synchronizes the device when it changes, so call it between requests.
+void cvec_set_enabled(bool on);
+bool cvec_enabled();
+
+/// Layer `layer`'s vector on T tokens' residual stacks (`R + t * r_ld`, hc streams of n_embd).  With `write`, the
+/// pending FFN write `R += bo * 2 sigmoid(inj / hc)` (the fused read's arithmetic) is applied first, for callers
+/// whose writes are folded into the next layer's read; without it, R must already hold the layer's output.
+void cvec_apply(float* R, int64_t layer, int64_t T, int64_t r_ld, const float* bo, int64_t bo_ld, const float* inj,
+                int64_t inj_ld, bool write, void* stream);
+
+}  // namespace strata::kernels
@@ -0,0 +1,61 @@
+// include/strata/kernels/kv_q4.hpp - Q4_0 KV storage with Walsh-Hadamard rotation for the QSA layers (`--kv q4_0`).
+//
+// From PR #21 (code-martin). K and V are rotated by the orthonormal 256-point Hadamard matrix H before they are
+// quantized to ggml's q4_0 (32 values per block: an fp16 scale + 16 bytes of 4-bit codes, 144 B per head per cell,
+// 576 B per cell for K and V of both heads, vs 1,056 B in INT8). The rotation spreads a head's outlier channels
+// over all 256 dimensions, which is what makes 4 bits usable. The query is rotated the same way, so
+// <Hq, Hk> = <q, k> and the scores are unchanged, and the attention output (a mix of rotated values) is rotated
+// back with H (self-inverse). What is lossy is the 4-bit rounding itself: measured, not assumed, in
+// bench/results/2026-09-27-kv-q4.
+//
+// With KV streaming (--kv-resident) the codes live in the host copy and the resident slots like INT8's (KvHostPools
+// k_q4/v_q4, the same block granule), so both options combine.
+#pragma once
+
+#include "strata/kernels/qsa.hpp"
+
+#include <cstdint>
+
+namespace strata::kernels {
+
+inline constexpr int QK4_0 = 32;
+
+#pragma pack(push, 1)
+struct block_q4_0 {
+    uint16_t d;             // scale (fp16 bits)
+    uint8_t qs[QK4_0 / 2];  // 32 4-bit codes: element j in the low nibble of qs[j], j + 16 in the high one
+};
+#pragma pack(pop)
+
+static_assert(sizeof(block_q4_0) == 18, "block_q4_0 must be 18 bytes");
+
+/// Bytes per cell and KV head: 8 blocks of 32 for head_dim 256 -> 144.
+inline uint64_t kv_q4_bytes_per_head(int head_dim) { return (uint64_t) (head_dim / QK4_0) * sizeof(block_q4_0); }
+
+/// Bytes per cell (one token, one layer): K and V of every KV head.
+inline uint64_t kv_q4_bytes_per_cell(const QsaShapes& s) {
+    return (uint64_t) s.n_head_kv * kv_q4_bytes_per_head((int) s.head_dim) * 2;
+}
+
+/// Orthonormal Fast Walsh-Hadamard Transform of rows of 256 floats (scale 1/16): its own inverse.
+void fwht256_cuda(const float* src, float* dst, int64_t n_rows, void* stream);
+inline void fwht256_inplace_cuda(float* data, int64_t n_rows, void* stream) { fwht256_cuda(data, data, n_rows, stream); }
+
+/// Append the (already rotated) cell at step[kStepPos]. With a host copy (KV streaming) it is written there too,
+/// and to VRAM only if its block is resident.
+void kv_append_q4_step(uint8_t* k_q4, uint8_t* v_q4, const int32_t* page_table, const int32_t* step,
+                       const float* kcur, const float* vcur, const QsaShapes& s, void* stream,
+                       const KvHostPools* host = nullptr);
+
+/// The prompt path: T consecutive (rotated) cells from pos0, K/V [T, n_head_kv, 256]; also into `stage` (identity
+/// layout, the one-layer staging pool of a streamed session) when given.
+void kv_append_q4(uint8_t* k_q4, uint8_t* v_q4, const int32_t* page_table, int64_t pos0, int64_t T, const float* K,
+                  const float* V, const QsaShapes& s, void* stream, const KvHostPools* host = nullptr,
+                  const KvHostPools* stage = nullptr);
+
+/// Gather step[kStepWidth] cells named by `ids` into FP16 scratch [id][kv_head][head_dim] (still rotated).
+void kv_gather_q4_step(const uint8_t* k_q4, const uint8_t* v_q4, const int32_t* page_table, const int32_t* ids,
+                       const int32_t* step, int64_t max_ids, const QsaShapes& s, uint16_t* k_scratch,
+                       uint16_t* v_scratch, void* stream);
+
+}  // namespace strata::kernels
@@ -36,9 +36,15 @@ struct KvHostPools {
    int8_t* v_q = nullptr;
    uint16_t* k_scale = nullptr;  ///< int8 mode: fp16 scale per 64 values
    uint16_t* v_scale = nullptr;
-    bool present() const { return k_pool != nullptr || k_q != nullptr; }
+    uint8_t* k_q4 = nullptr;      ///< q4_0 mode (kv_q4.hpp): block_q4_0 codes, 144 B per cell and head
+    uint8_t* v_q4 = nullptr;
+    bool present() const { return k_pool != nullptr || k_q != nullptr || k_q4 != nullptr; }
 };

+/// The KV storage format, for the functions below that move whole blocks (`fmt`): fp16, int8 (+ scales), q4_0.
+/// (A bool `int8` argument still reads as kKvF16 / kKvInt8.)
+enum KvFormat : int { kKvF16 = 0, kKvInt8 = 1, kKvQ4 = 2 };
+
 /// The residency map of a streamed layer, all device memory at fixed addresses (the graphs bake them in).
 struct KvStreamMap {
    int32_t* page_table = nullptr;  ///< (n_blocks,) block -> slot, or -1
@@ -62,7 +68,7 @@ void kv_stream_reset(const KvStreamMap& m, void* stream);
 /// Make every block named by the selections of `n_q` queries resident (ids [n_q][cap], width from
 /// steps[q * kStepCount + kStepWidth]). Capturable. `n_slots` must hold the distinct blocks of one call
 /// (n_q x (cap / page_size + 2)); a call that cannot sets ctl[3] (see `kv_stream_counters`).
-void kv_stream_resolve(const KvStreamMap& m, const QsaAttnPools& slots, const KvHostPools& host, bool int8,
+void kv_stream_resolve(const KvStreamMap& m, const QsaAttnPools& slots, const KvHostPools& host, int fmt,
                       const int32_t* ids, const int32_t* steps, int64_t n_q, int64_t cap, const QsaShapes& s,
                       void* stream);

@@ -71,12 +77,12 @@ void kv_ring_table(int32_t* page_table, int64_t n_blocks, int64_t n_slots, void*

 /// Copy blocks [b0, b1) of the host copy into the pool pages `page_table` names (host-side table `phys(b)`
 /// computed as `b % n_slots`: the ring restore). Not capturable.
-void kv_ring_restore(const QsaAttnPools& slots, const KvHostPools& host, bool int8, int64_t b0, int64_t b1,
+void kv_ring_restore(const QsaAttnPools& slots, const KvHostPools& host, int fmt, int64_t b0, int64_t b1,
                     int64_t n_slots, const QsaShapes& s, void* stream);

 /// Copy the first `n_blocks` blocks of the host copy into a fully resident (identity-layout) pool: the prompt
 /// path's staging of one layer. Not capturable (DMA).
-void kv_stage_from_host(const QsaAttnPools& stage, const KvHostPools& host, bool int8, int64_t n_blocks,
+void kv_stage_from_host(const QsaAttnPools& stage, const KvHostPools& host, int fmt, int64_t n_blocks,
                        const QsaShapes& s, void* stream);

 struct KvStreamCounters {
@@ -87,6 +93,6 @@ struct KvStreamCounters {
 KvStreamCounters kv_stream_counters(const KvStreamMap& m);

 /// Bytes of one block (page) of K and V together.
-uint64_t kv_block_bytes(const QsaShapes& s, bool int8);
+uint64_t kv_block_bytes(const QsaShapes& s, int fmt);

 }  // namespace strata::kernels
@@ -27,4 +27,12 @@ void native_ple_postops(const float* projected_key, const float* hidden,
                        const PleWeights& weights, const NativePlePostopsBuffers& buffers,
                        void* stream);

+/// The prompt path: T tokens at once, element for element the arithmetic of T calls of native_ple_postops with
+/// the history advanced after each.  `key` (T x 10240) holds the projected keys and is normalized in place;
+/// `hidden` (T x 10240) becomes the result; `value` T x 2560; `query_norm` (T x 10240, the normalized hidden, then
+/// the normalized gated values), `gated` (T x 10240) and `gate` (T x 4) are scratch; `history` (9 rows per
+/// channel) is advanced past the T tokens.
+void native_ple_postops_batch(float* key, float* hidden, const float* value, float* history, const PleWeights& w,
+                              float* query_norm, float* gated, float* gate, int T, void* stream);
+
 } // namespace strata::kernels
@@ -27,6 +27,8 @@ struct QsaAttnPools {
    const int8_t* v_q = nullptr;
    const uint16_t* k_scale = nullptr;  ///< fp16 [page][kv_head][page_size][head_dim / 64]
    const uint16_t* v_scale = nullptr;
+    const uint8_t* k_q4 = nullptr;      ///< q4_0 block_q4_0 [page][kv_head][page_size][head_dim / 32 * 18]
+    const uint8_t* v_q4 = nullptr;
    const int32_t* page_table = nullptr;
 };

@@ -1,12 +1,10 @@
 // include/strata/kernels/sampler.hpp - the sampler chain, host-callable (P2.S2).
 //
-//     penalties  ->  top_k  ->  top_p  ->  temperature  ->  pick
+//     penalties -> top_k -> min_p -> top_p -> temperature -> penalties -> pick
 //
-// The ORDER is specified in docs/sampling.md, transcribed from llama.cpp's own chain.  Two facts there are
-// easy to get backwards: temperature comes AFTER the truncation filters, and penalties come AFTER temperature.
-//
-// PENALTIES ARE NOT IMPLEMENTED YET: they need the token history, which is not in this signature, so a caller
-// passing a non-zero `penalty_last_n` gets a loud failure rather than a silently unpenalised sample.
+// The ORDER is specified in docs/sampling.md, transcribed from llama.cpp's own chain.  Three facts there are
+// easy to get backwards: temperature comes AFTER the truncation filters, penalties come AFTER temperature as
+// well as before the selection, and min_p cuts BEFORE top_p (both on the pre-temperature logits).
 #pragma once

 #include <cstdint>
@@ -16,6 +14,7 @@ namespace strata::kernels {
 struct SamplerParams {
    int top_k = 20;              // 0 disables the filter
    float top_p = 0.95f;         // 1.0 disables the filter
+    float min_p = 0.0f;          // 0 disables; keeps tokens with p >= min_p * p_max (a prefix of the top_k list)
    float temperature = 1.0f;    // <= 0 means greedy
    int min_keep = 1;            // top_p keeps at least this many
    int penalty_last_n = 0;      // 0 disables; the window over `history` to count occurrences in
@@ -36,6 +36,9 @@ public:
    void native(const uint16_t* X, int ggml_type, const void* W_blocks, float* Y, int64_t T, int64_t N, int64_t K,
                int64_t ldy = 0, float beta = 0.0f);

+    /// Caller-owned buffers only: the scratch and workspace moved (the prompt path laid its buffers out again).
+    void rebind(uint16_t* scratch, int64_t scratch_elems, void* workspace, size_t ws_bytes);
+
    uint16_t* scratch() const { return scratch_; }
    int64_t scratch_elems() const { return scratch_elems_; }
    void* stream() const { return stream_; }
@@ -0,0 +1,73 @@
+// include/strata/prefill/moe_mmq.hpp - prompt-speed plan step 2b: the prompt path's experts through llama.cpp's MMQ
+// kernels (ggml-cuda mmq.cuh, MIT): the weights stay quantized and the activations are rounded to q8_1, the
+// products run on int8 tensor cores.  The dequantize-to-FP16 + cuBLAS path wrote ~10 MB of FP16 per expert and
+// multiplied in FP16; this reads the ~1.4-2 MB expert once.  A group of experts is gathered into one buffer
+// (`gather_*`, one launch per expert as its blob arrives) and multiplied in one launch per product.
+#pragma once
+
+#include <cstddef>
+#include <cstdint>
+
+namespace strata::prefill::mmq {
+
+/// This build has the MMQ path (the ggml sources were available to the build).
+bool built();
+/// MMQ covers this ggml type (the i-quants and Q2_0 the packs use; IQ1_M is not covered).
+bool supported(int ggml_type);
+/// Bytes of one expert's gate+up ([2*n_ff, n_embd]) or down ([n_embd, n_ff]) weights in `ggml_type`.
+size_t matrix_bytes(int ggml_type, int64_t rows, int64_t cols);
+/// Bytes of `rows` activation rows of `cols` values quantized for MMQ (the row padded to 512 values).
+size_t q8_bytes(int64_t rows, int64_t cols);
+
+/// q8_1 activations for MMQ against weights of `ggml_type`: row i of the output is row ids[i] of x (or row i when
+/// ids is null); `x` has `ld` floats per row.
+void quantize(const float* x, const int32_t* ids, void* xq, int ggml_type, int64_t cols, int64_t ld, int64_t rows,
+              void* stream);
+
+/// One launch over n experts whose weights lie `expert_bytes` apart from `w`: for expert e, the activation rows
+/// [bounds[e], bounds[e+1]) of `xq` (bounds on the device, n+1 entries) times its [w_rows, w_cols] matrix into
+/// dst rows of the same indices (`ld_dst` floats apart, via `ids`: dst row = ids[row], an identity table works).
+/// `total_rows`: the rows of xq; `max_rows`: the most rows one expert has (the launch grid).
+struct Product {
+    const void* w = nullptr;
+    int type = -1;
+    int64_t w_rows = 0, w_cols = 0;
+    size_t expert_bytes = 0;
+    int n = 0;
+    const void* xq = nullptr;
+    const int32_t* bounds = nullptr;
+    const int32_t* ids = nullptr;
+    int64_t total_rows = 0, max_rows = 0;
+    float* dst = nullptr;
+    int64_t ld_dst = 0;
+};
+
+/// The launch context (llama.cpp's MMQ keeps a small scratch pool for its stream-k fixup).  One per prompt path.
+class Context {
+public:
+    Context();
+    ~Context();
+    Context(const Context&) = delete;
+    Context& operator=(const Context&) = delete;
+    void run(const Product& p, void* stream);
+
+private:
+    void* ctx_ = nullptr;
+};
+
+/// A GGUF-native expert (gate at `gate`, up at `up`, down at `down`, each its GGUF rows) into a group buffer's
+/// slot: gate rows then up rows at `gu_dst`, down at `d_dst`.
+void gather_native(const void* gate, const void* up, size_t gu_half_bytes, const void* down, size_t d_bytes,
+                   void* gu_dst, void* d_dst, void* stream);
+/// A Strata-pack Q2_0 expert blob (codes and fp16 scales in separate planes, gate/up rows interleaved) into GGUF
+/// Q2_0 blocks: gate/up [1280, 2560] at `gu_dst` (rows stay interleaved), down [2560, 640] at `d_dst`.  Same values.
+void gather_strata_q2(const uint8_t* blob, void* gu_dst, void* d_dst, void* stream);
+
+/// h[r, k] = silu(gate) * up of GU rows [2 n_ff wide]: interleaved (gate 2k, up 2k+1: the Strata pack) or split
+/// (gate k, up n_ff + k: GGUF).  FP32 out (the down product's quantizer reads floats).
+void swiglu(const float* gu, float* h, int64_t rows, int64_t n_ff, bool interleaved, void* stream);
+
+/// dst[i] = i for i < n (the identity row map MMQ's MoE mode writes through).
+void iota(int32_t* dst, int64_t n, void* stream);
+
+}  // namespace strata::prefill::mmq
@@ -48,6 +48,16 @@ public:
              core::ExpertSource* src, const core::ExpertCache* cache, const int32_t* host_res, int64_t chunk,
              void* stream, std::string& err, void* borrow = nullptr, uint64_t borrow_bytes = 0);

+    /// With borrowed buffers: lay them out again for chunks of `chunk` tokens (at most `init`'s) in `borrow` - a
+    /// request lends only the slots its prompt needs.  The stream must be idle (between prompts).
+    bool relayout(int64_t chunk, void* borrow, uint64_t borrow_bytes, std::string& err);
+    int64_t chunk() const;
+
+    /// The share of the streamed experts' bytes DMA-able straight from pinned RAM (1 = all).  Sizes the streamed
+    /// ring (a big one only pays when the copy engine, not the host copies, is the limit); set before bytes_needed.
+    static void set_pinned_share(double share);
+    static double pinned_share();
+
    /// Device bytes `init` needs for a chunk of `chunk` tokens (what a borrowed region must hold).
    static uint64_t bytes_needed(const core::ModelGeometry& g, const core::SessionState& ss, int64_t chunk);

@@ -70,6 +80,7 @@ public:
    const float* const* embd_rows = nullptr;

 private:
+    bool carve(std::size_t T, void* alloc);   // the device buffers of a chunk (prefill.cpp's Alloc)
    struct Impl;
    std::unique_ptr<Impl> impl_;
    PrefillStats stats_;
@@ -0,0 +1,52 @@
+// include/strata/spec/draft_policy.hpp - per verify round: the MTP's window, or a lookup (suffix) window?
+//
+// The suffix drafter (prompt lookup) proposes the tokens that followed an earlier repeat of the context. Taken
+// whenever it proposes more than the MTP, it lost 2-8% on ordinary text: a long lookup window costs much more to
+// verify than the MTP's usual 3-4 tokens, and it is only worth that when enough of it is accepted. llama.cpp's
+// lookup decoding answers the same problem with confidence thresholds on its n-gram statistics; here the policy
+// learns both sides online and compares expected committed tokens per millisecond:
+//
+//   MTP     E = the mean tokens a window of that size has committed (EMA), at the measured cost of that size
+//   lookup  E(k) = 1 + q + q^2 + ... + q^k for k <= the proposal, q = the acceptance rate of lookup drafts whose
+//           match was about as long (4 buckets of match length, decayed counts), at the measured cost of k + 1
+//
+// and takes the lookup window only when its best E/cost beats the MTP's by `margin`. Costs are the measured round
+// times per window size (EMA; sizes not seen yet are scaled from seen ones by a prior shape), so the policy adapts
+// to the machine and the context length. It only chooses which drafts to verify: the output is unchanged.
+#pragma once
+
+#include <array>
+
+namespace strata::spec {
+
+class DraftPolicy {
+public:
+    static constexpr int kMaxT = 8;
+    static constexpr int kBuckets = 4;
+
+    explicit DraftPolicy(int max_t, double margin = 0.03);
+
+    struct Pick {
+        bool lookup = false;
+        int t = 1;                      // window size (1 + drafts)
+    };
+    /// `t_mtp`: the MTP's window; `lookup_k`: the lookup proposal's length (0 = none); `match`: its match length.
+    Pick choose(int t_mtp, int lookup_k, int match) const;
+    /// After the round: the window it used, the drafts accepted, and the round's time (verify + commit + draft).
+    void observe(bool lookup, int t, int accepted, int match, double round_ms);
+
+    double lookup_rate(int match) const;   // current q for a match length
+    double cost_ms(int t) const;           // measured or scaled round time of a window of t tokens
+
+private:
+    static int bucket(int match);
+    double mtp_tokens(int t) const;
+
+    int max_t_;
+    double margin_;
+    std::array<double, kMaxT + 1> cost_{}, cost_n_{};      // round ms by window size
+    std::array<double, kMaxT + 1> mtp_tok_{}, mtp_n_{};    // tokens committed by MTP windows of that size
+    std::array<double, kBuckets> ok_{}, bad_{};            // lookup drafts accepted / windows cut short, decayed
+};
+
+}  // namespace strata::spec
@@ -1,152 +0,0 @@
-<!doctype html>
-<html lang="en">
-<head>
-<meta charset="utf-8">
-<meta name="viewport" content="width=device-width, initial-scale=1">
-<title>Strata</title>
-<style>
-  :root { --bg:#f6f7f9; --card:#fff; --fg:#1b1f24; --mute:#667085; --line:#e3e6ea; --acc:#2f6fed; --me:#e9f0ff; --ok:#1a7f37; }
-  @media (prefers-color-scheme: dark) {
-    :root { --bg:#111418; --card:#1a1e24; --fg:#e7e9ec; --mute:#9aa3ae; --line:#2b313a; --acc:#6b9bff; --me:#1f2b44; --ok:#3fb950; }
-  }
-  * { box-sizing: border-box; }
-  body { margin:0; background:var(--bg); color:var(--fg); font:15px/1.5 system-ui, -apple-system, "Segoe UI", sans-serif; }
-  main { max-width:860px; margin:0 auto; padding:16px; display:flex; flex-direction:column; gap:12px; min-height:100vh; }
-  .card { background:var(--card); border:1px solid var(--line); border-radius:12px; padding:14px 16px; }
-  h1 { font-size:20px; margin:0; display:flex; align-items:center; gap:8px; }
-  .dot { width:10px; height:10px; border-radius:50%; background:var(--mute); display:inline-block; }
-  .dot.on { background:var(--ok); }
-  .mute { color:var(--mute); font-size:13px; }
-  code { background:var(--bg); border:1px solid var(--line); border-radius:6px; padding:1px 6px; font-size:13px; }
-  .grid { display:grid; grid-template-columns: max-content 1fr; gap:4px 12px; margin-top:8px; font-size:14px; }
-  #log { flex:1; display:flex; flex-direction:column; gap:10px; overflow-y:auto; min-height:240px; }
-  .msg { padding:10px 12px; border-radius:10px; white-space:pre-wrap; word-wrap:break-word; }
-  .msg.user { background:var(--me); align-self:flex-end; max-width:85%; }
-  .msg.bot { background:var(--card); border:1px solid var(--line); }
-  .msg img { max-width:220px; max-height:160px; display:block; border-radius:6px; margin-bottom:6px; }
-  details { color:var(--mute); font-size:13px; margin-bottom:6px; }
-  form { display:flex; gap:8px; align-items:flex-end; flex-wrap:wrap; }
-  textarea { flex:1; min-width:200px; min-height:52px; max-height:200px; resize:vertical; padding:10px; border-radius:10px;
-             border:1px solid var(--line); background:var(--card); color:var(--fg); font:inherit; }
-  button, label.btn { border:1px solid var(--line); background:var(--card); color:var(--fg); border-radius:10px; padding:10px 14px;
-                      font:inherit; cursor:pointer; }
-  button.primary { background:var(--acc); border-color:var(--acc); color:#fff; }
-  button:disabled { opacity:.5; cursor:default; }
-  .row { display:flex; gap:14px; align-items:center; flex-wrap:wrap; font-size:13px; color:var(--mute); }
-  #att { font-size:13px; color:var(--mute); }
-</style>
-</head>
-<body>
-<main>
-  <div class="card">
-    <h1><span id="dot" class="dot"></span> Strata <span id="state" class="mute">connecting ...</span></h1>
-    <div class="grid">
-      <span class="mute">Model</span><span id="model">-</span>
-      <span class="mute">Context</span><span id="ctx">-</span>
-      <span class="mute">Images</span><span id="img">-</span>
-      <span class="mute">OpenAI API</span><span><code id="oai"></code> (any API key<span id="keynote"></span>)</span>
-      <span class="mute">Anthropic API</span><span><code id="ant"></code></span>
-    </div>
-    <p class="mute" style="margin:10px 0 0">Point any app with an "OpenAI-compatible" or "Anthropic" provider at these
-      addresses. Close the Strata window to stop the model.</p>
-  </div>
-
-  <div id="log" class="card"></div>
-
-  <div class="card">
-    <form id="f">
-      <textarea id="q" placeholder="Ask something ... (Enter sends, Shift+Enter = new line)"></textarea>
-      <label class="btn" id="attbtn" hidden>Picture<input id="file" type="file" accept="image/png,image/jpeg,image/gif,image/bmp" hidden></label>
-      <button class="primary" id="send">Send</button>
-      <button type="button" id="stop" disabled>Stop</button>
-    </form>
-    <div class="row" style="margin-top:8px">
-      <label>Thinking <select id="think" style="padding:4px;border-radius:8px;border:1px solid var(--line);background:var(--card);color:var(--fg)">
-        <option value="none">off - answer at once</option>
-        <option value="low">low</option>
-        <option value="medium" selected>medium</option>
-        <option value="high">high - best on hard questions, slowest</option>
-      </select></label>
-      <span id="att"></span>
-      <button type="button" id="clear" style="padding:4px 10px">New chat</button>
-      <input id="key" type="password" placeholder="API key" hidden style="padding:6px;border-radius:8px;border:1px solid var(--line)">
-    </div>
-  </div>
-</main>
-<script>
-const $ = id => document.getElementById(id);
-const base = location.origin;
-$("oai").textContent = base + "/v1";
-$("ant").textContent = base + "/v1/messages";
-let history = [], image = null, ctrl = null;
-
-async function status() {
-  try {
-    const h = await (await fetch("/health")).json();
-    $("dot").classList.add("on"); $("state").textContent = "running";
-    $("model").textContent = h.model; $("ctx").textContent = h.max_context.toLocaleString() + " tokens";
-    $("img").textContent = h.images ? "on" : "off (run setup again and answer yes to images)";
-    $("attbtn").hidden = !h.images;
-    if (h.api_key) { $("key").hidden = false; $("keynote").textContent = " - this server needs its key"; }
-  } catch (e) { $("state").textContent = "not reachable - is the Strata window still open?"; }
-}
-status();
-
-function bubble(cls, text) {
-  const d = document.createElement("div"); d.className = "msg " + cls; d.textContent = text || "";
-  $("log").appendChild(d); $("log").scrollTop = $("log").scrollHeight; return d;
-}
-
-$("file").onchange = () => {
-  const f = $("file").files[0]; if (!f) return;
-  const r = new FileReader();
-  r.onload = () => { image = r.result; $("att").textContent = "picture: " + f.name; };
-  r.readAsDataURL(f);
-};
-$("clear").onclick = () => { history = []; $("log").innerHTML = ""; };
-$("stop").onclick = () => ctrl && ctrl.abort();
-$("q").addEventListener("keydown", e => { if (e.key === "Enter" && !e.shiftKey) { e.preventDefault(); $("f").requestSubmit(); } });
-
-$("f").onsubmit = async e => {
-  e.preventDefault();
-  const text = $("q").value.trim(); if (!text) return;
-  $("q").value = "";
-  const content = image ? [{type: "image_url", image_url: {url: image}}, {type: "text", text}] : text;
-  const u = bubble("user", "");
-  if (image) { const im = document.createElement("img"); im.src = image; u.appendChild(im); }
-  u.appendChild(document.createTextNode(text));
-  history.push({role: "user", content});
-  image = null; $("att").textContent = ""; $("file").value = "";
-  const b = bubble("bot", ""); const det = document.createElement("details"); const sum = document.createElement("summary");
-  sum.textContent = "thinking"; det.appendChild(sum); const th = document.createElement("div"); det.appendChild(th);
-  const ans = document.createElement("div"); b.appendChild(ans);
-  $("send").disabled = true; $("stop").disabled = false; ctrl = new AbortController();
-  let answer = "";
-  try {
-    const headers = {"Content-Type": "application/json"};
-    if ($("key").value) headers["Authorization"] = "Bearer " + $("key").value;
-    const body = {model: "strata", messages: history, stream: true, max_tokens: 8192};
-    body.reasoning_effort = $("think").value;
-    const r = await fetch("/v1/chat/completions", {method: "POST", headers, body: JSON.stringify(body), signal: ctrl.signal});
-    if (!r.ok) { ans.textContent = "error: " + ((await r.json()).error || {}).message; throw 0; }
-    const rd = r.body.getReader(), dec = new TextDecoder(); let buf = "";
-    for (;;) {
-      const {value, done} = await rd.read(); if (done) break;
-      buf += dec.decode(value, {stream: true});
-      let i;
-      while ((i = buf.indexOf("\n\n")) >= 0) {
-        const line = buf.slice(0, i).trim(); buf = buf.slice(i + 2);
-        if (!line.startsWith("data: ") || line === "data: [DONE]") continue;
-        const d = JSON.parse(line.slice(6)).choices[0].delta;
-        if (d.reasoning_content) { if (!det.parentNode) b.insertBefore(det, ans); th.textContent += d.reasoning_content; }
-        if (d.content) { answer += d.content; ans.textContent = answer; }
-        $("log").scrollTop = $("log").scrollHeight;
-      }
-    }
-  } catch (err) { if (err && err.name === "AbortError") ans.textContent = answer + " [stopped]"; }
-  history.push({role: "assistant", content: answer});
-  $("send").disabled = false; $("stop").disabled = true; ctrl = null;
-};
-</script>
-</body>
-</html>
@@ -10,6 +10,7 @@ data, http(s) URLs or local file paths) go through `strata-vision` (the model's
 embeddings (`GENI`).  JPEG/PNG/BMP/GIF go straight in; WebP, TIFF, AVIF, ... (agents like omp send WebP) are
 converted to PNG first with Pillow.
 Requests whose prompt plus max tokens exceed the engine's context are REJECTED with 400, never truncated.
+An unset (or 0, or -1) max tokens means "unlimited": whatever the prompt leaves of the context.

 The engine boundary is `Engine.generate(prompt_ids, max_new, sampling, cancel) -> iterator of token ids`.
 `StrataEngine` keeps one `strata --serve` process resident (weights, expert arena and VRAM tier load once) and
@@ -18,6 +19,7 @@ talks to it over stdin/stdout; `MockEngine` is a scripted stand-in that makes ev
 from __future__ import annotations

 import argparse
+import collections
 import base64
 import hashlib
 import json
@@ -42,6 +44,7 @@ from serve.frontend import (ChatTemplate, Event, OutputParser, anthropic_to_mess

 IM_END = "<|im_end|>"
 IMAGE_PAD = "<|image_pad|>"
+CTX_SLACK = 8               # `strata --serve` rejects prompt + max_new + 8 > context: keep the same margin here


 # ------------------------------------------------------------------------------------------------ engines
@@ -69,19 +72,40 @@ class MockEngine:
            yield t


+class EngineDied(RuntimeError):
+    """The engine process ended in the middle of a request (issue #27: on Linux, the out-of-memory killer)."""
+
+
 class StrataEngine:
    """The resident engine: `strata --serve` reads `GEN <max_new> <ids>` lines and streams `T <id>` lines, then
-    `DONE ...`.  Requests are serialized by the service's FIFO, so one pipe is enough."""
+    `DONE ...`.  Requests are serialized by the service's FIFO, so one pipe is enough.
+
+    Per-request sampling rides the same line as engine-side keys between max_new and the ids
+    (`temperature=F top_p=F top_k=N seed=N`, the engine's own spelling).  An absent temperature keeps the
+    engine's default, which is greedy; `temperature=0` means the same thing, so it is not forwarded.
+    """

    def __init__(self, exe: str, args: list[str], cwd: str | None = None, log: str | None = None,
                 env: dict | None = None):
+        self.spawn = (exe, list(args), cwd, log, env)   # to start it again after it died (issue #27)
+        self.log_path = log
        self.log = open(log, "a", encoding="utf-8") if log else subprocess.DEVNULL
        self.proc = subprocess.Popen([exe, "--serve", *args], cwd=cwd, stdin=subprocess.PIPE, stdout=subprocess.PIPE,
                                     stderr=self.log, text=True, encoding="utf-8", bufsize=1, env=env)
        self.max_context = 0
        self.can_stop = False            # the engine honours a STOP line mid-request (READY <ctx> stop)
        self.last = {}
+        self.info = {}                   # INFO key=value facts (engine 0.1.8+): kv, expert slots, ... (Monitor tab)
+        self.progress = None             # (read, total) prompt tokens while a prompt is read, from PP lines
+        try:                             # a ready-made engine's BUILD.json says its version
+            self.info["version"] = json.loads((Path(exe).parent / "BUILD.json").read_text()).get("version")
+        except (OSError, ValueError):
+            self.info["version"] = None
        for line in self.proc.stdout:
+            if line.startswith("INFO "):
+                for kv in line.split()[1:]:
+                    k, _, v = kv.partition("=")
+                    self.info[k] = int(v) if v.lstrip("-").isdigit() else v
            if line.startswith("READY"):
                f = line.split()
                self.max_context = int(f[1])
@@ -96,20 +120,112 @@ class StrataEngine:
    def _pump(self):
        for line in self.proc.stdout:
            self.lines.put(line)
+        self.ended = True                               # its output closed: it is gone, even before the OS says so
        self.lines.put(None)

+    def death_note(self) -> str:
+        """Why the engine most likely ended, from the end of its log: its own watchdog (issue #29), else RAM."""
+        tail = ""
+        try:
+            with open(self.log_path, "rb") as f:
+                f.seek(0, 2)
+                f.seek(max(0, f.tell() - 4096))
+                tail = f.read().decode("utf-8", "replace")
+        except (OSError, TypeError):
+            pass
+        for line in reversed(tail.splitlines()):
+            if "issue #29" in line:
+                return ("The engine stopped itself because it had stopped making progress - a hang it caught. Its log "
+                        "line: " + line.strip() + " - please report it at github.com/Niko1221/Strata/issues.")
+        return ("The usual cause is running out of RAM: Linux then ends the biggest program (check: sudo dmesg | "
+                "grep -i -E 'killed process|out of memory'); Windows slows down instead. Close other programs or use a "
+                "smaller model (Q2_0 / IQ2_XS).")
+
+    def alive(self) -> bool:
+        return not getattr(self, "ended", False) and self.proc.poll() is None
+
+    def exit_code(self):
+        try:
+            return self.proc.wait(timeout=5)
+        except subprocess.TimeoutExpired:
+            return None
+
+    def restart(self):
+        """Start the engine again (the same command) after it died; the new process has its own line queue."""
+        try:
+            self.proc.kill()
+        except OSError:
+            pass
+        info = dict(self.info)
+        self.ended = False
+        self.__init__(*self.spawn)
+        self.info = {**info, **self.info}
+
    def _parse_done(self, line):
        f = line.split()
        self.last = {"generated": int(f[1]), "prompt_tokens": int(f[2]), "prompt_ms": float(f[3]),
                     "decode_ms": float(f[4]), "finish": f[5]}
+        if len(f) >= 9:                                   # the conversation cache's fields (engine 0.1.3+)
+            self.last.update(drafts_accepted=int(f[6]), drafts_offered=int(f[7]), reused=int(f[8]))
+
+    @staticmethod
+    def sampling_keys(sampling: dict) -> str:
+        keys = ""
+        t = sampling.get("temperature")
+        if isinstance(t, (int, float)) and float(t) > 0.0:
+            keys += f" temperature={float(t)!r}"
+        tp = sampling.get("top_p")
+        if isinstance(tp, (int, float)) and float(tp) < 1.0:
+            keys += f" top_p={float(tp)!r}"
+        tk = sampling.get("top_k")
+        if isinstance(tk, int) and 1 <= tk <= 64:
+            keys += f" top_k={tk}"        # the engine's sampled path takes 1..64; outside it keeps its 20
+        mp = sampling.get("min_p")
+        if isinstance(mp, (int, float)) and 0.0 < float(mp) <= 1.0:
+            keys += f" min_p={float(mp)!r}"
+        rp = sampling.get("repetition_penalty")
+        rp_on = isinstance(rp, (int, float)) and float(rp) != 1.0
+        pf = sampling.get("frequency_penalty")
+        pf_on = isinstance(pf, (int, float)) and float(pf) != 0.0
+        pp = sampling.get("presence_penalty")
+        pp_on = isinstance(pp, (int, float)) and float(pp) != 0.0
+        if rp_on:
+            keys += f" penalty_repeat={float(rp)!r}"
+        if pf_on:
+            keys += f" penalty_freq={float(pf)!r}"
+        if pp_on:
+            keys += f" penalty_present={float(pp)!r}"
+        if rp_on or pf_on or pp_on:
+            # a penalty without a window counts over nothing: the engine's default is the last 64 tokens
+            pln = sampling.get("penalty_last_n")
+            if isinstance(pln, int) and not isinstance(pln, bool) and pln > 0:
+                keys += f" penalty_last_n={pln}"
+            else:
+                keys += " penalty_last_n=64"
+        seed = sampling.get("seed")
+        if isinstance(seed, int) and seed > 0:
+            keys += f" seed={seed}"
+        return keys + StrataEngine.projection_key(sampling)
+
+    @staticmethod
+    def projection_key(sampling: dict) -> str:
+        """`cvec=0|1`: the experimental-speed-projection control vector for this request, when the engine was
+        started with one (--control-vector-scaled; an engine without one ignores the key).  Absent = on."""
+        on = sampling.get("experimental_speed_projection")
+        return f" cvec={int(on)}" if isinstance(on, bool) else ""

    def generate(self, ids, max_new, sampling, cancel, embeddings=None):
        """Yields token ids, and None as a heartbeat every 10 s while the engine is quiet (reading a long prompt):
        the HTTP layer turns it into an SSE comment, which keeps clients' watchdogs calm and notices a client that
        has gone.  A consumer that stops early (or `cancel`) makes the engine STOP, so it does not run to max_new."""
-        head = f"GENI {int(max_new)} {embeddings}" if embeddings else f"GEN {int(max_new)}"
-        self.proc.stdin.write(f"{head} {','.join(str(int(t)) for t in ids)}\n")
-        self.proc.stdin.flush()
+        self.progress = None
+        head = f"GENI {int(max_new)}{self.projection_key(sampling or {})} {embeddings}" if embeddings else \
+            f"GEN {int(max_new)}{self.sampling_keys(sampling or {}) if not embeddings else ''}"
+        try:
+            self.proc.stdin.write(f"{head} {','.join(str(int(t)) for t in ids)}\n")
+            self.proc.stdin.flush()
+        except OSError:                                  # the pipe is gone: the engine died (not the client)
+            raise EngineDied(f"the engine stopped unexpectedly (exit code {self.exit_code()})") from None
        done = False
        try:
            while True:
@@ -122,12 +238,15 @@ class StrataEngine:
                    continue
                if line is None:
                    done = True
-                    raise RuntimeError("the engine process ended")
+                    raise EngineDied(f"the engine stopped unexpectedly (exit code {self.exit_code()})")
                if line.startswith("T "):
                    if cancel.is_set():
                        return
                    yield int(line[2:])
-                elif line.startswith("PP "):             # prompt progress, one per chunk: also a heartbeat (the
+                elif line.startswith("PP "):
+                    f = line.split()
+                    if len(f) >= 3 and f[1].isdigit() and f[2].isdigit():
+                        self.progress = (int(f[1]), int(f[2]))             # prompt progress, one per chunk: also a heartbeat (the
                    if cancel.is_set():                   # lines reset the 10 s wait, so without this a long prompt
                        return                            # would send no keep-alives at all)
                    yield None
@@ -311,17 +430,107 @@ class Detokenizer:

 class Service:
    def __init__(self, engine: Engine, tokenizer, template: ChatTemplate, model_name: str = "qwen3.8-flash-next",
-                 vision: Vision | None = None):
+                 vision: Vision | None = None, sampling_defaults: dict | None = None,
+                 fit_max_tokens: bool = False):
        self.engine, self.tok, self.template, self.model, self.vision = engine, tokenizer, template, model_name, vision
+        self.fit_max_tokens = fit_max_tokens          # --fit-max-tokens: clamp the output cap instead of 400
+        self.sampling_defaults = dict(sampling_defaults or {})   # the run config's `sampling` block
+        self.shared = {}                              # the web app's Chat settings for every client (POST /settings)
+        self.shared_path = None                       # where they are kept between starts (next to the config)
        self.fifo = threading.Lock()
        self.embeddings = threading.local()           # the current request's image embeddings file (GENI)
        self.api_key = ""                              # when set, /v1/* needs it (Bearer or x-api-key)
        self.status = {"busy": False, "queued": 0}      # GET /status: what the model is doing right now
+        self.history = collections.deque(maxlen=500)    # the last finished requests, newest last (GET /metrics)
+        # since the server started (the Monitor's totals, issue #35)
+        self.totals = {"since": time.time(), "requests": 0, "prompt_tokens": 0, "reused": 0, "output_tokens": 0,
+                       "prompt_ms": 0.0, "decode_ms": 0.0}
        self.status_lock = threading.Lock()
        self.stop_ids = set(tokenizer.encode(IM_END, parse_special=True) +
                            tokenizer.encode("<|endoftext|>", parse_special=True))

-    def prepare(self, messages, tools, kwargs, max_new):
+    def set_shared(self, defaults) -> dict:
+        """The Chat settings every client gets for what it leaves out; {} / None = clients use their own again."""
+        self.shared = clean_shared_defaults(defaults)
+        if self.shared_path:
+            try:
+                if self.shared:
+                    Path(self.shared_path).write_text(json.dumps(self.shared, indent=1), encoding="utf-8")
+                else:
+                    Path(self.shared_path).unlink(missing_ok=True)
+            except OSError as e:
+                print(f"[strata] could not save the shared settings: {e}", flush=True)
+        return self.shared
+
+    def with_shared(self, req: dict, api: str) -> dict:
+        """The request with the shared thinking level and max tokens filled in where it has none of its own."""
+        s = self.shared
+        if not s:
+            return req
+        req = dict(req)
+        if "max_tokens" in s and not req.get("max_tokens") and not req.get("max_completion_tokens"):
+            req["max_tokens"] = s["max_tokens"]
+        effort = s.get("reasoning_effort")
+        if effort:
+            if api == "openai":
+                ctk = req.get("chat_template_kwargs") if isinstance(req.get("chat_template_kwargs"), dict) else {}
+                if not req.get("reasoning_effort") and not req.get("reasoning") and \
+                        "enable_thinking" not in ctk and "reasoning_effort" not in ctk:
+                    req["reasoning_effort"] = effort
+            elif not req.get("thinking") and not req.get("output_config"):
+                if effort == "none":
+                    req["thinking"] = {"type": "disabled"}
+                else:
+                    req["output_config"] = {"effort": effort}
+        return req
+
+    def start_telemetry(self):
+        """The hardware sampler behind GET /metrics (serve/telemetry.py), recording this server's tok/s too."""
+        if getattr(self, "telemetry", None) is None:
+            from serve.telemetry import Telemetry
+            self.telemetry = Telemetry(extra=lambda: {"tok_s": self._tok_s()})
+
+    def _tok_s(self):
+        with self.status_lock:
+            s = dict(self.status)
+        if not s.get("busy") or not s.get("first_token"):
+            return 0.0
+        return s["generated"] / max(1e-6, time.time() - s["first_token"])
+
+    def metrics(self, all_requests=False) -> dict:
+        """GET /metrics: what the Monitor tab shows - the engine's facts, what it is doing, the last requests, and
+        the hardware (with a minute of history per series)."""
+        with self.status_lock:
+            s = dict(self.status)
+            hist = list(self.history)
+            totals = dict(self.totals)
+        now = time.time()
+        progress = getattr(self.engine, "progress", None)
+        if s.get("busy") and s.get("first_token") is None:
+            state = "reading"
+        elif s.get("busy"):
+            state = "generating"
+        else:
+            state = "idle"
+        live = {"state": state, "queued": s.get("queued", 0), "phase": s.get("phase") if s.get("busy") else None,
+                "prompt_tokens": s.get("prompt_tokens") if s.get("busy") else None,
+                "prompt_read": None, "prompt_total": None, "generated": s.get("generated") if s.get("busy") else None,
+                "max_tokens": s.get("max_tokens") if s.get("busy") else None,
+                "elapsed_s": round(now - s["started"], 1) if s.get("busy") and s.get("started") else None,
+                "tok_s": round(self._tok_s(), 1) if state == "generating" else None}
+        if state == "reading" and progress:
+            live["prompt_read"], live["prompt_total"] = progress
+        engine = {"model": self.model, "max_context": self.engine.max_context, "images": self.vision is not None,
+                  **dict(getattr(self.engine, "info", {}) or {})}
+        tel = self.telemetry.snapshot() if getattr(self, "telemetry", None) else {"now": {}, "history": {}, "static": {}}
+        return {"engine": engine, "live": live, "requests": hist[::-1][:None if all_requests else 12],
+                "requests_kept": len(hist), "totals": totals, "hardware": tel["now"],
+                "hardware_static":
+                tel["static"], "history": tel["history"], "time": now}
+
+    def prepare(self, messages, tools, kwargs, max_new=None):
+        """-> (ids, thinking, max_new). An unset or non-positive max_new (some clients send -1) means "unlimited":
+        the rest of the context."""
        prompt = self.template.render(messages, tools=tools, **kwargs)
        ids = self.tok.encode(prompt, parse_special=True)
        self.embeddings.path = None
@@ -352,10 +561,18 @@ class Service:
                for path, _ in encoded:
                    f.write(path.read_bytes())
            self.embeddings.path = combined
-        if len(ids) + max_new > self.engine.max_context:
-            raise ValueError(f"prompt ({len(ids)} tokens) + max tokens ({max_new}) exceeds the context "
-                             f"({self.engine.max_context}); requests are never truncated")
-        return ids, kwargs.get("enable_thinking", True) is not False
+        room = self.engine.max_context - CTX_SLACK - len(ids)
+        if max_new is None or max_new <= 0 or (self.fit_max_tokens and room < 1):
+            if room < 1:
+                raise ValueError(f"prompt ({len(ids)} tokens) leaves no room to answer in the context "
+                                 f"({self.engine.max_context}); requests are never truncated")
+            max_new = room
+        elif max_new > room:
+            if not self.fit_max_tokens:
+                raise ValueError(f"prompt ({len(ids)} tokens) + max tokens ({max_new}) exceeds the context "
+                                 f"({self.engine.max_context}); requests are never truncated")
+            max_new = max(1, room)          # --fit-max-tokens: a shorter completion beats a 400
+        return ids, kwargs.get("enable_thinking", True) is not False, max_new

    def _note(self, n, evs):
        with self.status_lock:
@@ -383,7 +600,9 @@ class Service:
            s = dict(self.status)
        el = now - s.get("started", now)
        if s.get("first_token") is None:
-            print(f"[strata] reading the prompt: {s.get('prompt_tokens', 0)} tokens, {el:.0f} s so far", flush=True)
+            pr = getattr(self.engine, "progress", None)   # (position reached, prompt tokens): a reused prefix counts
+            done = f"{pr[0]:,} of {pr[1]:,}" if pr and pr[1] else f"{s.get('prompt_tokens', 0):,}"   # as read (#29)
+            print(f"[strata] reading the prompt: {done} tokens, {el:.0f} s so far", flush=True)
        else:
            rate = s["generated"] / max(1e-6, now - s["first_token"])
            print(f"[strata] {s['phase']}: {s['generated']} of max {s.get('max_tokens')} tokens, {rate:.1f} tok/s, "
@@ -392,6 +611,10 @@ class Service:

    def run(self, ids, thinking, tools, max_new, sampling, cancel) -> Iterator[tuple[str, object]]:
        """Yields ("event", Event) as text arrives, then ("done", {"finish": .., "completion_tokens": ..})."""
+        defaults = {**self.sampling_defaults, **self.shared}   # the config's, then the Chat settings shared with apps
+        if defaults:                   # the request's own fields win (explicit 0 stays greedy)
+            req_values = {k: v for k, v in (sampling or {}).items() if v is not None}
+            sampling = {**defaults, **req_values}
        parser = OutputParser(thinking=thinking, tools=tools, stream_tools=True)
        detok, n, finish = Detokenizer(self.tok), 0, "length"
        raw_ids = []                                    # every generated id (STRATA_DEBUG: dump raw model text)
@@ -402,6 +625,14 @@ class Service:
            with self.fifo:
                with self.status_lock:
                    self.status["queued"] -= 1
+                if hasattr(self.engine, "alive") and not self.engine.alive():
+                    # issue #27: it died in an earlier request - start it again instead of failing every request
+                    code = self.engine.exit_code() if hasattr(self.engine, "exit_code") else None
+                    print(f"[strata] the engine had stopped (exit code {code}); starting it again "
+                          "(a minute or two) ...", flush=True)
+                    self.engine.restart()
+                    print("[strata] the engine is running again", flush=True)
+                with self.status_lock:
                    self.status.update(busy=True, phase="reading the prompt", prompt_tokens=len(ids), generated=0,
                                       started=time.time(), first_token=None, tool=None, tail="", max_tokens=max_new)
                last_print = time.time()
@@ -426,6 +657,17 @@ class Service:
                            yield "event", ev
                    if cancel.is_set():
                        finish = "cancel"
+                except EngineDied as e:
+                    finish = "error"
+                    note = self.engine.death_note() if hasattr(self.engine, "death_note") else ""
+                    print(f"[strata] {e}. {note} The next request starts the engine again."
+                          f"{' Its log: ' + self.engine.log_path if getattr(self.engine, 'log_path', None) else ''}",
+                          flush=True)
+                    raise
+                except ValueError as e:                 # the engine's ERR line (it may have ended after it)
+                    finish = "error"
+                    print(f"[strata] the engine reported an error: {e}", flush=True)
+                    raise
                finally:
                    gen.close()                         # STOP+drain to THIS request's DONE while still holding the
                    #                                     fifo, so a stop-token break can't leave the shared engine
@@ -438,6 +680,24 @@ class Service:
                Path(emb).unlink(missing_ok=True)
            with self.status_lock:
                if self.status.get("busy"):
+                    last = dict(getattr(self.engine, "last", {}) or {})
+                    started = self.status.get("started", time.time())
+                    loaded = str((getattr(self.engine, "info", {}) or {}).get("cvec", 0)) not in ("0", "", "None")
+                    self.history.append({
+                        "projection": (sampling or {}).get("experimental_speed_projection") is not False
+                        if loaded else None,
+                        "time": started, "duration_s": round(time.time() - started, 1), "finish": finish,
+                        "prompt_tokens": len(ids), "reused": last.get("reused"), "output_tokens": n,
+                        "prompt_ms": last.get("prompt_ms"), "decode_ms": last.get("decode_ms"),
+                        "decode_tok_s": round(last["generated"] / (last["decode_ms"] / 1000), 1)
+                        if n and last.get("generated") and last.get("decode_ms") else None})
+                    t = self.totals
+                    t["requests"] += 1
+                    t["prompt_tokens"] += len(ids)
+                    t["reused"] += last.get("reused") or 0
+                    t["output_tokens"] += n
+                    t["prompt_ms"] += last.get("prompt_ms") or 0.0
+                    t["decode_ms"] += last.get("decode_ms") or 0.0
                    now = time.time()
                    el = now - self.status.get("started", now)
                    ft = self.status.get("first_token")
@@ -654,8 +914,50 @@ def make_handler(svc: Service):

        def do_GET(self):
            path = self.path.split("?")[0].rstrip("/")
+            if path.startswith("/fonts/"):
+                # the web app's font (Outfit, OFL: serve/web/fonts); the page falls back to the system font
+                name = path[len("/fonts/"):]
+                f = ROOT / "serve" / "web" / "fonts" / name
+                if "/" in name or "\\" in name or not name.endswith(".woff2") or not f.is_file():
+                    self._json(404, {"error": {"message": "not found"}})
+                    return
+                body = f.read_bytes()
+                self.send_response(200)
+                self.send_header("Content-Type", "font/woff2")
+                self.send_header("Cache-Control", "max-age=86400")
+                self.send_header("Content-Length", str(len(body)))
+                self.end_headers()
+                self.wfile.write(body)
+                return
+            if path.startswith("/web/"):
+                # the web app's own files (serve/web): styles, script, icon sprite - same origin, no CDN
+                name = path[len("/web/"):]
+                types = {".css": "text/css; charset=utf-8", ".js": "text/javascript; charset=utf-8",
+                         ".svg": "image/svg+xml"}
+                f = ROOT / "serve" / "web" / name
+                ext = os.path.splitext(name)[1]
+                if "/" in name or "\\" in name or ext not in types or not f.is_file():
+                    self._json(404, {"error": {"message": "not found"}})
+                    return
+                body = f.read_bytes()
+                self.send_response(200)
+                self.send_header("Content-Type", types[ext])
+                self.send_header("Cache-Control", "no-cache")
+                self.send_header("Content-Length", str(len(body)))
+                self.end_headers()
+                self.wfile.write(body)
+                return
+            if path == "/metrics":
+                if self._authorized():
+                    # the last 12 requests; `?requests=all` every one kept (the Monitor's "Show all", issue #35)
+                    self._json(200, svc.metrics(all_requests="requests=all" in self.path))
+                return
+            if path == "/settings":
+                if self._authorized():
+                    self._json(200, {"shared": bool(svc.shared), "defaults": svc.shared})
+                return
            if path == "":
-                body = (ROOT / "serve" / "index.html").read_bytes()
+                body = (ROOT / "serve" / "web" / "index.html").read_bytes()
                self.send_response(200)
                self.send_header("Content-Type", "text/html; charset=utf-8")
                self.send_header("Content-Length", str(len(body)))
@@ -684,6 +986,9 @@ def make_handler(svc: Service):
        def do_POST(self):
            if not self._authorized():
                return
+            if self.path.rstrip("/") == "/settings":
+                self._settings()
+                return
            try:
                req = json.loads(self.rfile.read(int(self.headers.get("Content-Length", 0))) or b"{}")
                if self.path.rstrip("/") == "/v1/chat/completions":
@@ -694,6 +999,30 @@ def make_handler(svc: Service):
                    self._json(404, {"error": {"message": "not found"}})
            except ValueError as e:
                self._json(400, {"error": {"type": "invalid_request_error", "message": str(e)}})
+            except EngineDied as e:                          # before the answer started (not streamed)
+                self._json(503, {"error": {"type": "server_error", "message": f"{e}; the next request restarts it"}})
+
+        def _settings(self):
+            # They change what every client gets, so only the app's own page may set them: JSON only (a form or a
+            # "simple" cross-site request can't send it without a CORS preflight, which this server never grants),
+            # and no foreign Origin
+            body = self.rfile.read(int(self.headers.get("Content-Length", 0)))
+            if not self.headers.get("Content-Type", "").startswith("application/json"):
+                self._json(415, {"error": {"message": "send application/json"}})
+                return
+            origin = self.headers.get("Origin")
+            if origin and origin.split("://", 1)[-1] != self.headers.get("Host", ""):
+                self._json(403, {"error": {"message": "settings can only be changed from Strata's own page"}})
+                return
+            try:
+                req = json.loads(body or b"{}")
+                shared = svc.set_shared(req.get("defaults") if isinstance(req, dict) else None)
+            except ValueError as e:
+                self._json(400, {"error": {"type": "invalid_request_error", "message": str(e)}})
+                return
+            print("[strata] other apps now use the Chat settings: " + ", ".join(f"{k}={v}" for k, v in shared.items())
+                  if shared else "[strata] other apps use their own settings again", flush=True)
+            self._json(200, {"shared": bool(shared), "defaults": shared})

        def _sse(self):
            self.send_response(200)
@@ -702,11 +1031,10 @@ def make_handler(svc: Service):
            self.end_headers()

        def _openai(self, req):
+            req = svc.with_shared(req, "openai")
            messages, tools, kw = openai_to_messages(req)
-            max_new = int(req.get("max_completion_tokens") or req.get("max_tokens") or 1024)
-            if max_new <= 0:                             # a non-positive budget (some clients send -1) means "unset"
-                max_new = 1024
-            ids, thinking = svc.prepare(messages, tools, kw, max_new)
+            max_new = int(req.get("max_completion_tokens") or req.get("max_tokens") or 0)   # 0/-1: the rest of the context
+            ids, thinking, max_new = svc.prepare(messages, tools, kw, max_new)
            _debug_req("openai", req, messages, tools, max_new, thinking, len(ids))
            cancel = threading.Event()
            chunks = openai_chunks(svc, req, ids, thinking, tools, max_new, cancel)
@@ -724,13 +1052,18 @@ def make_handler(svc: Service):
            except OSError:
                cancel.set()                                 # client went away: stop the engine
                chunks.close()
+            except EngineDied as e:                          # mid-stream: say so, then end the stream properly
+                err = {"error": {"type": "server_error", "message": f"{e}; the next request restarts it"}}
+                self.wfile.write(b"data: " + json.dumps(err).encode() + b"\n\ndata: [DONE]\n\n")
+            except ValueError as e:                          # the engine's ERR after the stream started: the
+                err = {"error": {"type": "server_error", "message": str(e)}}   # headers are sent, so no 400 now
+                self.wfile.write(b"data: " + json.dumps(err).encode() + b"\n\ndata: [DONE]\n\n")

        def _anthropic(self, req):
+            req = svc.with_shared(req, "anthropic")
            messages, tools, kw = anthropic_to_messages(req)
-            max_new = int(req.get("max_tokens") or 1024)
-            if max_new <= 0:                             # a non-positive budget (some clients send -1) means "unset"
-                max_new = 1024
-            ids, thinking = svc.prepare(messages, tools, kw, max_new)
+            max_new = int(req.get("max_tokens") or 0)                  # 0/-1: the rest of the context
+            ids, thinking, max_new = svc.prepare(messages, tools, kw, max_new)
            _debug_req("anthropic", req, messages, tools, max_new, thinking, len(ids))
            cancel = threading.Event()
            events = anthropic_events(svc, req, ids, thinking, tools, max_new, cancel)
@@ -749,6 +1082,12 @@ def make_handler(svc: Service):
            except OSError:
                cancel.set()
                events.close()
+            except EngineDied as e:                          # mid-stream: Anthropic's error event
+                err = {"type": "error", "error": {"type": "api_error", "message": f"{e}; the next request restarts it"}}
+                self.wfile.write(b"event: error\ndata: " + json.dumps(err).encode() + b"\n\n")
+            except ValueError as e:                          # the engine's ERR after the stream started
+                err = {"type": "error", "error": {"type": "api_error", "message": str(e)}}
+                self.wfile.write(b"event: error\ndata: " + json.dumps(err).encode() + b"\n\n")

    return Handler

@@ -759,27 +1098,171 @@ class Server(ThreadingHTTPServer):
    allow_reuse_address = os.name != "nt"


+def warn_tight_ram(arena_mib) -> None:
+    """The model's experts live in RAM (INFO arena_mib, engine 0.1.10+).  With less than ~6 GB left beside them for the
+    system, the engine and this server, Linux ends the engine mid-answer when memory runs out (issue #27) and Windows
+    pages to disk; say so at start instead of after a lost answer."""
+    if not isinstance(arena_mib, int) or arena_mib <= 0:
+        return
+    try:
+        import psutil
+        total = psutil.virtual_memory().total
+    except Exception:  # noqa: BLE001 - psutil is optional here
+        return
+    left = total / 2**30 - arena_mib / 1024
+    if left < 6:
+        print(f"[strata] WARNING: RAM is tight - the model's experts take {arena_mib / 1024:.1f} GB of this PC's "
+              f"{total / 2**30:.0f} GB, leaving {left:.1f} GB for everything else. "
+              + ("Linux may stop the engine in the middle of an answer. " if os.name != "nt" else
+                 "Windows will slow down (paging to disk). ")
+              + "Close other programs, or run START-HERE --setup and pick a smaller size (Q2_0 / IQ2_XS).", flush=True)
+
+
+def lan_addresses() -> list[str]:
+    """This PC's IPv4 addresses on its networks (what another device types in), without loopback/link-local."""
+    import socket
+    first, ips = None, set()
+    try:                                                # the address of the default route; sends nothing (UDP)
+        with socket.socket(socket.AF_INET, socket.SOCK_DGRAM) as s:
+            s.connect(("10.255.255.255", 1))
+            first = s.getsockname()[0]
+    except OSError:
+        pass
+    try:
+        for info in socket.getaddrinfo(socket.gethostname(), None, socket.AF_INET):
+            ips.add(info[4][0])
+    except OSError:
+        pass
+    ok = lambda ip: ip and not ip.startswith(("127.", "169.254.", "0."))
+    return ([first] if ok(first) else []) + sorted(ip for ip in ips if ok(ip) and ip != first)
+
+
 def serve(svc: Service, host="127.0.0.1", port=8095) -> ThreadingHTTPServer:
+    svc.start_telemetry()
    httpd = Server((host, port), make_handler(svc))
    threading.Thread(target=httpd.serve_forever, daemon=True).start()
    return httpd


+SHARED_KEYS = ("reasoning_effort", "temperature", "top_p", "top_k", "seed", "max_tokens", "experimental_speed_projection")
+
+
+def clean_shared_defaults(d) -> dict:
+    """The Chat settings other apps get (POST /settings): only known keys, each checked; ValueError names a bad one."""
+    if d is None:
+        return {}
+    if not isinstance(d, dict):
+        raise ValueError("defaults must be an object")
+    out = {}
+    for key, value in d.items():
+        if value is None or value == "":
+            continue
+        number = isinstance(value, (int, float)) and not isinstance(value, bool)
+        if key == "reasoning_effort":
+            if value not in ("none", "low", "medium", "high"):
+                raise ValueError("reasoning_effort: none, low, medium or high")
+        elif key == "temperature":
+            if not number or not 0 <= value <= 2:
+                raise ValueError("temperature: 0..2")
+        elif key == "top_p":
+            if not number or not 0 < value <= 1:
+                raise ValueError("top_p: 0 < top_p <= 1")
+        elif key == "top_k":
+            if not number or value != int(value) or not 1 <= value <= 64:
+                raise ValueError("top_k: an integer 1..64")
+            value = int(value)
+        elif key in ("seed", "max_tokens"):
+            if not number or value != int(value) or value <= 0:
+                raise ValueError(f"{key}: a positive integer")
+            value = int(value)
+        elif key == "experimental_speed_projection":
+            if not isinstance(value, bool):
+                raise ValueError("experimental_speed_projection: true or false")
+        else:
+            raise ValueError(f"unknown setting {key!r}")
+        out[key] = float(value) if key in ("temperature", "top_p") else value
+    return out
+
+
+def sampling_defaults_from_config(cfg: dict) -> dict:
+    """The run config's optional `sampling` block: defaults for the sampling fields a request leaves out, so
+    a plain client gets configured sampling instead of greedy.  Supported: temperature, top_p, top_k, min_p,
+    presence_penalty, repetition_penalty, frequency_penalty, penalty_last_n, seed.  The request's own fields
+    always win - an explicit temperature=0 still means greedy, a field set to null falls back to the default.
+    A bad value refuses to start the server (a typo'd config should not quietly change sampling); unknown keys
+    are named at startup and ignored."""
+    out = {}
+    for key, value in (cfg.get("sampling") or {}).items():
+        if value is None:
+            continue
+        number = isinstance(value, (int, float)) and not isinstance(value, bool)
+        if key == "temperature":
+            if not number or value < 0:
+                raise SystemExit(f"[strata] config sampling.temperature={value!r}: expected a number >= 0 (0 = greedy)")
+            out[key] = float(value)
+        elif key == "top_p":
+            if not number or not 0 < value <= 1:
+                raise SystemExit(f"[strata] config sampling.top_p={value!r}: expected 0 < top_p <= 1")
+            out[key] = float(value)
+        elif key == "min_p":
+            if not number or not 0 <= value <= 1:
+                raise SystemExit(f"[strata] config sampling.min_p={value!r}: expected 0 <= min_p <= 1")
+            out[key] = float(value)
+        elif key == "top_k":
+            if not number or value != int(value) or not 1 <= value <= 64:
+                raise SystemExit(f"[strata] config sampling.top_k={value!r}: the sampled path takes an integer 1..64")
+            out[key] = int(value)
+        elif key == "presence_penalty":
+            if not number or value < 0:
+                raise SystemExit(f"[strata] config sampling.presence_penalty={value!r}: expected a number >= 0")
+            out[key] = float(value)
+        elif key == "frequency_penalty":
+            if not number or value < 0:
+                raise SystemExit(f"[strata] config sampling.frequency_penalty={value!r}: expected a number >= 0")
+            out[key] = float(value)
+        elif key == "repetition_penalty":
+            if not number or value <= 0:
+                raise SystemExit(f"[strata] config sampling.repetition_penalty={value!r}: expected a number > 0 (1 = off)")
+            out[key] = float(value)
+        elif key == "penalty_last_n":
+            if not number or value != int(value) or value < 0:
+                raise SystemExit(f"[strata] config sampling.penalty_last_n={value!r}: expected a non-negative integer")
+            out[key] = int(value)
+        elif key == "seed":
+            if not number or value != int(value) or value <= 0:
+                raise SystemExit(f"[strata] config sampling.seed={value!r}: expected a positive integer")
+            out[key] = int(value)
+        elif key == "experimental_speed_projection":
+            if not isinstance(value, bool):
+                raise SystemExit(f"[strata] config sampling.experimental_speed_projection={value!r}: expected true or "
+                                 "false (the default for requests that leave it out, when the engine has the vector)")
+            out[key] = value
+        else:
+            print(f"[strata] config sampling.{key}={value!r}: unknown key, ignored", flush=True)
+    return out
+
+
 def main() -> int:
    ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
    ap.add_argument("--engine", choices=["mock", "strata"], default="mock")
    ap.add_argument("--config", help="strata engine config (JSON: exe, args, cwd, tokenizer, model_name), "
                                     "written by setup.py")
-    ap.add_argument("--host", default="127.0.0.1")
+    ap.add_argument("--host", default=None,
+                    help="the address to listen on: 127.0.0.1 = this PC only (the default), 0.0.0.0 = also other devices "
+                         "on your network (set an API key); also \"host\" in the config")
    ap.add_argument("--script", default="Thinking about it.</think>\n\nHello from the mock engine.")
    ap.add_argument("--port", type=int, default=8095)
    ap.add_argument("--tokenizer", default=str(ROOT / "pack/full/tokenizer"),
                    help="pack tokenizer directory (falls back to a byte tokenizer if absent)")
    ap.add_argument("--open", action="store_true", help="open the local page in the browser once the model is ready")
+    ap.add_argument("--fit-max-tokens", action="store_true",
+                    help="clamp max_tokens to the remaining context instead of rejecting the request "
+                         "(default: reject with 400, like llama.cpp; also \"fit_max_tokens\": true in the config)")
    ap.add_argument("--api-key", default=os.environ.get("STRATA_API_KEY", ""),
                    help="require this key on /v1/* (Authorization: Bearer ... or x-api-key); also $STRATA_API_KEY")
    a = ap.parse_args()
    cfg = json.loads(Path(a.config).read_text(encoding="utf-8-sig")) if a.config else {}   # Notepad adds a BOM
+    a.host = a.host or cfg.get("host") or "127.0.0.1"   # issue #26: the run scripts pass no --host, the config can
    try:                                                # before the minutes of loading: is the port free?
        Server((a.host, a.port), BaseHTTPRequestHandler).server_close()
    except OSError:
@@ -805,24 +1288,56 @@ def main() -> int:
            ap.error("--engine strata needs --config")
        vision = None
        env = child_env(cfg)
+        sampling_defaults = sampling_defaults_from_config(cfg)
+        if sampling_defaults:
+            pretty = ", ".join(f"{k}={v}" for k, v in sampling_defaults.items())
+            print(f"[strata] sampling defaults from the config: {pretty}", flush=True)
        if cfg.get("vision"):
            print("loading the vision encoder ...", flush=True)
            vision = Vision(cfg["vision"], log=open(cfg["log"], "a", encoding="utf-8") if cfg.get("log") else None,
                            env=env)
        print("loading the model (the first start takes a minute or two) ...", flush=True)
        engine = StrataEngine(cfg["exe"], cfg["args"], cwd=cfg.get("cwd"), log=cfg.get("log"), env=env)
+        warn_tight_ram(engine.info.get("arena_mib"))
    else:
-        engine, vision = MockEngine(tok, a.script), None
+        engine, vision, sampling_defaults = MockEngine(tok, a.script), None, {}
    # the model's own chat template (exported with its tokenizer), else the original model's
    tpl = tpath / "chat_template.jinja"
    svc = Service(engine, tok, ChatTemplate(tpl if tpl.exists() else ROOT / "serve/chat_template.jinja"),
-                  model_name=cfg.get("model_name", "qwen3.8-flash-next"), vision=vision)
+                  model_name=cfg.get("model_name", "qwen3.8-flash-next"), vision=vision,
+                  sampling_defaults=sampling_defaults,
+                  fit_max_tokens=a.fit_max_tokens or cfg.get("fit_max_tokens") is True)
    svc.api_key = a.api_key or cfg.get("api_key", "")
+    if a.config:                                        # the Chat settings shared with other apps, from last time
+        svc.shared_path = str(Path(a.config).with_suffix("")) + ".shared-settings.json"
+        try:
+            svc.shared = clean_shared_defaults(json.loads(Path(svc.shared_path).read_text(encoding="utf-8")))
+            if svc.shared:
+                print("[strata] other apps use the Chat settings: " +
+                      ", ".join(f"{k}={v}" for k, v in svc.shared.items()), flush=True)
+        except (OSError, ValueError):
+            svc.shared = {}
    httpd = serve(svc, host=a.host, port=a.port)
-    print(f"ready: http://{a.host}:{a.port}/v1  (OpenAI: /v1/chat/completions, Anthropic: /v1/messages, "
+    here = "127.0.0.1" if a.host in ("0.0.0.0", "", "::") else a.host
+    print(f"ready: http://{here}:{a.port}/v1  (OpenAI: /v1/chat/completions, Anthropic: /v1/messages, "
          f"context {engine.max_context} tokens{', images on' if vision else ''}"
          f"{', API key required' if svc.api_key else ''})", flush=True)
-    print(f"       open http://{a.host}:{a.port}/ in a browser to chat; close this window to stop the model", flush=True)
+    print(f"       open http://{here}:{a.port}/ in a browser to chat; close this window to stop the model", flush=True)
+    if a.host not in ("127.0.0.1", "localhost", "::1"):
+        # issue #26: reachable from other devices - say at which address, and what can still block it
+        ips = lan_addresses()
+        for ip in ips:
+            print(f"       from other devices: http://{ip}:{a.port}/   (API: http://{ip}:{a.port}/v1)", flush=True)
+        if not ips:
+            print("       from other devices: http://<this PC's IP address>:" + str(a.port) + "/", flush=True)
+        if not svc.api_key:
+            print("       WARNING: no API key - anyone on your network can use this model. Add \"api_key\": \"...\" "
+                  "to the config (clients send it as their API key; the web page asks for it)", flush=True)
+        if os.name == "nt":
+            print("       nothing arrives? Windows Firewall blocks it until allowed: accept its prompt for Python, or run "
+                  "in an admin PowerShell:\n         New-NetFirewallRule -DisplayName \"Strata " + str(a.port) + "\" "
+                  "-Direction Inbound -Protocol TCP -LocalPort " + str(a.port) + " -Action Allow -Profile Private\n"
+                  "       (and set this network to Private in Windows' network settings)", flush=True)
    if a.open:
        import webbrowser
        webbrowser.open(f"http://{'127.0.0.1' if a.host in ('0.0.0.0', '') else a.host}:{a.port}/")
@@ -0,0 +1,246 @@
+"""serve/telemetry.py - hardware readings for the web app's Monitor tab (idea from PR #22 by code-martin).
+
+A background thread samples once a second and keeps the last 60 readings of each series for the sparklines:
+- GPU: NVIDIA's own NVML library (nvml.dll / libnvidia-ml.so.1, installed with every driver) through ctypes, so no
+  pip package is needed: load, VRAM, temperature, power, PCIe link and throughput.
+- CPU, RAM, disk: `psutil` when it is installed (setup installs it); without it the CPU and RAM readings fall back to
+  the OS (Windows GlobalMemoryStatusEx / GetSystemTimes, Linux /proc) and the disk rate is absent.
+Anything that cannot be read is None; nothing here can stop the server.
+"""
+from __future__ import annotations
+
+import collections
+import ctypes
+import os
+import platform
+import sys
+import threading
+import time
+
+HISTORY = 60
+
+
+# ------------------------------------------------------------------------------------------------ NVML
+class _Nvml:
+    class Util(ctypes.Structure):
+        _fields_ = [("gpu", ctypes.c_uint), ("memory", ctypes.c_uint)]
+
+    class Mem(ctypes.Structure):
+        _fields_ = [("total", ctypes.c_ulonglong), ("free", ctypes.c_ulonglong), ("used", ctypes.c_ulonglong)]
+
+    def __init__(self, index=0):
+        self.lib = self.dev = None
+        names = ["nvml.dll", os.path.join(os.environ.get("ProgramFiles", r"C:\Program Files"),
+                                          "NVIDIA Corporation", "NVSMI", "nvml.dll")] if os.name == "nt" \
+            else ["libnvidia-ml.so.1", "libnvidia-ml.so"]
+        for n in names:
+            try:
+                self.lib = ctypes.CDLL(n)
+                break
+            except OSError:
+                continue
+        if self.lib is None:
+            return
+        try:
+            init = getattr(self.lib, "nvmlInit_v2", None) or self.lib.nvmlInit
+            if init() != 0:
+                self.lib = None
+                return
+            h = ctypes.c_void_p()
+            get = getattr(self.lib, "nvmlDeviceGetHandleByIndex_v2", None) or self.lib.nvmlDeviceGetHandleByIndex
+            if get(ctypes.c_uint(index), ctypes.byref(h)) != 0:
+                self.lib = None
+                return
+            self.dev = h
+        except (AttributeError, OSError):
+            self.lib = None
+
+    def ok(self):
+        return self.lib is not None and self.dev is not None
+
+    def _uint(self, fn, *args):
+        v = ctypes.c_uint()
+        try:
+            return v.value if getattr(self.lib, fn)(self.dev, *args, ctypes.byref(v)) == 0 else None
+        except (AttributeError, OSError):
+            return None
+
+    def name(self):
+        buf = ctypes.create_string_buffer(96)
+        try:
+            if self.lib.nvmlDeviceGetName(self.dev, buf, ctypes.c_uint(96)) == 0:
+                return buf.value.decode(errors="replace")
+        except (AttributeError, OSError):
+            pass
+        return None
+
+    def read(self):
+        out = {}
+        u = self.Util()
+        try:
+            if self.lib.nvmlDeviceGetUtilizationRates(self.dev, ctypes.byref(u)) == 0:
+                out["util"] = u.gpu
+        except (AttributeError, OSError):
+            pass
+        m = self.Mem()
+        try:
+            if self.lib.nvmlDeviceGetMemoryInfo(self.dev, ctypes.byref(m)) == 0:
+                out["mem_used"], out["mem_total"] = m.used, m.total
+        except (AttributeError, OSError):
+            pass
+        out["temp"] = self._uint("nvmlDeviceGetTemperature", ctypes.c_uint(0))          # NVML_TEMPERATURE_GPU
+        mw = self._uint("nvmlDeviceGetPowerUsage")
+        out["power"] = mw / 1000.0 if mw is not None else None
+        lim = self._uint("nvmlDeviceGetEnforcedPowerLimit")
+        out["power_limit"] = lim / 1000.0 if lim is not None else None
+        out["pcie_gen"] = self._uint("nvmlDeviceGetCurrPcieLinkGeneration")        # drops at idle (power saving)
+        out["pcie_gen_max"] = self._uint("nvmlDeviceGetMaxPcieLinkGeneration")
+        out["pcie_width"] = self._uint("nvmlDeviceGetCurrPcieLinkWidth")
+        rx = self._uint("nvmlDeviceGetPcieThroughput", ctypes.c_uint(1))                 # NVML_PCIE_UTIL_RX_BYTES, KB/s
+        tx = self._uint("nvmlDeviceGetPcieThroughput", ctypes.c_uint(0))
+        out["pcie_rx_mb"] = rx / 1024.0 if rx is not None else None
+        out["pcie_tx_mb"] = tx / 1024.0 if tx is not None else None
+        return out
+
+
+# ------------------------------------------------------------------------------------------------ CPU / RAM
+def _cpu_name():
+    if os.name == "nt":
+        try:
+            import winreg
+            k = winreg.OpenKey(winreg.HKEY_LOCAL_MACHINE, r"HARDWARE\DESCRIPTION\System\CentralProcessor\0")
+            return winreg.QueryValueEx(k, "ProcessorNameString")[0].strip()
+        except OSError:
+            pass
+    elif os.path.exists("/proc/cpuinfo"):
+        for line in open("/proc/cpuinfo", encoding="utf-8", errors="replace"):
+            if line.startswith("model name"):
+                return line.split(":", 1)[1].strip()
+    return platform.processor() or None
+
+
+class _CpuRamFallback:
+    """CPU load and RAM without psutil."""
+
+    def __init__(self):
+        self.prev = self._times()
+
+    def _times(self):
+        if os.name == "nt":
+            idle, kern, user = (ctypes.c_ulonglong() for _ in range(3))
+            if ctypes.windll.kernel32.GetSystemTimes(ctypes.byref(idle), ctypes.byref(kern), ctypes.byref(user)):
+                return idle.value, kern.value + user.value           # kernel time includes idle
+            return None
+        try:
+            f = [int(x) for x in open("/proc/stat").readline().split()[1:]]
+            return f[3] + f[4], sum(f)
+        except (OSError, ValueError):
+            return None
+
+    def cpu(self):
+        cur = self._times()
+        prev, self.prev = self.prev, cur
+        if not cur or not prev or cur[1] == prev[1]:
+            return None
+        return max(0.0, min(100.0, 100.0 * (1 - (cur[0] - prev[0]) / (cur[1] - prev[1]))))
+
+    @staticmethod
+    def ram():
+        if os.name == "nt":
+            class MS(ctypes.Structure):
+                _fields_ = [("dwLength", ctypes.c_ulong), ("dwMemoryLoad", ctypes.c_ulong),
+                            ("ullTotalPhys", ctypes.c_ulonglong), ("ullAvailPhys", ctypes.c_ulonglong),
+                            ("ullTotalPageFile", ctypes.c_ulonglong), ("ullAvailPageFile", ctypes.c_ulonglong),
+                            ("ullTotalVirtual", ctypes.c_ulonglong), ("ullAvailVirtual", ctypes.c_ulonglong),
+                            ("ullAvailExtendedVirtual", ctypes.c_ulonglong)]
+            m = MS()
+            m.dwLength = ctypes.sizeof(MS)
+            if ctypes.windll.kernel32.GlobalMemoryStatusEx(ctypes.byref(m)):
+                return m.ullTotalPhys - m.ullAvailPhys, m.ullTotalPhys
+            return None, None
+        try:
+            info = dict(line.split(":", 1) for line in open("/proc/meminfo"))
+            total = int(info["MemTotal"].split()[0]) * 1024
+            avail = int(info["MemAvailable"].split()[0]) * 1024
+            return total - avail, total
+        except (OSError, KeyError, ValueError):
+            return None, None
+
+
+# ------------------------------------------------------------------------------------------------ the sampler
+class Telemetry:
+    def __init__(self, extra=None):
+        """`extra()` -> dict of more series to record each second (the server's tok/s)."""
+        self.extra = extra
+        self.lock = threading.Lock()
+        self.now: dict = {}
+        self.hist = collections.defaultdict(lambda: collections.deque(maxlen=HISTORY))
+        self.gpu = _Nvml()
+        try:
+            import psutil  # noqa: F401
+            self.ps = sys.modules["psutil"]
+        except ImportError:
+            self.ps = None
+        self.fallback = _CpuRamFallback()
+        self.static = {
+            "gpu_name": self.gpu.name() if self.gpu.ok() else None,
+            "cpu_name": _cpu_name(),
+            "cores": (self.ps.cpu_count(logical=False) if self.ps else None) or None,
+            "threads": os.cpu_count(),
+            "psutil": self.ps is not None,
+        }
+        self._disk_prev = None
+        threading.Thread(target=self._loop, daemon=True).start()
+
+    def _disk(self):
+        if not self.ps:
+            return None, None
+        try:
+            c = self.ps.disk_io_counters()
+        except (OSError, RuntimeError):
+            return None, None
+        t = time.time()
+        prev, self._disk_prev = self._disk_prev, (t, c.read_bytes, c.write_bytes)
+        if prev is None or t <= prev[0]:
+            return None, None
+        dt = t - prev[0]
+        return (c.read_bytes - prev[1]) / dt / 2**20, (c.write_bytes - prev[2]) / dt / 2**20
+
+    def sample(self):
+        s = {}
+        if self.gpu.ok():
+            g = self.gpu.read()
+            s.update({f"gpu_{k}": v for k, v in g.items()})
+        if self.ps:
+            try:
+                s["cpu"] = self.ps.cpu_percent(interval=None)
+                vm = self.ps.virtual_memory()
+                s["ram_used"], s["ram_total"] = vm.total - vm.available, vm.total
+            except (OSError, RuntimeError):
+                pass
+        else:
+            s["cpu"] = self.fallback.cpu()
+            s["ram_used"], s["ram_total"] = self.fallback.ram()
+        s["disk_read_mb"], s["disk_write_mb"] = self._disk()
+        if self.extra:
+            try:
+                s.update(self.extra())
+            except Exception:  # noqa: BLE001 - telemetry must never take the server down
+                pass
+        return s
+
+    def _loop(self):
+        while True:
+            s = self.sample()
+            with self.lock:
+                self.now = s
+                for k in ("gpu_util", "gpu_mem_used", "gpu_temp", "gpu_power", "gpu_pcie_rx_mb", "cpu", "ram_used",
+                          "disk_read_mb", "tok_s"):
+                    v = s.get(k)
+                    self.hist[k].append(round(v, 2) if isinstance(v, float) else v)
+            time.sleep(1.0)
+
+    def snapshot(self):
+        with self.lock:
+            return {"now": dict(self.now), "history": {k: list(v) for k, v in self.hist.items()},
+                    "static": dict(self.static)}
@@ -0,0 +1,412 @@
+"""serve/test_server.py - the max tokens budget over both APIs, against the mock engine (no GPU, no pack).
+
+    python -m unittest serve.test_server -v
+"""
+from __future__ import annotations
+
+import json
+import os
+import sys
+import unittest
+import urllib.error
+import urllib.request
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+from serve.frontend import ChatTemplate  # noqa: E402
+from serve.server import CTX_SLACK, ByteTokenizer, EngineDied, MockEngine, Service, serve  # noqa: E402
+
+ROOT = Path(__file__).resolve().parents[1]
+CTX = 4096
+ANSWER = "x" * 2000                              # longer than the old 1024 fallback: one token per byte
+
+
+class RecordingEngine(MockEngine):
+    def generate(self, ids, max_new, sampling, cancel, embeddings=None):
+        self.last_max_new = max_new
+        yield from super().generate(ids, max_new, sampling, cancel, embeddings)
+
+
+class MaxTokens(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        tok = ByteTokenizer()
+        cls.engine = RecordingEngine(tok, "</think>\n\n" + ANSWER, max_context=CTX)
+        cls.svc = Service(cls.engine, tok, ChatTemplate(ROOT / "serve/chat_template.jinja"))
+        cls.httpd = serve(cls.svc, port=0)
+        cls.base = f"http://127.0.0.1:{cls.httpd.server_address[1]}"
+
+    @classmethod
+    def tearDownClass(cls):
+        cls.httpd.shutdown()
+        cls.httpd.server_close()
+
+    def post(self, path, body):
+        req = urllib.request.Request(self.base + path, data=json.dumps(body).encode(),
+                                     headers={"Content-Type": "application/json"})
+        try:
+            with urllib.request.urlopen(req, timeout=30) as r:
+                return r.status, json.loads(r.read())
+        except urllib.error.HTTPError as e:
+            with e:
+                return e.code, json.loads(e.read())
+
+    def call(self, api, text="hi", **budget):
+        """-> (status, body, prompt tokens, completion tokens); `budget` is merged into the request as given."""
+        msgs = [{"role": "user", "content": text}]
+        if api == "openai":
+            s, b = self.post("/v1/chat/completions", {"model": "m", "messages": msgs, **budget})
+            u = b.get("usage", {})
+            return s, b, u.get("prompt_tokens"), u.get("completion_tokens")
+        s, b = self.post("/v1/messages", {"model": "m", "messages": msgs, **budget})
+        u = b.get("usage", {})
+        return s, b, u.get("input_tokens"), u.get("output_tokens")
+
+    def test_unset_budget_is_the_rest_of_the_context(self):
+        cases = {"openai": [{"max_tokens": -1}, {"max_tokens": 0}, {}, {"max_tokens": None},
+                            {"max_completion_tokens": -1}, {"max_completion_tokens": None, "max_tokens": None}],
+                 "anthropic": [{"max_tokens": -1}, {"max_tokens": 0}, {}, {"max_tokens": None}]}
+        for api, budgets in cases.items():
+            for budget in budgets:
+                with self.subTest(api=api, budget=budget):
+                    s, b, pt, ct = self.call(api, **budget)
+                    self.assertEqual(s, 200, b)
+                    self.assertEqual(self.engine.last_max_new, CTX - CTX_SLACK - pt)
+                    self.assertGreater(ct, 1024)          # the whole answer, not cut at the old 1024 fallback
+
+    def test_explicit_budget_is_honoured(self):
+        for api, budget in [("openai", {"max_tokens": 50}), ("openai", {"max_completion_tokens": 50}),
+                            ("openai", {"max_completion_tokens": 50, "max_tokens": 9}),
+                            ("anthropic", {"max_tokens": 50}), ("openai", {"max_tokens": 1500}),
+                            ("anthropic", {"max_tokens": 1500})]:
+            with self.subTest(api=api, budget=budget):
+                want = budget.get("max_completion_tokens") or budget["max_tokens"]
+                s, b, _, ct = self.call(api, **budget)
+                self.assertEqual(s, 200, b)
+                self.assertEqual(self.engine.last_max_new, want)
+                self.assertEqual(ct, want)
+
+    def test_explicit_budget_over_the_context_is_rejected(self):
+        for api in ("openai", "anthropic"):
+            with self.subTest(api=api):
+                s, b, _, _ = self.call(api, max_tokens=CTX)
+                self.assertEqual(s, 400)
+                self.assertIn("exceeds the context", b["error"]["message"])
+
+    def test_unset_budget_with_a_near_full_prompt(self):
+        _, _, pt0, _ = self.call("openai", max_tokens=1)
+        overhead = pt0 - len("hi")                  # the template's tokens around the user text
+        for api in ("openai", "anthropic"):
+            _, _, pa, _ = self.call(api, max_tokens=1)
+            over = pa - pt0                          # the Anthropic template may differ slightly
+            with self.subTest(api=api, room=5):     # a few tokens left: the budget is exactly those
+                text = "y" * (CTX - CTX_SLACK - overhead - over - 5)
+                s, b, pt, ct = self.call(api, text=text, max_tokens=-1)
+                self.assertEqual(s, 200, b)
+                self.assertEqual(self.engine.last_max_new, 5)
+                self.assertEqual(ct, 5)
+            with self.subTest(api=api, room=0):     # nothing left: rejected, not truncated
+                text = "y" * (CTX - CTX_SLACK - overhead - over)
+                s, b, _, _ = self.call(api, text=text)
+                self.assertEqual(s, 400, b)
+                self.assertIn("no room to answer", b["error"]["message"])
+
+    def test_debug_log_shows_the_resolved_budget(self):
+        import contextlib
+        import io
+        os.environ["STRATA_DEBUG"] = "1"
+        try:
+            for api in ("openai", "anthropic"):
+                with self.subTest(api=api):
+                    out = io.StringIO()
+                    with contextlib.redirect_stdout(out):
+                        _, _, pt, _ = self.call(api, max_tokens=-1)
+                    self.assertIn(f"max_new={CTX - CTX_SLACK - pt} ", out.getvalue())
+        finally:
+            del os.environ["STRATA_DEBUG"]
+
+
+class FitMaxTokens(unittest.TestCase):
+    """PR #24: --fit-max-tokens clamps an explicit budget that overshoots the context instead of a 400."""
+
+    @classmethod
+    def setUpClass(cls):
+        tok = ByteTokenizer()
+        cls.engine = RecordingEngine(tok, "</think>\n\n" + ANSWER, max_context=CTX)
+        cls.svc = Service(cls.engine, tok, ChatTemplate(ROOT / "serve/chat_template.jinja"), fit_max_tokens=True)
+        cls.httpd = serve(cls.svc, port=0)
+        cls.base = f"http://127.0.0.1:{cls.httpd.server_address[1]}"
+
+    @classmethod
+    def tearDownClass(cls):
+        cls.httpd.shutdown()
+        cls.httpd.server_close()
+
+    post = MaxTokens.post
+    call = MaxTokens.call
+
+    def test_overshoot_is_clamped_to_the_room(self):
+        for api in ("openai", "anthropic"):
+            with self.subTest(api=api):
+                s, b, pt, ct = self.call(api, max_tokens=CTX)
+                self.assertEqual(s, 200, b)
+                self.assertEqual(self.engine.last_max_new, CTX - CTX_SLACK - pt)
+
+    def test_a_budget_that_fits_is_unchanged(self):
+        s, b, _, ct = self.call("openai", max_tokens=50)
+        self.assertEqual(s, 200, b)
+        self.assertEqual(self.engine.last_max_new, 50)
+
+    def test_no_room_is_still_a_400(self):
+        _, _, pt0, _ = self.call("openai", max_tokens=1)
+        overhead = pt0 - len("hi")
+        s, b, _, _ = self.call("openai", text="y" * (CTX - CTX_SLACK - overhead), max_tokens=100)
+        self.assertEqual(s, 400, b)
+        self.assertIn("no room to answer", b["error"]["message"])
+
+
+class DyingEngine(MockEngine):
+    """Issue #27: an engine that dies after a few tokens of its first answer, and comes back when restarted."""
+
+    def __init__(self, tok, script, max_context):
+        super().__init__(tok, script, max_context=max_context)
+        self.dead, self.restarts, self.die_after = False, 0, 5
+
+    def alive(self):
+        return not self.dead
+
+    def restart(self):
+        self.dead, self.die_after = False, None
+        self.restarts += 1
+
+    def generate(self, ids, max_new, sampling, cancel, embeddings=None):
+        for i, t in enumerate(super().generate(ids, max_new, sampling, cancel, embeddings)):
+            if self.die_after is not None and i == self.die_after:
+                self.dead = True
+                raise EngineDied("the engine stopped unexpectedly (exit code -9)")
+            yield t
+
+
+class EngineDeath(unittest.TestCase):
+    """Issue #27: a dead engine is an error (not "length"), and the next request starts it again."""
+
+    def test_error_then_restart(self):
+        tok = ByteTokenizer()
+        eng = DyingEngine(tok, "</think>\n\n" + ANSWER, max_context=CTX)
+        svc = Service(eng, tok, ChatTemplate(ROOT / "serve/chat_template.jinja"))
+        httpd = serve(svc, port=0)
+        base = f"http://127.0.0.1:{httpd.server_address[1]}"
+        try:
+            def post(body):
+                req = urllib.request.Request(base + "/v1/chat/completions", data=json.dumps(body).encode(),
+                                             headers={"Content-Type": "application/json"})
+                try:
+                    with urllib.request.urlopen(req, timeout=30) as r:
+                        return r.status, r.read().decode()
+                except urllib.error.HTTPError as e:
+                    with e:
+                        return e.code, e.read().decode()
+            msgs = [{"role": "user", "content": "hi"}]
+            code, text = post({"model": "m", "messages": msgs, "max_tokens": 50, "stream": True})
+            self.assertEqual(code, 200)
+            self.assertIn('"error"', text)
+            self.assertIn("stopped unexpectedly", text)
+            self.assertTrue(text.rstrip().endswith("data: [DONE]"))
+            self.assertEqual(svc.metrics()["requests"][0]["finish"], "error")
+            code, text = post({"model": "m", "messages": msgs, "max_tokens": 50})
+            self.assertEqual(code, 200, text)
+            self.assertEqual(eng.restarts, 1)
+            self.assertEqual(json.loads(text)["usage"]["completion_tokens"], 50)
+        finally:
+            httpd.shutdown()
+            httpd.server_close()
+
+    def test_engine_err_mid_stream(self):
+        """The engine's ERR line after the stream started reaches the client as an error event (it used to be a
+        400 written into the open stream, which clients read as an empty answer)."""
+        class ErrEngine(MockEngine):
+            def generate(self, ids, max_new, sampling, cancel, embeddings=None):
+                yield None                                  # a prompt-progress heartbeat: the stream has started
+                raise ValueError("verify: layer 31 never rang (an illegal memory access was encountered)")
+
+        tok = ByteTokenizer()
+        svc = Service(ErrEngine(tok, ANSWER, max_context=CTX), tok, ChatTemplate(ROOT / "serve/chat_template.jinja"))
+        httpd = serve(svc, port=0)
+        base = f"http://127.0.0.1:{httpd.server_address[1]}"
+        try:
+            for path, body in [("/v1/chat/completions", {"model": "m", "stream": True, "max_tokens": 20,
+                                                          "messages": [{"role": "user", "content": "hi"}]}),
+                               ("/v1/messages", {"model": "m", "stream": True, "max_tokens": 20,
+                                                 "messages": [{"role": "user", "content": "hi"}]})]:
+                req = urllib.request.Request(base + path, data=json.dumps(body).encode(),
+                                             headers={"Content-Type": "application/json"})
+                with urllib.request.urlopen(req, timeout=30) as r:
+                    text = r.read().decode()
+                self.assertIn("illegal memory access", text, path)
+                self.assertNotIn("HTTP/1", text, path)
+                self.assertEqual(svc.metrics()["requests"][0]["finish"], "error")
+        finally:
+            httpd.shutdown()
+            httpd.server_close()
+
+
+class SharedSettings(unittest.TestCase):
+    """The web app's "Use for other apps too": POST /settings makes its Chat settings every client's defaults."""
+
+    @classmethod
+    def setUpClass(cls):
+        import tempfile
+
+        class Sampled(RecordingEngine):
+            def generate(self, ids, max_new, sampling, cancel, embeddings=None):
+                self.last_sampling = dict(sampling or {})
+                yield from super().generate(ids, max_new, sampling, cancel, embeddings)
+
+        tok = ByteTokenizer()
+        cls.engine = Sampled(tok, "</think>\n\nhello", max_context=CTX)
+        cls.svc = Service(cls.engine, tok, ChatTemplate(ROOT / "serve/chat_template.jinja"))
+        cls.tmp = tempfile.TemporaryDirectory()
+        cls.svc.shared_path = os.path.join(cls.tmp.name, "strata-x.shared-settings.json")
+        cls.httpd = serve(cls.svc, port=0)
+        cls.base = f"http://127.0.0.1:{cls.httpd.server_address[1]}"
+
+    @classmethod
+    def tearDownClass(cls):
+        cls.httpd.shutdown()
+        cls.httpd.server_close()
+        cls.tmp.cleanup()
+
+    def req(self, path, body, headers=None, raw=None):
+        h = {"Content-Type": "application/json", **(headers or {})}
+        r = urllib.request.Request(self.base + path, data=raw if raw is not None else json.dumps(body).encode(), headers=h)
+        try:
+            with urllib.request.urlopen(r, timeout=30) as resp:
+                return resp.status, json.loads(resp.read())
+        except urllib.error.HTTPError as e:
+            with e:
+                return e.code, json.loads(e.read())
+
+    def chat(self, **extra):
+        return self.req("/v1/chat/completions", {"model": "m", "messages": [{"role": "user", "content": "hi"}], **extra})
+
+    def tearDown(self):
+        self.svc.set_shared(None)
+
+    def test_other_apps_get_the_chat_settings(self):
+        d = {"temperature": 0.3, "top_p": 0.9, "top_k": 10, "seed": 7, "max_tokens": 77,
+             "reasoning_effort": "low", "experimental_speed_projection": False}
+        code, b = self.req("/settings", {"defaults": d})
+        self.assertEqual(code, 200, b)
+        self.assertTrue(b["shared"])
+        self.assertTrue(os.path.exists(self.svc.shared_path))
+        code, _ = self.chat()                                        # a client that sets nothing
+        self.assertEqual(code, 200)
+        got = self.engine.last_sampling
+        for k in ("temperature", "top_p", "top_k", "seed", "experimental_speed_projection"):
+            self.assertEqual(got[k], d[k], k)
+        self.assertEqual(self.engine.last_max_new, 77)
+        code, _ = self.chat(temperature=0.9, max_tokens=5)          # its own values win
+        self.assertEqual(self.engine.last_sampling["temperature"], 0.9)
+        self.assertEqual(self.engine.last_max_new, 5)
+        r = self.svc.with_shared({"messages": []}, "openai")
+        self.assertEqual(r["reasoning_effort"], "low")
+        self.assertEqual(self.svc.with_shared({"reasoning_effort": "high"}, "openai")["reasoning_effort"], "high")
+        self.assertEqual(self.svc.with_shared({}, "anthropic")["output_config"], {"effort": "low"})
+
+    def test_off_again(self):
+        self.req("/settings", {"defaults": {"temperature": 0.3}})
+        code, b = self.req("/settings", {"defaults": None})
+        self.assertEqual((code, b["shared"]), (200, False))
+        self.assertFalse(os.path.exists(self.svc.shared_path))
+        self.chat()
+        self.assertNotIn("temperature", self.engine.last_sampling)
+
+    def test_only_strata_s_own_page_may_set_them(self):
+        code, _ = self.req("/settings", None, {"Content-Type": "text/plain"}, raw=b'{"defaults": {"temperature": 1}}')
+        self.assertEqual(code, 415)
+        code, _ = self.req("/settings", {"defaults": {"temperature": 1}}, {"Origin": "http://evil.example"})
+        self.assertEqual(code, 403)
+        code, b = self.req("/settings", {"defaults": {"temperature": 9}})
+        self.assertEqual(code, 400)
+        self.assertIn("temperature", b["error"]["message"])
+        self.assertEqual(self.svc.shared, {})
+        host = self.base.split("://", 1)[1]
+        code, _ = self.req("/settings", {"defaults": {"temperature": 1}}, {"Origin": "http://" + host})
+        self.assertEqual(code, 200)
+
+    def test_they_need_the_key_when_one_is_set(self):
+        self.svc.api_key = "secret"
+        try:
+            self.assertEqual(self.req("/settings", {"defaults": {"temperature": 1}})[0], 401)
+            self.assertEqual(self.req("/settings", {"defaults": {"temperature": 1}},
+                                      {"Authorization": "Bearer secret"})[0], 200)
+        finally:
+            self.svc.api_key = ""
+
+
+class WebApp(unittest.TestCase):
+    """The web app (PR #22's dashboard idea, rebuilt): its page and files, and GET /metrics."""
+
+    @classmethod
+    def setUpClass(cls):
+        tok = ByteTokenizer()
+        cls.svc = Service(RecordingEngine(tok, "</think>\n\nhello", max_context=CTX), tok,
+                          ChatTemplate(ROOT / "serve/chat_template.jinja"))
+        cls.httpd = serve(cls.svc, port=0)
+        cls.base = f"http://127.0.0.1:{cls.httpd.server_address[1]}"
+
+    @classmethod
+    def tearDownClass(cls):
+        cls.httpd.shutdown()
+
+    def get(self, path, headers=None):
+        req = urllib.request.Request(self.base + path, headers=headers or {})
+        try:
+            with urllib.request.urlopen(req, timeout=10) as r:
+                return r.status, r.headers.get("Content-Type", ""), r.read()
+        except urllib.error.HTTPError as e:
+            return e.code, e.headers.get("Content-Type", ""), e.read()
+
+    def test_page_and_files(self):
+        code, ctype, body = self.get("/")
+        self.assertEqual(code, 200)
+        self.assertIn("text/html", ctype)
+        self.assertIn(b"/web/app.js", body)
+        for path, want in (("/web/app.js", "javascript"), ("/web/app.css", "text/css"), ("/web/tokens.css", "text/css"),
+                           ("/web/components.css", "text/css"), ("/web/sprite.svg", "image/svg+xml")):
+            with self.subTest(path=path):
+                code, ctype, _ = self.get(path)
+                self.assertEqual(code, 200)
+                self.assertIn(want, ctype)
+
+    def test_only_the_app_files_are_served(self):
+        for path in ("/web/..%2Fserver.py", "/web/index.html", "/web/test.py", "/fonts/..%2F..%2Fsetup.py",
+                     "/fonts/missing.woff2", "/fonts/x.ttf"):
+            with self.subTest(path=path):
+                self.assertEqual(self.get(path)[0], 404)
+
+    def test_metrics(self):
+        data = json.dumps({"model": "m", "messages": [{"role": "user", "content": "hi"}], "max_tokens": 5}).encode()
+        urllib.request.urlopen(urllib.request.Request(self.base + "/v1/chat/completions", data=data,
+                                                      headers={"Content-Type": "application/json"}), timeout=10).read()
+        code, ctype, body = self.get("/metrics")
+        self.assertEqual(code, 200)
+        m = json.loads(body)
+        for key in ("engine", "live", "requests", "hardware", "hardware_static", "history"):
+            self.assertIn(key, m)
+        self.assertEqual(m["engine"]["max_context"], CTX)
+        self.assertEqual(m["live"]["state"], "idle")
+        self.assertEqual(m["requests"][0]["output_tokens"], 5)
+
+    def test_metrics_need_the_key_when_one_is_set(self):
+        self.svc.api_key = "secret"
+        try:
+            self.assertEqual(self.get("/metrics")[0], 401)
+            self.assertEqual(self.get("/metrics", {"Authorization": "Bearer secret"})[0], 200)
+            self.assertEqual(self.get("/")[0], 200)                  # the page itself asks for the key
+        finally:
+            self.svc.api_key = ""
+
+
+if __name__ == "__main__":
+    unittest.main()
@@ -0,0 +1,146 @@
+/* Strata web app - layout on top of tokens.css + components.css (the Strata UI kit). Colours, type, radii and
+   shadows come only from the kit's tokens. */
+[hidden] { display: none !important; }
+html, body { height: 100%; }
+body { display: flex; flex-direction: column; overflow: hidden; }
+main { flex: 1; min-height: 0; display: flex; }
+.view { flex: 1; min-height: 0; display: flex; flex-direction: column; }
+.view[hidden] { display: none; }
+.muted { color: var(--st-ink-muted); }
+.small { font-size: var(--st-fs-xs); }
+.card-title { font-weight: var(--st-fw-bold); font-size: var(--st-fs-base); color: var(--st-ink); }
+.st-header { position: relative; z-index: 2; flex: none; }
+.st-header .st-tabs { margin-left: var(--st-s-4); }
+#drawer-close .st-icon { transform: rotate(-90deg); }
+
+/* ---------------------------------------------------------------- chat */
+.chat-scroll { flex: 1; min-height: 0; overflow-y: auto; }
+.chat { max-width: 860px; margin: 0 auto; padding: var(--st-s-8) var(--st-s-6) var(--st-s-6); display: flex;
+        flex-direction: column; gap: var(--st-s-6); }
+.chat-empty { margin: 12vh auto 0; text-align: center; }
+.chat-empty__title { font-weight: var(--st-fw-black); font-size: var(--st-fs-display); line-height: var(--st-lh-tight);
+                     letter-spacing: -.02em; }
+.chat-empty__sub { margin-top: var(--st-s-3); color: var(--st-ink-muted); }
+.st-msg { width: 100%; }
+.st-msg--assistant { align-self: flex-start; }
+.st-msg--assistant .st-bubble { width: 100%; }
+.st-msg--user .st-bubble { white-space: pre-wrap; overflow-wrap: anywhere; }
+.st-bubble > :first-child { margin-top: 0; }
+.st-bubble > :last-child { margin-bottom: 0; }
+.st-bubble p, .st-bubble ul, .st-bubble ol { margin: 0 0 var(--st-s-3); }
+.st-bubble ul, .st-bubble ol { padding-left: var(--st-s-6); }
+.st-bubble li + li { margin-top: var(--st-s-1); }
+.st-bubble h3 { font-size: var(--st-fs-lg); margin: var(--st-s-4) 0 var(--st-s-2); color: var(--st-ink); }
+.st-bubble h4 { font-size: var(--st-fs-base); margin: var(--st-s-3) 0 var(--st-s-2); color: var(--st-ink); }
+.st-bubble code.inline { font: 400 .9em var(--st-font-mono); background: var(--st-surface-2); border: 1px solid var(--st-line-soft);
+                         border-radius: 6px; padding: 1px 5px; }
+.st-bubble .st-code { margin: var(--st-s-3) 0; }
+.st-bubble blockquote { margin: 0 0 var(--st-s-3); padding-left: var(--st-s-3); border-left: 3px solid var(--st-line);
+                        color: var(--st-ink-muted); }
+.st-bubble hr { border: 0; border-top: 1px solid var(--st-line); margin: var(--st-s-4) 0; }
+.st-bubble table { border-collapse: collapse; margin: 0 0 var(--st-s-3); font-size: var(--st-fs-md); }
+.st-bubble th, .st-bubble td { border: 1px solid var(--st-line); padding: 6px 10px; text-align: left; }
+.msg-images { display: flex; flex-wrap: wrap; gap: var(--st-s-2); justify-content: flex-end; }
+.msg-images img { max-width: 220px; max-height: 180px; border-radius: var(--st-r-md); border: 1px solid var(--st-line); }
+.st-msg .st-collapse { width: 100%; }
+.st-collapse__body.thinking { white-space: pre-wrap; overflow-wrap: anywhere; max-height: 420px; overflow-y: auto; }
+.st-msg__meta .st-btn--icon { width: 28px; height: 28px; }
+.st-msg__meta .st-btn--icon .st-icon { width: 16px; height: 16px; }
+.cursor::after { content: ""; display: inline-block; width: 8px; height: 1.1em; margin-left: 2px; vertical-align: -3px;
+                 border-radius: 2px; background: var(--st-accent); animation: blink 1s steps(2) infinite; }
+@keyframes blink { 50% { opacity: 0; } }
+.msg-error { color: var(--st-danger-text); background: var(--st-danger-tint); border-radius: var(--st-r-md); padding: 10px 14px;
+             font-size: var(--st-fs-md); }
+
+.composer-wrap { flex: none; padding: 0 var(--st-s-6) var(--st-s-6); }
+.st-composer { max-width: 860px; margin: 0 auto; }
+.st-composer textarea { max-height: 40vh; }
+.st-btn--send { width: 42px; padding: 0; }
+.composer-hint { font-size: var(--st-fs-xs); color: var(--st-ink-muted); margin-right: var(--st-s-2); font-variant-numeric: tabular-nums; }
+.attachments { display: flex; flex-wrap: wrap; gap: var(--st-s-2); padding: 2px 4px 0; }
+.st-composer.dragging { outline: 2px dashed var(--st-accent); outline-offset: 3px; }
+.chip { display: inline-flex; align-items: center; gap: 6px; height: 30px; padding: 0 6px 0 10px; border-radius: var(--st-r-pill);
+        background: var(--st-surface-2); border: 1px solid var(--st-line); font-size: var(--st-fs-xs); color: var(--st-ink-soft); }
+.chip .st-btn--icon { width: 22px; height: 22px; border-radius: 50%; }
+.chip .st-btn--icon .st-icon { width: 14px; height: 14px; }
+
+/* ---------------------------------------------------------------- monitor */
+.view--monitor, .view--about { overflow-y: auto; }
+.monitor, .about { max-width: 1240px; width: 100%; margin: 0 auto; padding: var(--st-s-6); display: flex; flex-direction: column;
+                   gap: var(--st-s-5); }
+.state-card { display: flex; flex-direction: column; gap: var(--st-s-3); }
+.state-card__head { display: flex; align-items: center; gap: var(--st-s-4); flex-wrap: wrap; }
+.badges { display: flex; gap: var(--st-s-2); flex-wrap: wrap; }
+.badges .st-badge { opacity: .38; transition: opacity var(--st-dur); }
+.badges .st-badge.on { opacity: 1; }
+.state-card__row { display: flex; justify-content: space-between; gap: var(--st-s-4); font-size: var(--st-fs-sm);
+                   font-weight: var(--st-fw-medium); font-variant-numeric: tabular-nums; }
+.metrics { display: grid; grid-template-columns: repeat(4, minmax(0, 1fr)); gap: var(--st-s-4); }
+.metric-card { padding: var(--st-s-4) var(--st-s-5); }
+.st-metric__value small { font-size: var(--st-fs-md); font-weight: var(--st-fw-medium); color: var(--st-ink-muted); margin-left: 4px; }
+.st-metric__sub { font-size: var(--st-fs-xs); color: var(--st-ink-muted); font-variant-numeric: tabular-nums; min-height: 1em; }
+.st-metric__spark { display: block; overflow: visible; }
+.st-metric__spark[data-tone="warn"] { color: var(--st-warn); }
+.st-metric__spark[data-tone="info"] { color: var(--st-info); }
+.monitor-row { display: grid; grid-template-columns: minmax(260px, 1fr) minmax(0, 2.4fr); gap: var(--st-s-4); }
+.ctx-card { display: flex; flex-direction: column; gap: var(--st-s-2); }
+.ctx-gauge { position: relative; width: 150px; height: 150px; margin: var(--st-s-2) auto var(--st-s-3); }
+.ctx-gauge .st-gauge { width: 150px; height: 150px; }
+.ctx-gauge .st-gauge__fill { transition: stroke-dasharray 400ms var(--st-ease); }
+.ctx-gauge__label { position: absolute; inset: 0; display: flex; flex-direction: column; align-items: center; justify-content: center;
+                    font-size: var(--st-fs-xs); }
+.ctx-gauge__pct { font-weight: var(--st-fw-black); font-size: var(--st-fs-xl); line-height: 1.1; font-variant-numeric: tabular-nums; }
+.bar-row { display: flex; justify-content: space-between; font-size: var(--st-fs-sm); font-weight: var(--st-fw-medium);
+           margin-top: var(--st-s-2); font-variant-numeric: tabular-nums; }
+.req-card { display: flex; flex-direction: column; gap: var(--st-s-3); min-width: 0; }
+.table-wrap { overflow-x: auto; }
+.req-head { display: flex; align-items: center; justify-content: space-between; gap: var(--st-s-2); }
+.table-wrap.all { max-height: 420px; overflow-y: auto; }
+.req-totals { margin: 0; font-size: var(--st-fs-sm); }
+.req-totals:empty { display: none; }
+
+/* ---------------------------------------------------------------- about */
+.about { max-width: 860px; }
+.about .st-card { display: flex; flex-direction: column; gap: var(--st-s-3); }
+.about p { margin: 0; }
+.facts { display: grid; grid-template-columns: minmax(140px, max-content) 1fr; gap: 10px var(--st-s-6); margin: 0; font-size: var(--st-fs-md); }
+.facts dt { color: var(--st-ink-muted); }
+.facts dd { margin: 0; color: var(--st-ink-soft); font-variant-numeric: tabular-nums; display: flex; align-items: center; gap: 6px;
+            overflow-wrap: anywhere; }
+.facts dd code { font: 400 13px var(--st-font-mono); }
+.facts .st-btn--icon { width: 28px; height: 28px; }
+.facts .st-btn--icon .st-icon { width: 16px; height: 16px; }
+.toggle-row { display: flex; align-items: center; justify-content: space-between; gap: var(--st-s-4); font-size: var(--st-fs-sm);
+              font-weight: var(--st-fw-medium); color: var(--st-ink-soft); }
+.toggle-row .st-toggle { flex: none; }   /* a long label must not squeeze the switch */
+
+/* ---------------------------------------------------------------- drawer */
+.st-drawer { z-index: 40; }
+.scrim { position: fixed; inset: 0; background: rgba(15, 23, 42, .18); z-index: 39; }
+.seg { display: grid; grid-template-columns: repeat(4, 1fr); gap: 4px; padding: 4px; background: var(--st-surface-2);
+       border: 1px solid var(--st-line); border-radius: var(--st-r-md); }
+.seg button { height: 32px; border: 0; border-radius: 10px; background: transparent; color: var(--st-ink-muted);
+              font: 500 var(--st-fs-sm)/1 var(--st-font); cursor: pointer; }
+.seg button[aria-checked="true"] { background: var(--st-surface); color: var(--st-ink); font-weight: var(--st-fw-bold);
+                                   box-shadow: var(--st-sh-card); }
+.drawer-actions { display: grid; grid-template-columns: 1fr 1fr; gap: var(--st-s-3); margin-top: var(--st-s-2); }
+.st-toast { animation: toast-in 180ms var(--st-ease); }
+@keyframes toast-in { from { opacity: 0; transform: translateY(6px); } }
+
+/* ---------------------------------------------------------------- narrow screens */
+@media (max-width: 1000px) {
+  .metrics { grid-template-columns: repeat(2, minmax(0, 1fr)); }
+  .monitor-row { grid-template-columns: 1fr; }
+}
+@media (max-width: 640px) {
+  .st-header { padding: 0 var(--st-s-3); gap: var(--st-s-2); }
+  .st-header .st-tabs { margin-left: 0; }
+  .st-tab { padding: 0 10px; }
+  .st-tab .st-icon { display: none; }
+  #pill-text { display: none; }
+  .chat { padding: var(--st-s-5) var(--st-s-4); }
+  .composer-wrap { padding: 0 var(--st-s-3) var(--st-s-3); }
+  .monitor, .about { padding: var(--st-s-4); }
+  .facts { grid-template-columns: 1fr; gap: 2px; }
+  .facts dd { margin-bottom: var(--st-s-2); }
+}
@@ -0,0 +1,829 @@
+// serve/web/app.js - the Strata web app: Chat, Monitor, About. No framework, no network beyond this server.
+// The Monitor tab rebuilds PR #22's dashboard idea (code-martin) on the server's own /metrics.
+"use strict";
+
+const $ = (id) => document.getElementById(id);
+const SPRITE = "/web/sprite.svg";
+const icon = (name, cls = "st-icon") => `<svg class="${cls}" aria-hidden="true"><use href="${SPRITE}#i-${name}"/></svg>`;
+const esc = (s) => String(s).replace(/[&<>"']/g, (c) => ({"&": "&amp;", "<": "&lt;", ">": "&gt;", '"': "&quot;", "'": "&#39;"}[c]));
+const fmt = (n, d = 0) => (n == null || Number.isNaN(n) ? "–" : Number(n).toLocaleString(undefined, {maximumFractionDigits: d, minimumFractionDigits: d}));
+const kfmt = (n) => (n == null ? "–" : n >= 1000 ? `${fmt(n / 1000, n >= 10000 ? 0 : 1)}k` : fmt(n));
+// a context size: 32768 -> "32K" (powers of two), else like kfmt
+const ctxfmt = (n) => (n && n % 1024 === 0 ? `${fmt(n / 1024)}K` : kfmt(n));
+const gb = (b, d = 1) => (b == null ? "–" : fmt(b / 1073741824, d));   // memory: binary GB, as Windows shows it
+
+const store = {
+  get(k, d) { try { const v = localStorage.getItem("strata." + k); return v === null ? d : JSON.parse(v); } catch (e) { return d; } },
+  set(k, v) { try { localStorage.setItem("strata." + k, JSON.stringify(v)); } catch (e) { /* private mode: in memory only */ } },
+};
+
+// ------------------------------------------------------------------ toasts
+function toast(kind, title, text = "", ms = 3500, action = null) {
+  const names = {info: "info", success: "check", warn: "warning", error: "error"};
+  const el = document.createElement("div");
+  el.className = `st-toast st-toast--${kind}`;
+  el.innerHTML = `${icon(names[kind] || "info")}<div><div class="st-toast__title"></div><div class="t-text"></div></div>`;
+  el.querySelector(".st-toast__title").textContent = title;
+  el.querySelector(".t-text").textContent = text;
+  if (action) {
+    const b = document.createElement("button");
+    b.className = "st-btn st-btn--secondary";
+    b.style.height = "32px";
+    b.style.marginLeft = "auto";
+    b.textContent = action.label;
+    b.onclick = () => { action.run(); el.remove(); };
+    el.appendChild(b);
+  }
+  $("toasts").appendChild(el);
+  setTimeout(() => el.remove(), ms);
+}
+
+async function copyText(text, btn) {
+  try {
+    await navigator.clipboard.writeText(text);
+  } catch (e) {                                   // http on another host: no async clipboard
+    const ta = document.createElement("textarea");
+    ta.value = text; document.body.appendChild(ta); ta.select(); document.execCommand("copy"); ta.remove();
+  }
+  if (btn) {
+    const use = btn.querySelector("use");
+    use.setAttribute("href", `${SPRITE}#i-check`);
+    setTimeout(() => use.setAttribute("href", `${SPRITE}#i-copy`), 1500);
+  }
+  toast("success", "Copied to clipboard", "", 1800);
+}
+
+// ------------------------------------------------------------------ theme and tabs
+// the system's theme until the user picks one (only a click is saved)
+function setTheme(t, save) {
+  document.documentElement.dataset.theme = t;
+  if (save) try { localStorage.setItem("strata.theme", t); } catch (e) { /* ignore */ }
+  $("theme-icon").setAttribute("href", `${SPRITE}#i-${t === "dark" ? "sun" : "moon"}`);
+  $("dark-toggle").setAttribute("aria-checked", String(t === "dark"));
+}
+const flipTheme = () => setTheme(document.documentElement.dataset.theme === "dark" ? "light" : "dark", true);
+$("theme-btn").onclick = flipTheme;
+$("dark-toggle").onclick = flipTheme;
+setTheme(document.documentElement.dataset.theme || "light", false);
+matchMedia("(prefers-color-scheme: dark)").addEventListener("change", (e) => {
+  let saved = null;
+  try { saved = localStorage.getItem("strata.theme"); } catch (err) { /* ignore */ }
+  if (!saved) setTheme(e.matches ? "dark" : "light", false);
+});
+
+let tab = "chat";
+function showTab(name) {
+  tab = ["chat", "monitor", "about"].includes(name) ? name : "chat";
+  for (const b of document.querySelectorAll(".st-tab")) b.setAttribute("aria-selected", String(b.dataset.tab === tab));
+  for (const v of ["chat", "monitor", "about"]) $(`view-${v}`).hidden = v !== tab;
+  if (location.hash.slice(1) !== tab) history.replaceState(null, "", tab === "chat" ? location.pathname : `#${tab}`);
+  if (tab === "chat") $("input").focus();
+  if (lastMetrics) render(lastMetrics);
+}
+for (const b of document.querySelectorAll(".st-tab")) b.onclick = () => showTab(b.dataset.tab);
+window.addEventListener("hashchange", () => showTab(location.hash.slice(1)));
+
+// ------------------------------------------------------------------ server access
+function headers(json = false) {
+  const h = {};
+  const key = store.get("apikey", "");
+  if (key) h.Authorization = "Bearer " + key;
+  if (json) h["Content-Type"] = "application/json";
+  return h;
+}
+$("api-key").value = store.get("apikey", "");
+$("api-key").onchange = () => { store.set("apikey", $("api-key").value.trim()); toast("success", "API key saved", "Kept in this browser only."); };
+
+let health = {model: "strata", images: false, max_context: 0};
+async function loadHealth() {
+  try {
+    health = await (await fetch("/health")).json();
+    $("attach-btn").title = health.images ? "Attach a text file or a picture (or drop it here)"
+                                          : "Attach a text file (or drop it here)";
+    $("chat-empty-sub").textContent = `${health.model} runs on this PC. Nothing leaves it.`;
+  } catch (e) {
+    setTimeout(loadHealth, 2000);
+  }
+}
+
+// ------------------------------------------------------------------ Monitor
+const METRICS = [
+  {key: "speed", label: "Speed", icon: "gauge", unit: "tok/s", series: "tok_s"},
+  {key: "gpu", label: "GPU load", icon: "gpu", unit: "%", series: "gpu_util", max: 100},
+  {key: "vram", label: "VRAM", icon: "layers", unit: "GB", series: "gpu_mem_used"},
+  {key: "temp", label: "GPU temp", icon: "thermometer", unit: "°C", series: "gpu_temp", tone: "warn"},
+  {key: "power", label: "Power", icon: "bolt", unit: "W", series: "gpu_power"},
+  {key: "pcie", label: "PCIe", icon: "link", unit: "", series: "gpu_pcie_rx_mb", tone: "info"},
+  {key: "cpu", label: "CPU", icon: "cpu", unit: "%", series: "cpu", max: 100},
+  {key: "disk", label: "Disk read", icon: "disk", unit: "MB/s", series: "disk_read_mb", tone: "info"},
+];
+$("metrics").innerHTML = METRICS.map((m) => `
+  <div class="st-card metric-card"><div class="st-metric">
+    <span class="st-metric__label">${icon(m.icon, "st-icon st-icon--sm")}${esc(m.label)}</span>
+    <span class="st-metric__value" id="mv-${m.key}">–</span>
+    <span class="st-metric__sub" id="ms-${m.key}"></span>
+    <svg class="st-metric__spark" id="sp-${m.key}" viewBox="0 0 100 32" preserveAspectRatio="none"${m.tone ? ` data-tone="${m.tone}"` : ""}>
+      <path class="area" fill="currentColor" opacity=".12"/><path class="line" fill="none" stroke="currentColor"
+      stroke-width="1.6" stroke-linejoin="round" stroke-linecap="round" vector-effect="non-scaling-stroke"/></svg>
+  </div></div>`).join("");
+
+function spark(id, values, max) {
+  const svg = $(id);
+  const v = (values || []).map((x) => (x == null ? 0 : x));
+  if (v.length < 2) { svg.querySelector(".line").setAttribute("d", ""); svg.querySelector(".area").setAttribute("d", ""); return; }
+  const top = Math.max(max || 0, ...v, 1e-9);
+  const pts = v.map((x, i) => [(i / (v.length - 1)) * 100, 30 - (x / top) * 26]);
+  const line = pts.map((p, i) => `${i ? "L" : "M"}${p[0].toFixed(2)},${p[1].toFixed(2)}`).join("");
+  svg.querySelector(".line").setAttribute("d", line);
+  svg.querySelector(".area").setAttribute("d", `${line}L100,32L0,32Z`);
+}
+function setMetric(key, value, unit, sub) {
+  $(`mv-${key}`).innerHTML = value == null ? "–" : `${esc(value)}${unit ? `<small>${esc(unit)}</small>` : ""}`;
+  $(`ms-${key}`).textContent = sub || "";
+}
+
+let lastMetrics = null, metricsFailures = 0, keyWarned = false;
+let reqShowAll = false;   // the Monitor's request table: the last 12, or every one the server keeps (issue #35)
+async function poll() {
+  try {
+    const r = await fetch(reqShowAll ? "/metrics?requests=all" : "/metrics", {headers: headers()});
+    if (r.status === 401) {
+      setPill("error", "API key needed");
+      if (!keyWarned) { keyWarned = true; toast("warn", "API key needed", "This server needs a key: add it under About > Settings.", 6000); }
+    } else if (r.ok) {
+      lastMetrics = await r.json();
+      metricsFailures = 0;
+      render(lastMetrics);
+    } else {
+      throw new Error(`HTTP ${r.status}`);
+    }
+  } catch (e) {
+    if (++metricsFailures === 3) setPill("error", "Server not reachable");
+  }
+  setTimeout(poll, 1000);
+}
+
+function setPill(state, text) {
+  $("pill").dataset.state = state === "error" ? "queued" : state;
+  $("pill-text").textContent = text;
+}
+
+function render(m) {
+  const live = m.live || {}, hw = m.hardware || {}, st = m.hardware_static || {}, eng = m.engine || {}, h = m.history || {};
+  const last = (m.requests || [])[0];
+  // the header pill
+  if (live.state === "reading") {
+    const pct = live.prompt_total ? Math.round((100 * live.prompt_read) / live.prompt_total) : null;
+    setPill("reading", pct != null ? `Reading prompt · ${pct}%` : "Reading prompt");
+  } else if (live.state === "generating") {
+    setPill("generating", `Generating · ${fmt(live.tok_s, 1)} tok/s`);
+  } else {
+    setPill("idle", "Idle");
+  }
+  if (live.queued > 0) setPill("queued", `${live.queued} queued`);
+  if (tab === "monitor") renderMonitor(live, hw, st, eng, h, last, m.requests || [], m.totals, m.requests_kept);
+  if (tab === "about") renderAbout(eng, hw, st);
+}
+
+function renderTotals(t) {
+  if (!t || !t.requests) return "";
+  const since = new Date(t.since * 1000).toLocaleString([], {weekday: "short", hour: "2-digit", minute: "2-digit"});
+  const read = t.prompt_tokens - t.reused;
+  const pSpeed = t.prompt_ms > 0 && read > 0 ? ` at ${fmt(read / (t.prompt_ms / 1000))} tok/s` : "";
+  const oSpeed = t.decode_ms > 0 && t.output_tokens > 0 ? ` at ${fmt(t.output_tokens / (t.decode_ms / 1000), 1)} tok/s` : "";
+  return `Since ${since}: ${fmt(t.requests)} requests · ${fmt(read)} prompt tokens read${pSpeed} (${fmt(t.reused)} reused) · ` +
+         `${fmt(t.output_tokens)} written${oSpeed}`;
+}
+function renderMonitor(live, hw, st, eng, h, last, requests, totals, kept) {
+  // model state
+  const on = live.queued > 0 ? "queued" : live.state;
+  for (const b of document.querySelectorAll("#state-badges .st-badge")) b.classList.toggle("on", b.dataset.s === on || b.dataset.s === live.state);
+  const prog = $("state-progress");
+  let label = "Waiting for a request", detail = "", pct = 0;
+  if (live.state === "reading") {
+    label = "Reading prompt";
+    prog.dataset.tone = "info";
+    if (live.prompt_total) {
+      pct = (100 * live.prompt_read) / live.prompt_total;
+      detail = `${fmt(live.prompt_read)} / ${fmt(live.prompt_total)} tokens · ${fmt(pct)}%`;
+    } else {
+      detail = `${fmt(live.prompt_tokens)} tokens`;
+    }
+  } else if (live.state === "generating") {
+    label = live.phase ? live.phase[0].toUpperCase() + live.phase.slice(1) : "Generating";
+    delete prog.dataset.tone;
+    pct = live.max_tokens ? Math.min(100, (100 * live.generated) / live.max_tokens) : 0;
+    detail = `${fmt(live.generated)} tokens · ${fmt(live.tok_s, 1)} tok/s`;
+  } else if (last) {
+    delete prog.dataset.tone;
+    detail = `last: ${fmt(last.output_tokens)} tokens${last.decode_tok_s ? ` at ${fmt(last.decode_tok_s, 1)} tok/s` : ""}`;
+  }
+  $("state-label").textContent = label;
+  $("state-detail").textContent = detail;
+  $("state-bar").style.width = `${pct}%`;
+
+  // the eight cards
+  const speed = live.state === "generating" ? live.tok_s : last ? last.decode_tok_s : null;
+  setMetric("speed", speed == null ? null : fmt(speed, 1), "tok/s", live.state === "generating" ? "now" : last ? "last request" : "");
+  spark("sp-speed", h.tok_s);
+  setMetric("gpu", hw.gpu_util == null ? null : fmt(hw.gpu_util), "%", st.gpu_name || "");
+  spark("sp-gpu", h.gpu_util, 100);
+  setMetric("vram", hw.gpu_mem_used == null ? null : gb(hw.gpu_mem_used), hw.gpu_mem_total ? `/ ${gb(hw.gpu_mem_total, 0)} GB` : "GB",
+            eng.expert_slots ? `${fmt(eng.expert_slots)} experts cached` : "");
+  spark("sp-vram", h.gpu_mem_used, hw.gpu_mem_total);
+  setMetric("temp", hw.gpu_temp == null ? null : fmt(hw.gpu_temp), "°C", "");
+  spark("sp-temp", h.gpu_temp, 90);
+  setMetric("power", hw.gpu_power == null ? null : fmt(hw.gpu_power), "W", hw.gpu_power_limit ? `of ${fmt(hw.gpu_power_limit)} W limit` : "");
+  spark("sp-power", h.gpu_power, hw.gpu_power_limit);
+  const gen = hw.gpu_pcie_gen_max || hw.gpu_pcie_gen;
+  setMetric("pcie", gen ? `Gen${gen}` : null, hw.gpu_pcie_width ? `x${hw.gpu_pcie_width}` : "",
+            hw.gpu_pcie_rx_mb == null ? "" : `to GPU ${fmt(hw.gpu_pcie_rx_mb, hw.gpu_pcie_rx_mb < 10 ? 1 : 0)} MB/s` +
+            (hw.gpu_pcie_gen && gen && hw.gpu_pcie_gen < gen ? ` · idle Gen${hw.gpu_pcie_gen}` : ""));
+  spark("sp-pcie", h.gpu_pcie_rx_mb);
+  setMetric("cpu", hw.cpu == null ? null : fmt(hw.cpu), "%", st.threads ? `${st.cores ? `${st.cores} cores · ` : ""}${st.threads} threads` : "");
+  spark("sp-cpu", h.cpu, 100);
+  if (hw.disk_read_mb == null) {
+    setMetric("disk", null, "", st.psutil ? "" : "needs psutil (setup installs it)");
+  } else {
+    const big = hw.disk_read_mb >= 1000;
+    setMetric("disk", big ? fmt(hw.disk_read_mb / 1024, 2) : fmt(hw.disk_read_mb, hw.disk_read_mb < 10 ? 1 : 0), big ? "GB/s" : "MB/s",
+              hw.disk_write_mb == null ? "" : `write ${fmt(hw.disk_write_mb, 1)} MB/s`);
+  }
+  spark("sp-disk", h.disk_read_mb);
+
+  // context fill: the running request, else the last one
+  const ctx = eng.max_context || 0;
+  let used = 0;
+  if (live.state !== "idle") used = (live.prompt_tokens || 0) + (live.generated || 0);
+  else if (last) used = (last.prompt_tokens || 0) + (last.output_tokens || 0);
+  const frac = ctx ? Math.min(1, used / ctx) : 0;
+  $("ctx-fill").setAttribute("stroke-dasharray", `${(235.6 * frac).toFixed(1)} 314.2`);
+  $("ctx-fill").style.opacity = 235.6 * frac >= 3 ? "1" : "0";         // a near-zero arc would draw just its round cap
+  $("ctx-pct").textContent = `${Math.round(frac * 100)}%`;
+  $("ctx-sub").textContent = ctx ? `${kfmt(used)} / ${ctxfmt(ctx)}` : "–";
+  const cacheBytes = (eng.expert_cache_mib || 0) * 1048576;
+  $("slots-text").textContent = eng.expert_slots ? `${fmt(eng.expert_slots)} · ${gb(cacheBytes)} GB` : "–";
+  $("slots-bar").style.width = hw.gpu_mem_total ? `${Math.min(100, (100 * cacheBytes) / hw.gpu_mem_total)}%` : "0%";
+  $("ram-text").textContent = hw.ram_total ? `${gb(hw.ram_used)} / ${gb(hw.ram_total, 0)} GB` : "–";
+  const ramPct = hw.ram_total ? (100 * hw.ram_used) / hw.ram_total : 0;
+  $("ram-bar").style.width = `${ramPct}%`;
+  if (ramPct > 92) $("ram-progress").dataset.tone = "danger"; else delete $("ram-progress").dataset.tone;
+  $("temp-text").textContent = hw.gpu_temp == null ? "–" : `${fmt(hw.gpu_temp)} °C`;
+  $("temp-bar").style.width = hw.gpu_temp == null ? "0%" : `${Math.min(100, hw.gpu_temp)}%`;
+
+  // recent requests
+  const body = $("req-body");
+  if (!requests.length) {
+    body.innerHTML = `<tr><td colspan="7" class="muted">No requests yet</td></tr>`;
+  } else {
+    const badge = {stop: ["", "Done"], length: ["", "Max tokens"], cancel: ["st-badge--queued", "Stopped"],
+                   disconnect: ["st-badge--queued", "Closed"], error: ["st-badge--error", "Error"]};
+    body.innerHTML = requests.slice(0, reqShowAll ? requests.length : 12).map((r) => {
+      const [cls, text] = badge[r.finish] || ["", r.finish || "–"];
+      const t = new Date(r.time * 1000).toLocaleTimeString([], {hour: "2-digit", minute: "2-digit", second: "2-digit"});
+      const proj = r.projection == null ? "" : ` <span class="st-badge${r.projection ? " st-badge--reading" : ""}" title="experimental speed projection ${r.projection ? "on" : "off"}">${r.projection ? "ESP" : "stock"}</span>`;
+      return `<tr><td>${esc(t)}</td><td><span class="st-badge ${cls}">${esc(text)}</span>${proj}</td><td class="num">${fmt(r.prompt_tokens)}</td>
+        <td class="num">${fmt(r.reused)}</td><td class="num">${fmt(r.output_tokens)}</td><td class="num">${fmt(r.decode_tok_s, 1)}</td>
+        <td class="num">${fmt(r.duration_s, 1)} s</td></tr>`;
+    }).join("");
+  }
+  const all = $("req-all");
+  kept = kept == null ? requests.length : kept;
+  all.hidden = kept <= 12;
+  all.textContent = reqShowAll ? "Show fewer" : `Show all (${kept})`;
+  $("req-wrap").classList.toggle("all", reqShowAll);
+  $("req-totals").textContent = renderTotals(totals);
+}
+
+function facts(el, rows) {
+  el.innerHTML = rows.filter((r) => r[1] != null && r[1] !== "").map(([k, v, copy]) =>
+    `<dt>${esc(k)}</dt><dd>${copy ? `<code>${esc(v)}</code><button class="st-btn st-btn--icon" data-copy="${esc(v)}" aria-label="Copy">${icon("copy")}</button>` : esc(v)}</dd>`).join("");
+}
+// INFO cvec=project:4-44[:singleL] | add:A-B | 0
+function projectionText(c) {
+  if (!c || c === "0" || c === 0) return null;
+  const [mode, range, single] = String(c).split(":");
+  const [a, b] = (range || "").split("-");
+  return `${mode === "project" ? "Projection" : "Additive"} control vector on layers ${a}–${b}` +
+         `${single ? ` (layer ${single.replace("single", "")}'s direction)` : ""}. Per chat in Sampling. Its package ` +
+         "describes the vector as a refusal-direction projection; measure the speed yourself";
+}
+function renderAbout(eng, hw, st) {
+  const kv = {int8: "8-bit", q4_0: "4-bit (Hadamard-rotated)", fp16: "16-bit"}[eng.kv] || eng.kv;
+  facts($("facts-engine"), [
+    ["Model", eng.model],
+    ["Engine", eng.version ? `v${eng.version}` : "built from source"],
+    ["Context", eng.max_context ? `${fmt(eng.max_context)} tokens` : null],
+    ["KV cache", kv ? `${kv}${eng.kv_resident ? `, streamed: ${fmt(eng.kv_resident)} positions per layer in VRAM, the rest in RAM` : ", all in VRAM"}` : null],
+    ["Experts in VRAM", eng.expert_slots ? `${fmt(eng.expert_slots)} (${gb((eng.expert_cache_mib || 0) * 1048576)} GB)` : null],
+    ["Speculation", eng.spec ? `MTP drafts up to ${Math.max(0, (eng.mtp_max || eng.spec) - 1)} tokens${eng.lookup ? ", prompt lookup on" : ""}` : null],
+    ["Images", eng.images ? "on" : "off"],
+    ["Experimental speed projection", projectionText(eng.cvec)],
+  ]);
+  facts($("facts-hw"), [
+    ["GPU", st.gpu_name ? `${st.gpu_name}${hw.gpu_mem_total ? `, ${gb(hw.gpu_mem_total, 0)} GB` : ""}` : "not readable (NVML)"],
+    ["CPU", st.cpu_name ? `${st.cpu_name}${st.threads ? `, ${st.threads} threads` : ""}` : null],
+    ["RAM", hw.ram_total ? `${gb(hw.ram_total, 0)} GB` : null],
+  ]);
+  const base = location.origin;
+  facts($("facts-api"), [
+    ["OpenAI base URL", `${base}/v1`, true],
+    ["Anthropic base URL", base, true],
+    ["Model name", eng.model, true],
+  ]);
+}
+document.addEventListener("click", (e) => {
+  const b = e.target.closest("[data-copy]");
+  if (b) copyText(b.dataset.copy, b);
+});
+$("req-all").addEventListener("click", () => { reqShowAll = !reqShowAll; if (lastMetrics) render(lastMetrics); });
+
+// ------------------------------------------------------------------ Markdown (escaped first, then formatted)
+function inline(s) {
+  const codes = [];
+  s = s.replace(/`([^`\n]+)`/g, (_, c) => { codes.push(c); return `\u0000${codes.length - 1}\u0000`; });
+  s = esc(s)
+    .replace(/\*\*([^*\n]+)\*\*/g, "<strong>$1</strong>")
+    .replace(/(^|[^*\w])\*([^*\n]+)\*(?![*\w])/g, "$1<em>$2</em>")
+    .replace(/\[([^\]\n]+)\]\((https?:\/\/[^)\s]+)\)/g, '<a href="$2" target="_blank" rel="noopener noreferrer">$1</a>');
+  return s.replace(/\u0000(\d+)\u0000/g, (_, i) => `<code class="inline">${esc(codes[+i])}</code>`);
+}
+function codeBlock(lang, code) {
+  return `<div class="st-code"><div class="st-code__head"><span>${esc(lang || "code")}</span>` +
+    `<button class="st-btn st-btn--icon" data-code-copy aria-label="Copy code">${icon("copy")}</button></div>` +
+    `<pre><code>${esc(code)}</code></pre></div>`;
+}
+function blocks(text) {
+  const out = [], lines = text.split("\n");
+  let para = [], list = null;
+  const flushPara = () => { if (para.length) out.push(`<p>${para.map(inline).join("<br>")}</p>`); para = []; };
+  const flushList = () => { if (list) out.push(`<${list.tag}>${list.items.map((i) => `<li>${inline(i)}</li>`).join("")}</${list.tag}>`); list = null; };
+  for (let i = 0; i < lines.length; i++) {
+    const l = lines[i];
+    let m;
+    if (!l.trim()) { flushPara(); flushList(); continue; }
+    if ((m = l.match(/^(#{1,6})\s+(.*)$/))) { flushPara(); flushList(); out.push(`<${m[1].length <= 2 ? "h3" : "h4"}>${inline(m[2])}</${m[1].length <= 2 ? "h3" : "h4"}>`); continue; }
+    if (/^\s*([-*_])\s*\1\s*\1[\s\1]*$/.test(l)) { flushPara(); flushList(); out.push("<hr>"); continue; }
+    if ((m = l.match(/^>\s?(.*)$/))) { flushPara(); flushList(); out.push(`<blockquote>${inline(m[1])}</blockquote>`); continue; }
+    if (/^\s*\|.*\|\s*$/.test(l) && i + 1 < lines.length && /^\s*\|?[\s:-]+\|[\s|:-]*$/.test(lines[i + 1])) {
+      flushPara(); flushList();
+      const cells = (row) => row.trim().replace(/^\||\|$/g, "").split("|").map((c) => inline(c.trim()));
+      let html = `<table><thead><tr>${cells(l).map((c) => `<th>${c}</th>`).join("")}</tr></thead><tbody>`;
+      i += 2;
+      while (i < lines.length && /^\s*\|.*\|\s*$/.test(lines[i])) html += `<tr>${cells(lines[i++]).map((c) => `<td>${c}</td>`).join("")}</tr>`;
+      i--;
+      out.push(html + "</tbody></table>");
+      continue;
+    }
+    if ((m = l.match(/^\s*(?:[-*+]|(\d+)[.)])\s+(.*)$/))) {
+      flushPara();
+      const tag = m[1] ? "ol" : "ul";
+      if (!list || list.tag !== tag) { flushList(); list = {tag, items: []}; }
+      list.items.push(m[2]);
+      continue;
+    }
+    if (list && /^\s{2,}\S/.test(l)) { list.items[list.items.length - 1] += " " + l.trim(); continue; }
+    flushList();
+    para.push(l);
+  }
+  flushPara(); flushList();
+  return out.join("");
+}
+function markdown(text) {
+  let html = "", rest = text;
+  for (;;) {
+    const m = rest.match(/(^|\n)```([^\n`]*)\n/);
+    if (!m) { html += blocks(rest); break; }
+    html += blocks(rest.slice(0, m.index));
+    rest = rest.slice(m.index + m[0].length);
+    const end = rest.match(/(^|\n)```[ \t]*(\n|$)/);
+    if (!end) { html += codeBlock(m[2].trim(), rest); break; }         // still streaming
+    html += codeBlock(m[2].trim(), rest.slice(0, end.index));
+    rest = rest.slice(end.index + end[0].length);
+  }
+  return html;
+}
+
+// ------------------------------------------------------------------ Chat
+const DEFAULTS = {thinking: "high", temperature: 0.6, top_p: 0.95, top_k: 20, max: "", seed: "", show: true, esp: true};
+let settings = {...DEFAULTS, ...store.get("sampling", {})};
+let messages = store.get("chat", []);
+let attachments = [];                 // {name, url}
+let busy = null;                      // {controller, msg}
+
+function saveChat() {
+  store.set("chat", messages.map((m) => ({...m, images: (m.images || []).map((i) => ({name: i.name})),
+                                           files: (m.files || []).map((f) => ({name: f.name}))})));
+}
+function timeStr(t) { return new Date(t).toLocaleTimeString([], {hour: "2-digit", minute: "2-digit"}); }
+
+function msgEl(m, i) {
+  const el = document.createElement("div");
+  el.className = `st-msg st-msg--${m.role}`;
+  el.dataset.i = i;
+  if (m.role === "user") {
+    if (m.files && m.files.length) {
+      const wrap = document.createElement("div");
+      wrap.className = "msg-images";
+      for (const f of m.files) {
+        const c = document.createElement("span");
+        c.className = "chip";
+        c.innerHTML = icon("attach", "st-icon st-icon--sm");
+        c.append(f.name);
+        wrap.appendChild(c);
+      }
+      el.appendChild(wrap);
+    }
+    if (m.images && m.images.length) {
+      const wrap = document.createElement("div");
+      wrap.className = "msg-images";
+      for (const im of m.images) {
+        if (im.url) { const img = document.createElement("img"); img.src = im.url; img.alt = im.name || "image"; wrap.appendChild(img); }
+        else { const c = document.createElement("span"); c.className = "chip"; c.innerHTML = icon("image", "st-icon st-icon--sm"); c.append(im.name || "image"); wrap.appendChild(c); }
+      }
+      el.appendChild(wrap);
+    }
+    const b = document.createElement("div");
+    b.className = "st-bubble";
+    b.textContent = m.text;
+    el.appendChild(b);
+    const meta = document.createElement("div");
+    meta.className = "st-msg__meta";
+    meta.textContent = `You · ${timeStr(m.time)}`;
+    el.appendChild(meta);
+  } else {
+    el.innerHTML = `<details class="st-collapse" hidden><summary>${icon("thinking", "st-icon st-icon--sm")}<span class="think-title"></span>` +
+      `${icon("chevron", "st-icon st-icon--sm st-chev")}</summary><div class="st-collapse__body thinking"></div></details>` +
+      `<div class="st-bubble"></div><div class="st-msg__meta"><span class="meta-text"></span>` +
+      `<button class="st-btn st-btn--icon" data-msg-copy aria-label="Copy the answer" title="Copy">${icon("copy")}</button></div>`;
+    updateAssistant(el, m, false);
+  }
+  return el;
+}
+function updateAssistant(el, m, streaming) {
+  const det = el.querySelector("details");
+  if (m.reasoning) {
+    det.hidden = false;
+    const thinkingNow = streaming && !m.text;
+    el.querySelector(".think-title").textContent = thinkingNow ? "Thinking…" :
+      m.thinkSecs != null ? `Thought for ${fmt(m.thinkSecs, 1)} s` : "Thoughts";
+    const body = el.querySelector(".thinking");
+    if (det.open || thinkingNow) body.textContent = m.reasoning;
+    else body.dataset.pending = "1";
+    // open while it streams (if wanted), closed once the answer starts - unless the user toggled it themselves
+    if (thinkingNow && settings.show && !det.dataset.touched && !det.open) { det._auto = true; det.open = true; }
+    if (!thinkingNow && det.open && !det.dataset.touched) { det._auto = true; det.open = false; }
+  }
+  const bubble = el.querySelector(".st-bubble");
+  if (m.error) {
+    bubble.innerHTML = `<div class="msg-error"></div>`;
+    bubble.firstChild.textContent = m.error;
+  } else if (!m.text && streaming) {
+    bubble.innerHTML = m.reasoning ? `<span class="muted cursor">Writing</span>` : `<span class="cursor"></span>`;
+  } else {
+    bubble.innerHTML = markdown(m.text || "");
+    if (streaming) bubble.classList.add("cursor"); else bubble.classList.remove("cursor");
+  }
+  el.querySelector(".meta-text").textContent = m.meta || (streaming ? "" : m.stopped ? "Stopped" : "");
+  el.querySelector("[data-msg-copy]").hidden = streaming || !m.text;
+}
+function renderChat() {
+  const chat = $("chat");
+  chat.querySelectorAll(".st-msg").forEach((e) => e.remove());
+  $("chat-empty").hidden = messages.length > 0;
+  messages.forEach((m, i) => chat.appendChild(msgEl(m, i)));
+  scrollDown(true);
+}
+function nearBottom() { const s = $("chat-scroll"); return s.scrollHeight - s.scrollTop - s.clientHeight < 120; }
+function scrollDown(force) { const s = $("chat-scroll"); if (force || nearBottom()) s.scrollTop = s.scrollHeight; }
+
+$("chat").addEventListener("click", (e) => {
+  const cc = e.target.closest("[data-code-copy]");
+  if (cc) { copyText(cc.closest(".st-code").querySelector("pre").textContent, cc); return; }
+  const mc = e.target.closest("[data-msg-copy]");
+  if (mc) { const i = +mc.closest(".st-msg").dataset.i; copyText(messages[i].text, mc); }
+});
+$("chat").addEventListener("toggle", (e) => {
+  const d = e.target;
+  if (d.tagName !== "DETAILS") return;
+  if (d._auto) { d._auto = false; return; }          // our own open/close, not the user's
+  d.dataset.touched = "1";
+  const body = d.querySelector(".thinking");
+  if (d.open && body.dataset.pending) { body.textContent = messages[+d.closest(".st-msg").dataset.i].reasoning; delete body.dataset.pending; }
+}, true);
+
+function apiMessages() {
+  const out = [];
+  for (const m of messages) {
+    if (m.role === "user") {
+      const imgs = (m.images || []).filter((i) => i.url);
+      const text = userText(m);
+      out.push({role: "user", content: imgs.length ? [{type: "text", text},
+        ...imgs.map((i) => ({type: "image_url", image_url: {url: i.url}}))] : text});
+    } else if (m.text && !m.error) {
+      out.push({role: "assistant", content: m.text});
+    }
+  }
+  return out;
+}
+
+function setBusy(on) {
+  $("stop-btn").hidden = !on;
+  $("send-btn").disabled = on;
+  $("composer-hint").textContent = on ? "" : "Shift+Enter: new line";
+}
+
+async function send() {
+  const text = $("input").value.trim();
+  if ((!text && !attachments.length) || busy) return;
+  messages.push({role: "user", text, images: attachments.filter((a) => a.kind !== "file"),
+                 files: attachments.filter((a) => a.kind === "file"), time: Date.now()});
+  attachments = [];
+  renderAttachments();
+  $("input").value = "";
+  autosize();
+  const m = {role: "assistant", text: "", reasoning: "", time: Date.now()};
+  messages.push(m);
+  renderChat();
+  const el = $("chat").lastElementChild;
+  const controller = new AbortController();
+  busy = {controller, msg: m};
+  setBusy(true);
+
+  const body = {model: health.model, messages: apiMessages(), stream: true,
+                reasoning_effort: settings.thinking};
+  if (settings.temperature > 0) {
+    Object.assign(body, {temperature: +settings.temperature, top_p: +settings.top_p, top_k: +settings.top_k});
+  } else {
+    body.temperature = 0;
+  }
+  if (settings.seed) body.seed = +settings.seed;
+  if (settings.max) body.max_tokens = +settings.max;
+  if (projectionLoaded()) body.experimental_speed_projection = !!settings.esp;
+
+  let firstAt = null, thinkStart = null, usage = null, frame = 0;
+  const paint = () => { frame = 0; updateAssistant(el, m, true); scrollDown(); };
+  try {
+    const r = await fetch("/v1/chat/completions", {method: "POST", headers: headers(true), body: JSON.stringify(body),
+                                                   signal: controller.signal});
+    if (!r.ok) {
+      let msg = `HTTP ${r.status}`;
+      try { msg = (await r.json()).error.message || msg; } catch (e) { /* not json */ }
+      if (r.status === 401) msg = "This server needs an API key: add it under About > Settings.";
+      throw new Error(msg);
+    }
+    const reader = r.body.getReader(), dec = new TextDecoder();
+    let buf = "";
+    for (;;) {
+      const {value, done} = await reader.read();
+      if (done) break;
+      buf += dec.decode(value, {stream: true});
+      let nl;
+      while ((nl = buf.indexOf("\n")) >= 0) {
+        const line = buf.slice(0, nl).trim();
+        buf = buf.slice(nl + 1);
+        if (!line.startsWith("data:")) continue;              // ": keep-alive" comments while a long prompt is read
+        const data = line.slice(5).trim();
+        if (data === "[DONE]") continue;
+        let j;
+        try { j = JSON.parse(data); } catch (e) { continue; }
+        if (j.error) throw new Error(j.error.message || "the engine reported an error");
+        if (j.usage) usage = j.usage;
+        const d = (j.choices && j.choices[0] && j.choices[0].delta) || {};
+        if (d.reasoning_content) { if (!firstAt) firstAt = performance.now(); if (!thinkStart) thinkStart = performance.now(); m.reasoning += d.reasoning_content; }
+        if (d.content) {
+          if (!firstAt) firstAt = performance.now();
+          if (thinkStart && m.thinkSecs == null) m.thinkSecs = (performance.now() - thinkStart) / 1000;
+          m.text += d.content;
+        }
+        if (!frame) frame = requestAnimationFrame(paint);
+      }
+    }
+  } catch (e) {
+    if (e.name === "AbortError") m.stopped = true;
+    else { m.error = e.message || String(e); toast("error", "The request failed", m.error, 6000); }
+  }
+  if (thinkStart && m.thinkSecs == null) m.thinkSecs = (performance.now() - thinkStart) / 1000;
+  const n = usage ? usage.completion_tokens : null;
+  if (n && firstAt) {
+    const secs = (performance.now() - firstAt) / 1000;
+    m.meta = `${fmt(n)} tokens${secs > 0.25 ? ` · ${fmt(n / secs, 1)} tok/s` : ""}${m.stopped ? " · stopped" : ""}` +
+             (projectionLoaded() ? (settings.esp ? " · projection on" : " · projection off") : "");
+  } else if (m.stopped) {
+    m.meta = "Stopped";
+  }
+  busy = null;
+  setBusy(false);
+  if (frame) cancelAnimationFrame(frame);
+  updateAssistant(el, m, false);
+  saveChat();
+  scrollDown();
+}
+
+$("composer").onsubmit = (e) => { e.preventDefault(); send(); };
+$("stop-btn").onclick = () => { if (busy) busy.controller.abort(); };
+$("input").addEventListener("keydown", (e) => {
+  if (e.key === "Enter" && !e.shiftKey && !e.isComposing) { e.preventDefault(); send(); }
+});
+function autosize() { const t = $("input"); t.style.height = "auto"; t.style.height = `${Math.min(t.scrollHeight, innerHeight * 0.4)}px`; }
+$("input").addEventListener("input", autosize);
+
+$("new-btn").onclick = () => {
+  if (busy) { toast("warn", "Still writing", "Stop the answer first."); return; }
+  if (!messages.length) return;
+  const backup = messages;
+  messages = [];
+  saveChat();
+  renderChat();
+  toast("info", "New chat", "The last one was cleared.", 6000, {label: "Undo", run: () => { messages = backup; saveChat(); renderChat(); }});
+};
+$("export-btn").onclick = () => {
+  if (!messages.length) { toast("info", "Nothing to save yet"); return; }
+  const md = messages.map((m) => m.role === "user" ? `## You\n\n${m.text}\n` :
+    `## ${health.model}\n\n${m.reasoning ? `<details><summary>Thinking</summary>\n\n${m.reasoning}\n\n</details>\n\n` : ""}${m.text || m.error || ""}\n`).join("\n");
+  const a = document.createElement("a");
+  a.href = URL.createObjectURL(new Blob([md], {type: "text/markdown"}));
+  a.download = `strata-chat-${new Date().toISOString().slice(0, 16).replace(/[:T]/g, "-")}.md`;
+  a.click();
+  setTimeout(() => URL.revokeObjectURL(a.href), 5000);
+};
+
+// pictures and text files: the attach button, dropping them on the chat, or pasting a picture (issue #30)
+const TEXT_EXT = /\.(txt|md|markdown|rst|tex|py|pyi|ipynb|js|mjs|cjs|ts|tsx|jsx|vue|svelte|json|jsonl|csv|tsv|log|ya?ml|toml|ini|cfg|conf|env|xml|html?|css|scss|less|c|cc|cpp|cxx|h|hh|hpp|cu|cuh|rs|go|java|kt|kts|swift|rb|php|pl|lua|r|jl|scala|sql|sh|bash|zsh|fish|ps1|psm1|bat|cmd|diff|patch|gradle|cmake|mk|dockerfile|gitignore|proto|graphql)$/i;
+const MAX_TEXT_FILE = 512 * 1024;
+function isTextFile(f) {
+  return f.type.startsWith("text/") || /json|xml|javascript|yaml|toml|x-sh|x-python/.test(f.type) ||
+         TEXT_EXT.test(f.name) || /(^|[\\/])(makefile|dockerfile|readme|license)$/i.test(f.name);
+}
+function addFiles(files) {
+  for (const f of files) {
+    if (f.type.startsWith("image/")) {
+      if (!health.images) { toast("warn", "Pictures are off", "This model was set up for text only."); continue; }
+      if (f.size > 20e6) { toast("warn", "Picture too large", `${f.name} is over 20 MB.`); continue; }
+      const r = new FileReader();
+      r.onload = () => { attachments.push({kind: "image", name: f.name || "pasted image", url: r.result}); renderAttachments(); };
+      r.readAsDataURL(f);
+      continue;
+    }
+    if (!isTextFile(f)) { toast("warn", "Not a text file", `${f.name}: attach text files (code, notes, logs, data)${health.images ? " or pictures" : ""}.`); continue; }
+    if (f.size > MAX_TEXT_FILE) { toast("warn", "File too large", `${f.name} is over 512 KB.`); continue; }
+    const r = new FileReader();
+    r.onload = () => {
+      const text = String(r.result);
+      if (text.includes("\u0000")) { toast("warn", "Not a text file", `${f.name} looks like a binary file.`); return; }
+      attachments.push({kind: "file", name: f.name, text});
+      renderAttachments();
+    };
+    r.readAsText(f);
+  }
+}
+// a file's text in the message, fenced with more backticks than it contains itself
+function fileBlock(f) {
+  const longest = Math.max(2, ...(f.text.match(/`+/g) || []).map((s) => s.length));
+  const fence = "`".repeat(longest + 1);
+  return `File: ${f.name}\n${fence}\n${f.text}\n${fence}`;
+}
+function userText(m) {
+  const files = (m.files || []).filter((f) => f.text != null);
+  return [m.text, ...files.map(fileBlock)].filter((s) => s).join("\n\n");
+}
+function renderAttachments() {
+  const box = $("attachments");
+  box.hidden = !attachments.length;
+  box.innerHTML = "";
+  attachments.forEach((a, i) => {
+    const c = document.createElement("span");
+    c.className = "chip";
+    c.innerHTML = icon(a.kind === "file" ? "attach" : "image", "st-icon st-icon--sm");
+    c.append(a.name);
+    const x = document.createElement("button");
+    x.type = "button"; x.className = "st-btn st-btn--icon"; x.setAttribute("aria-label", "Remove");
+    x.innerHTML = icon("trash");
+    x.onclick = () => { attachments.splice(i, 1); renderAttachments(); };
+    c.appendChild(x);
+    box.appendChild(c);
+  });
+}
+$("attach-btn").onclick = () => $("file").click();
+// drop files on the chat or the message box
+for (const id of ["chat", "composer"]) {
+  const el = $(id);
+  el.addEventListener("dragover", (e) => {
+    if (![...(e.dataTransfer || {}).types || []].includes("Files")) return;
+    e.preventDefault();
+    $("composer").classList.add("dragging");
+  });
+  el.addEventListener("dragleave", () => $("composer").classList.remove("dragging"));
+  el.addEventListener("drop", (e) => {
+    $("composer").classList.remove("dragging");
+    if (!e.dataTransfer || !e.dataTransfer.files.length) return;
+    e.preventDefault();
+    addFiles(e.dataTransfer.files);
+    $("input").focus();
+  });
+}
+$("file").onchange = () => { addFiles($("file").files); $("file").value = ""; };
+$("input").addEventListener("paste", (e) => {
+  if (!health.images) return;
+  const files = [...(e.clipboardData || {}).files || []].filter((f) => f.type.startsWith("image/"));
+  if (files.length) { e.preventDefault(); addFiles(files); }
+});
+
+// ------------------------------------------------------------------ the sampling drawer
+function openDrawer(open) {
+  $("drawer").dataset.open = String(open);
+  $("drawer").setAttribute("aria-hidden", String(!open));
+  $("scrim").hidden = !open;
+  if (open) { loadDrawer(); loadShared(); }
+}
+function loadDrawer(s = settings) {
+  for (const b of $("s-thinking").children) b.setAttribute("aria-checked", String(b.dataset.v === s.thinking));
+  $("s-temp").value = s.temperature; $("s-topp").value = s.top_p; $("s-topk").value = s.top_k;
+  $("s-max").value = s.max; $("s-seed").value = s.seed;
+  $("s-show").setAttribute("aria-checked", String(!!s.show));
+  $("s-esp").setAttribute("aria-checked", String(s.esp !== false));
+  $("esp-row").hidden = !projectionLoaded();
+  $("s-share").setAttribute("aria-checked", String(sharedOn));
+  outputs();
+}
+// "Use for other apps too": the server keeps these settings as every client's defaults (GET/POST /settings)
+let sharedOn = false;
+async function loadShared() {
+  try {
+    const r = await fetch("/settings", {headers: headers()});
+    if (r.ok) sharedOn = !!(await r.json()).shared;
+  } catch (e) { /* an older server: the switch just stays off */ }
+  $("s-share").setAttribute("aria-checked", String(sharedOn));
+}
+function sharedDefaults(s) {
+  const d = {reasoning_effort: s.thinking, temperature: +s.temperature};
+  if (+s.temperature > 0) Object.assign(d, {top_p: +s.top_p, top_k: +s.top_k});
+  if (s.seed) d.seed = +s.seed;
+  if (s.max) d.max_tokens = +s.max;
+  if (projectionLoaded()) d.experimental_speed_projection = s.esp !== false;
+  return d;
+}
+async function saveShared(on, s) {
+  const r = await fetch("/settings", {method: "POST", headers: headers(true),
+                                      body: JSON.stringify({defaults: on ? sharedDefaults(s) : null})});
+  if (!r.ok) {
+    let msg = `HTTP ${r.status}`;
+    try { msg = (await r.json()).error.message || msg; } catch (e) { /* not json */ }
+    throw new Error(msg);
+  }
+  sharedOn = !!(await r.json()).shared;
+}
+// the engine was started with the experimental-speed-projection control vector (INFO cvec=...)
+function projectionLoaded() {
+  const c = lastMetrics && lastMetrics.engine ? lastMetrics.engine.cvec : 0;
+  return !!c && c !== "0";
+}
+function outputs() {
+  const t = +$("s-temp").value;
+  $("o-temp").textContent = t === 0 ? "0 · greedy" : t.toFixed(2);
+  $("o-topp").textContent = (+$("s-topp").value).toFixed(2);
+  $("o-topk").textContent = $("s-topk").value;
+  const sel = [...$("s-thinking").children].find((b) => b.getAttribute("aria-checked") === "true");
+  $("o-thinking").textContent = sel ? {none: "answers right away", low: "short", medium: "medium", high: "thorough (default)"}[sel.dataset.v] : "";
+  for (const id of ["s-topp", "s-topk"]) $(id).disabled = t === 0;
+}
+for (const b of $("s-thinking").children) b.onclick = () => { for (const x of $("s-thinking").children) x.setAttribute("aria-checked", String(x === b)); outputs(); };
+for (const id of ["s-temp", "s-topp", "s-topk"]) $(id).oninput = outputs;
+$("s-show").onclick = () => $("s-show").setAttribute("aria-checked", String($("s-show").getAttribute("aria-checked") !== "true"));
+$("s-esp").onclick = () => $("s-esp").setAttribute("aria-checked", String($("s-esp").getAttribute("aria-checked") !== "true"));
+$("s-share").onclick = () => $("s-share").setAttribute("aria-checked", String($("s-share").getAttribute("aria-checked") !== "true"));
+$("s-reset").onclick = () => loadDrawer(DEFAULTS);
+$("s-apply").onclick = async () => {
+  const sel = [...$("s-thinking").children].find((b) => b.getAttribute("aria-checked") === "true");
+  settings = {thinking: sel ? sel.dataset.v : "high", temperature: +$("s-temp").value, top_p: +$("s-topp").value,
+              top_k: +$("s-topk").value, max: $("s-max").value.trim(), seed: $("s-seed").value.trim(),
+              show: $("s-show").getAttribute("aria-checked") === "true",
+              esp: $("s-esp").getAttribute("aria-checked") === "true"};
+  store.set("sampling", settings);
+  const share = $("s-share").getAttribute("aria-checked") === "true";
+  openDrawer(false);
+  if (share || sharedOn) {
+    try {
+      await saveShared(share, settings);
+      toast("success", "Sampling saved", share ? "Other apps (omp, API clients) use these settings from their next request."
+                                               : "Other apps use their own settings again.");
+    } catch (e) {
+      toast("error", "Saved here, but not for other apps", e.message, 6000);
+    }
+    return;
+  }
+  toast("success", "Sampling saved", settings.temperature === 0 ? "Greedy: the same question gives the same answer." : "");
+};
+$("sampling-btn").onclick = () => openDrawer(true);
+$("drawer-close").onclick = () => openDrawer(false);
+$("scrim").onclick = () => openDrawer(false);
+document.addEventListener("keydown", (e) => { if (e.key === "Escape" && $("drawer").dataset.open === "true") openDrawer(false); });
+
+// ------------------------------------------------------------------ start
+setBusy(false);
+renderChat();
+const startQuestion = new URLSearchParams(location.search).get("q");   // /?q=... starts a chat (a shortcut)
+if (startQuestion) history.replaceState(null, "", location.pathname + location.hash);
+loadHealth().then(() => { if (startQuestion) { $("input").value = startQuestion; send(); } });
+showTab(location.hash.slice(1) || "chat");
+poll();
@@ -0,0 +1,128 @@
+/* Strata UI — components. Requires tokens.css. */
+*,*::before,*::after{box-sizing:border-box}
+html,body{margin:0;background:var(--st-bg);color:var(--st-ink);font-family:var(--st-font);font-size:var(--st-fs-base);line-height:var(--st-lh-body);-webkit-font-smoothing:antialiased}
+a{color:var(--st-accent-text);text-decoration:none} a:hover{text-decoration:underline}
+:focus-visible{outline:2px solid var(--st-focus);outline-offset:2px}
+
+/* icon — <svg class="st-icon"><use href="icons/sprite.svg#i-chat"/></svg> */
+.st-icon{width:20px;height:20px;flex:none;color:inherit}
+.st-icon--sm{width:16px;height:16px} .st-icon--lg{width:24px;height:24px}
+
+/* header */
+.st-header{display:flex;align-items:center;gap:var(--st-s-4);height:60px;padding:0 var(--st-s-6);background:var(--st-surface);border-bottom:1px solid var(--st-line)}
+.st-header__brand{font-weight:900;font-size:var(--st-fs-lg);letter-spacing:-.01em}
+.st-header__spacer{flex:1}
+
+/* status pill */
+.st-pill{display:inline-flex;align-items:center;gap:8px;height:30px;padding:0 12px;border-radius:var(--st-r-pill);border:1px solid var(--st-line);background:var(--st-surface-2);font-size:var(--st-fs-sm);font-weight:500;color:var(--st-ink-soft);font-variant-numeric:tabular-nums}
+.st-pill__dot{width:8px;height:8px;border-radius:50%;background:var(--st-ink-muted)}
+.st-pill[data-state="generating"] .st-pill__dot{background:var(--st-accent);box-shadow:0 0 0 4px var(--st-accent-tint)}
+.st-pill[data-state="reading"] .st-pill__dot{background:var(--st-info);box-shadow:0 0 0 4px var(--st-info-tint)}
+.st-pill[data-state="queued"] .st-pill__dot{background:var(--st-warn)}
+
+/* tabs */
+.st-tabs{display:flex;gap:4px;padding:4px;background:var(--st-surface-2);border:1px solid var(--st-line);border-radius:var(--st-r-md)}
+.st-tab{display:inline-flex;align-items:center;gap:8px;height:36px;padding:0 14px;border:0;border-radius:10px;background:transparent;color:var(--st-ink-muted);font:500 var(--st-fs-md)/1 var(--st-font);cursor:pointer}
+.st-tab:hover{color:var(--st-ink)}
+.st-tab[aria-selected="true"]{background:var(--st-surface);color:var(--st-ink);font-weight:700;box-shadow:var(--st-sh-card)}
+
+/* card + metric */
+.st-card{background:var(--st-surface);border:1px solid var(--st-line);border-radius:var(--st-r-lg);box-shadow:var(--st-sh-card);padding:var(--st-s-5)}
+.st-metric{display:flex;flex-direction:column;gap:10px}
+.st-metric__label{display:flex;align-items:center;gap:8px;font-size:var(--st-fs-sm);font-weight:500;color:var(--st-ink-muted)}
+.st-metric__value{font-weight:900;font-size:var(--st-fs-stat);line-height:1;letter-spacing:-.01em;font-variant-numeric:tabular-nums}
+.st-metric__unit{font-weight:500;font-size:var(--st-fs-md);color:var(--st-ink-muted);margin-left:4px}
+.st-metric__spark{width:100%;height:32px;color:var(--st-accent)}
+
+/* progress + gauge */
+.st-progress{height:8px;border-radius:var(--st-r-pill);background:var(--st-surface-2);border:1px solid var(--st-line-soft);overflow:hidden}
+.st-progress__bar{height:100%;border-radius:inherit;background:var(--st-accent);transition:width var(--st-dur) var(--st-ease)}
+.st-progress[data-tone="info"] .st-progress__bar{background:var(--st-info)}
+.st-progress[data-tone="warn"] .st-progress__bar{background:var(--st-warn)}
+.st-progress[data-tone="danger"] .st-progress__bar{background:var(--st-danger)}
+/* gauge: see README for SVG markup (stroke-dasharray on a 270° arc) */
+.st-gauge{position:relative;width:120px;height:120px}
+.st-gauge__track{stroke:var(--st-surface-2)} .st-gauge__fill{stroke:var(--st-accent)}
+
+/* table */
+.st-table{width:100%;border-collapse:collapse;font-size:var(--st-fs-md);font-variant-numeric:tabular-nums}
+.st-table th{text-align:left;font-weight:500;font-size:var(--st-fs-xs);letter-spacing:.04em;text-transform:uppercase;color:var(--st-ink-muted);padding:10px 12px;border-bottom:1px solid var(--st-line)}
+.st-table td{padding:12px;border-bottom:1px solid var(--st-line-soft);color:var(--st-ink-soft)}
+.st-table tr:hover td{background:var(--st-surface-2)}
+.st-table .num{text-align:right}
+
+/* badges */
+.st-badge{display:inline-flex;align-items:center;gap:6px;height:24px;padding:0 10px;border-radius:var(--st-r-pill);font-size:var(--st-fs-xs);font-weight:700;background:var(--st-surface-2);color:var(--st-ink-muted);border:1px solid var(--st-line)}
+.st-badge::before{content:"";width:6px;height:6px;border-radius:50%;background:currentColor}
+.st-badge--reading{background:var(--st-info-tint);color:var(--st-info-text);border-color:transparent}
+.st-badge--generating{background:var(--st-accent-tint);color:var(--st-accent-text);border-color:transparent}
+.st-badge--queued{background:var(--st-warn-tint);color:var(--st-warn-text);border-color:transparent}
+.st-badge--error{background:var(--st-danger-tint);color:var(--st-danger-text);border-color:transparent}
+
+/* chat */
+.st-msg{display:flex;flex-direction:column;gap:6px;max-width:720px}
+.st-msg--user{align-self:flex-end;align-items:flex-end}
+.st-bubble{padding:12px 16px;border-radius:var(--st-r-lg);font-size:var(--st-fs-base);line-height:1.55;color:var(--st-ink-soft)}
+.st-msg--user .st-bubble{background:var(--st-accent);color:var(--st-accent-ink);border-bottom-right-radius:6px}
+.st-msg--assistant .st-bubble{background:var(--st-surface);border:1px solid var(--st-line);border-bottom-left-radius:6px}
+.st-msg__meta{font-size:var(--st-fs-xs);color:var(--st-ink-muted);display:flex;gap:8px;align-items:center}
+
+/* collapsible — <details class="st-collapse"><summary>…</summary><div class="st-collapse__body">…</div></details> */
+.st-collapse{border:1px solid var(--st-line);border-radius:var(--st-r-md);background:var(--st-surface-2)}
+.st-collapse>summary{list-style:none;display:flex;align-items:center;gap:8px;padding:10px 14px;cursor:pointer;font-size:var(--st-fs-sm);font-weight:500;color:var(--st-ink-muted)}
+.st-collapse>summary::-webkit-details-marker{display:none}
+.st-collapse>summary .st-chev{margin-left:auto;transition:transform var(--st-dur) var(--st-ease)}
+.st-collapse[open]>summary .st-chev{transform:rotate(180deg)}
+.st-collapse__body{padding:0 14px 14px;font-size:var(--st-fs-sm);color:var(--st-ink-muted);line-height:1.6}
+
+/* code */
+.st-code{border-radius:var(--st-r-md);background:var(--st-code-bg);color:var(--st-code-ink);overflow:hidden}
+.st-code__head{display:flex;align-items:center;justify-content:space-between;padding:8px 8px 8px 14px;font:500 var(--st-fs-xs)/1 var(--st-font);color:#9aa3ab;border-bottom:1px solid rgba(255,255,255,.06)}
+.st-code pre{margin:0;padding:14px;overflow:auto;font:400 13px/1.6 var(--st-font-mono)}
+.st-code .st-btn--icon{color:#9aa3ab} .st-code .st-btn--icon:hover{background:rgba(255,255,255,.08);color:#fff}
+
+/* composer */
+.st-composer{display:flex;flex-direction:column;gap:8px;padding:10px;background:var(--st-surface);border:1px solid var(--st-line);border-radius:var(--st-r-lg);box-shadow:var(--st-sh-bar)}
+.st-composer:focus-within{border-color:var(--st-focus)}
+.st-composer textarea{border:0;outline:0;resize:none;background:transparent;color:var(--st-ink);font:400 var(--st-fs-base)/1.5 var(--st-font);padding:6px 8px;min-height:48px}
+.st-composer__bar{display:flex;align-items:center;gap:6px}
+.st-composer__bar .spacer{flex:1}
+
+/* drawer */
+.st-drawer{position:fixed;top:0;right:0;bottom:0;width:360px;max-width:100vw;background:var(--st-surface);border-left:1px solid var(--st-line);box-shadow:var(--st-sh-pop);display:flex;flex-direction:column;transform:translateX(100%);transition:transform 220ms var(--st-ease)}
+.st-drawer[data-open="true"]{transform:none}
+.st-drawer__head{display:flex;align-items:center;justify-content:space-between;padding:18px 20px;border-bottom:1px solid var(--st-line)}
+.st-drawer__body{padding:20px;display:flex;flex-direction:column;gap:22px;overflow:auto}
+
+/* buttons */
+.st-btn{display:inline-flex;align-items:center;justify-content:center;gap:8px;height:42px;padding:0 18px;border-radius:var(--st-r-md);border:0;font:700 var(--st-fs-md)/1 var(--st-font);cursor:pointer;transition:background var(--st-dur),transform 120ms}
+.st-btn:active{transform:translateY(1px)}
+.st-btn--primary{background:var(--st-accent);color:var(--st-accent-ink);box-shadow:0 6px 16px rgba(16,185,129,.24),inset 0 1px 0 rgba(255,255,255,.18)}
+.st-btn--primary:hover{background:var(--st-accent-hover)}
+.st-btn--secondary{background:transparent;color:var(--st-ink);border:1.5px solid var(--st-line)}
+.st-btn--secondary:hover{background:var(--st-surface-2)}
+.st-btn--danger{background:var(--st-danger-tint);color:var(--st-danger-text)}
+.st-btn--icon{width:36px;height:36px;padding:0;border-radius:10px;background:transparent;color:var(--st-ink-muted)}
+.st-btn--icon:hover{background:var(--st-surface-2);color:var(--st-ink)}
+.st-btn:disabled{opacity:.45;cursor:not-allowed}
+
+/* form controls */
+.st-field{display:flex;flex-direction:column;gap:8px}
+.st-label{display:flex;justify-content:space-between;font-size:var(--st-fs-sm);font-weight:500;color:var(--st-ink-soft)}
+.st-label output{font-variant-numeric:tabular-nums;color:var(--st-ink-muted)}
+.st-input{height:44px;padding:0 14px;border-radius:var(--st-r-md);border:1.5px solid var(--st-line);background:var(--st-surface);color:var(--st-ink);font:400 var(--st-fs-md) var(--st-font)}
+.st-input:focus{outline:0;border-color:var(--st-focus);box-shadow:0 0 0 3px color-mix(in srgb,var(--st-focus) 20%,transparent)}
+.st-slider{width:100%;accent-color:var(--st-accent)}
+.st-toggle{position:relative;width:40px;height:24px;border-radius:var(--st-r-pill);border:0;background:var(--st-line);cursor:pointer;transition:background var(--st-dur)}
+.st-toggle::after{content:"";position:absolute;top:3px;left:3px;width:18px;height:18px;border-radius:50%;background:#fff;box-shadow:0 1px 2px rgba(0,0,0,.2);transition:transform var(--st-dur) var(--st-ease)}
+.st-toggle[aria-checked="true"]{background:var(--st-accent)}
+.st-toggle[aria-checked="true"]::after{transform:translateX(16px)}
+
+/* toast / alert */
+.st-toast{display:flex;align-items:flex-start;gap:12px;padding:14px 16px;border-radius:var(--st-r-md);background:var(--st-surface);border:1px solid var(--st-line);box-shadow:var(--st-sh-pop);font-size:var(--st-fs-md);color:var(--st-ink-soft);max-width:420px}
+.st-toast__title{font-weight:700;color:var(--st-ink)}
+.st-toast--info .st-icon{color:var(--st-info)}
+.st-toast--success .st-icon{color:var(--st-accent)}
+.st-toast--warn .st-icon{color:var(--st-warn)}
+.st-toast--error .st-icon{color:var(--st-danger)}
+.st-toasts{position:fixed;right:20px;bottom:20px;display:flex;flex-direction:column;gap:10px;z-index:50}
@@ -0,0 +1,93 @@
+Copyright 2021 The Outfit Project Authors (https://github.com/Outfitio/Outfit-Fonts)
+
+This Font Software is licensed under the SIL Open Font License, Version 1.1.
+This license is copied below, and is also available with a FAQ at:
+https://scripts.sil.org/OFL
+
+
+-----------------------------------------------------------
+SIL OPEN FONT LICENSE Version 1.1 - 26 February 2007
+-----------------------------------------------------------
+
+PREAMBLE
+The goals of the Open Font License (OFL) are to stimulate worldwide
+development of collaborative font projects, to support the font creation
+efforts of academic and linguistic communities, and to provide a free and
+open framework in which fonts may be shared and improved in partnership
+with others.
+
+The OFL allows the licensed fonts to be used, studied, modified and
+redistributed freely as long as they are not sold by themselves. The
+fonts, including any derivative works, can be bundled, embedded, 
+redistributed and/or sold with any software provided that any reserved
+names are not used by derivative works. The fonts and derivatives,
+however, cannot be released under any other type of license. The
+requirement for fonts to remain under this license does not apply
+to any document created using the fonts or their derivatives.
+
+DEFINITIONS
+"Font Software" refers to the set of files released by the Copyright
+Holder(s) under this license and clearly marked as such. This may
+include source files, build scripts and documentation.
+
+"Reserved Font Name" refers to any names specified as such after the
+copyright statement(s).
+
+"Original Version" refers to the collection of Font Software components as
+distributed by the Copyright Holder(s).
+
+"Modified Version" refers to any derivative made by adding to, deleting,
+or substituting -- in part or in whole -- any of the components of the
+Original Version, by changing formats or by porting the Font Software to a
+new environment.
+
+"Author" refers to any designer, engineer, programmer, technical
+writer or other person who contributed to the Font Software.
+
+PERMISSION & CONDITIONS
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of the Font Software, to use, study, copy, merge, embed, modify,
+redistribute, and sell modified and unmodified copies of the Font
+Software, subject to the following conditions:
+
+1) Neither the Font Software nor any of its individual components,
+in Original or Modified Versions, may be sold by itself.
+
+2) Original or Modified Versions of the Font Software may be bundled,
+redistributed and/or sold with any software, provided that each copy
+contains the above copyright notice and this license. These can be
+included either as stand-alone text files, human-readable headers or
+in the appropriate machine-readable metadata fields within text or
+binary files as long as those fields can be easily viewed by the user.
+
+3) No Modified Version of the Font Software may use the Reserved Font
+Name(s) unless explicit written permission is granted by the corresponding
+Copyright Holder. This restriction only applies to the primary font name as
+presented to the users.
+
+4) The name(s) of the Copyright Holder(s) or the Author(s) of the Font
+Software shall not be used to promote, endorse or advertise any
+Modified Version, except to acknowledge the contribution(s) of the
+Copyright Holder(s) and the Author(s) or with their explicit written
+permission.
+
+5) The Font Software, modified or unmodified, in part or in whole,
+must be distributed entirely under this license, and must not be
+distributed under any other license. The requirement for fonts to
+remain under this license does not apply to any document created
+using the Font Software.
+
+TERMINATION
+This license becomes null and void if any of the above conditions are
+not met.
+
+DISCLAIMER
+THE FONT SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT
+OF COPYRIGHT, PATENT, TRADEMARK, OR OTHER RIGHT. IN NO EVENT SHALL THE
+COPYRIGHT HOLDER BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+INCLUDING ANY GENERAL, SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL
+DAMAGES, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+FROM, OUT OF THE USE OR INABILITY TO USE THE FONT SOFTWARE OR FROM
+OTHER DEALINGS IN THE FONT SOFTWARE.
@@ -0,0 +1,213 @@
+<!doctype html>
+<html lang="en" data-theme="light">
+<head>
+<meta charset="utf-8">
+<meta name="viewport" content="width=device-width, initial-scale=1">
+<title>Strata</title>
+<link rel="stylesheet" href="/web/tokens.css">
+<link rel="stylesheet" href="/web/components.css">
+<link rel="stylesheet" href="/web/app.css">
+<script>
+  // the theme before the first paint: the saved choice, else the system's
+  try {
+    const t = localStorage.getItem("strata.theme") ||
+      (matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light");
+    document.documentElement.dataset.theme = t;
+  } catch (e) {}
+</script>
+</head>
+<body>
+<header class="st-header">
+  <span class="st-header__brand">Strata</span>
+  <nav class="st-tabs" role="tablist" aria-label="Views">
+    <button class="st-tab" role="tab" id="tab-btn-chat" aria-controls="view-chat" aria-selected="true" data-tab="chat">
+      <svg class="st-icon st-icon--sm"><use href="/web/sprite.svg#i-chat"/></svg>Chat</button>
+    <button class="st-tab" role="tab" id="tab-btn-monitor" aria-controls="view-monitor" aria-selected="false" data-tab="monitor">
+      <svg class="st-icon st-icon--sm"><use href="/web/sprite.svg#i-activity"/></svg>Monitor</button>
+    <button class="st-tab" role="tab" id="tab-btn-about" aria-controls="view-about" aria-selected="false" data-tab="about">
+      <svg class="st-icon st-icon--sm"><use href="/web/sprite.svg#i-info"/></svg>About</button>
+  </nav>
+  <span class="st-header__spacer"></span>
+  <span class="st-pill" id="pill" data-state="idle" role="status" aria-live="polite">
+    <span class="st-pill__dot"></span><span id="pill-text">Connecting&hellip;</span></span>
+  <button class="st-btn st-btn--icon" id="theme-btn" title="Light / dark" aria-label="Switch between light and dark">
+    <svg class="st-icon"><use id="theme-icon" href="/web/sprite.svg#i-moon"/></svg></button>
+</header>
+
+<main>
+  <!-- ============================ Chat ============================ -->
+  <section class="view view--chat" id="view-chat" role="tabpanel" aria-labelledby="tab-btn-chat">
+    <div class="chat-scroll" id="chat-scroll">
+      <div class="chat" id="chat">
+        <div class="chat-empty" id="chat-empty">
+          <div class="chat-empty__title">Ask anything</div>
+          <div class="chat-empty__sub" id="chat-empty-sub">The model runs on this PC. Nothing leaves it.</div>
+        </div>
+      </div>
+    </div>
+    <div class="composer-wrap">
+      <form class="st-composer" id="composer">
+        <div class="attachments" id="attachments" hidden></div>
+        <textarea id="input" rows="2" placeholder="Ask anything&hellip;" aria-label="Message"></textarea>
+        <div class="st-composer__bar">
+          <button type="button" class="st-btn st-btn--icon" id="attach-btn" title="Attach a text file (or drop it here)" aria-label="Attach a file">
+            <svg class="st-icon"><use href="/web/sprite.svg#i-attach"/></svg></button>
+          <input type="file" id="file" multiple hidden>
+          <button type="button" class="st-btn st-btn--icon" id="new-btn" title="New chat" aria-label="New chat">
+            <svg class="st-icon"><use href="/web/sprite.svg#i-new-chat"/></svg></button>
+          <button type="button" class="st-btn st-btn--icon" id="export-btn" title="Save this chat as Markdown" aria-label="Save this chat">
+            <svg class="st-icon"><use href="/web/sprite.svg#i-download"/></svg></button>
+          <button type="button" class="st-btn st-btn--icon" id="sampling-btn" title="Sampling and thinking" aria-label="Sampling and thinking">
+            <svg class="st-icon"><use href="/web/sprite.svg#i-settings"/></svg></button>
+          <span class="spacer"></span>
+          <span class="composer-hint" id="composer-hint"></span>
+          <button type="button" class="st-btn st-btn--secondary" id="stop-btn" hidden>
+            <svg class="st-icon st-icon--sm"><use href="/web/sprite.svg#i-stop"/></svg>Stop</button>
+          <button type="submit" class="st-btn st-btn--primary st-btn--send" id="send-btn" aria-label="Send">
+            <svg class="st-icon"><use href="/web/sprite.svg#i-send"/></svg></button>
+        </div>
+      </form>
+    </div>
+  </section>
+
+  <!-- ============================ Monitor ============================ -->
+  <section class="view view--monitor" id="view-monitor" role="tabpanel" aria-labelledby="tab-btn-monitor" hidden>
+    <div class="monitor">
+      <div class="st-card state-card">
+        <div class="state-card__head">
+          <span class="card-title">Model state</span>
+          <span class="badges" id="state-badges">
+            <span class="st-badge" data-s="idle">Idle</span>
+            <span class="st-badge st-badge--reading" data-s="reading">Reading</span>
+            <span class="st-badge st-badge--generating" data-s="generating">Generating</span>
+            <span class="st-badge st-badge--queued" data-s="queued">Queued</span>
+            <span class="st-badge st-badge--error" data-s="error">Error</span>
+          </span>
+        </div>
+        <div class="state-card__row"><span id="state-label">Waiting for a request</span><span class="muted" id="state-detail"></span></div>
+        <div class="st-progress" id="state-progress"><div class="st-progress__bar" id="state-bar" style="width:0%"></div></div>
+      </div>
+
+      <div class="metrics" id="metrics">
+        <!-- filled by app.js: Speed, GPU load, VRAM, GPU temp, Power, PCIe, CPU, Disk read -->
+      </div>
+
+      <div class="monitor-row">
+        <div class="st-card ctx-card">
+          <span class="card-title">Context fill</span>
+          <div class="ctx-gauge">
+            <svg class="st-gauge" viewBox="0 0 120 120" aria-hidden="true">
+              <circle class="st-gauge__track" cx="60" cy="60" r="50" fill="none" stroke-width="10" stroke-linecap="round"
+                      stroke-dasharray="235.6 314.2" transform="rotate(135 60 60)"/>
+              <circle class="st-gauge__fill" id="ctx-fill" cx="60" cy="60" r="50" fill="none" stroke-width="10"
+                      stroke-linecap="round" stroke-dasharray="0 314.2" transform="rotate(135 60 60)"/>
+            </svg>
+            <div class="ctx-gauge__label"><span class="ctx-gauge__pct" id="ctx-pct">0%</span><span class="muted" id="ctx-sub">&ndash;</span></div>
+          </div>
+          <div class="bar-row"><span>Experts in VRAM</span><span class="muted" id="slots-text">&ndash;</span></div>
+          <div class="st-progress"><div class="st-progress__bar" id="slots-bar" style="width:0%"></div></div>
+          <div class="bar-row"><span>System RAM</span><span class="muted" id="ram-text">&ndash;</span></div>
+          <div class="st-progress" id="ram-progress"><div class="st-progress__bar" id="ram-bar" style="width:0%"></div></div>
+          <div class="bar-row"><span>GPU temperature</span><span class="muted" id="temp-text">&ndash;</span></div>
+          <div class="st-progress" data-tone="warn"><div class="st-progress__bar" id="temp-bar" style="width:0%"></div></div>
+        </div>
+        <div class="st-card req-card">
+          <div class="req-head"><span class="card-title">Recent requests</span>
+            <button class="st-btn st-btn--secondary" id="req-all" hidden>Show all</button></div>
+          <div class="table-wrap" id="req-wrap">
+            <table class="st-table">
+              <thead><tr><th>Time</th><th>Status</th><th class="num">Prompt</th><th class="num">Reused</th><th class="num">Output</th>
+                <th class="num">Tok/s</th><th class="num">Duration</th></tr></thead>
+              <tbody id="req-body"><tr><td colspan="7" class="muted">No requests yet</td></tr></tbody>
+            </table>
+          </div>
+          <p class="muted req-totals" id="req-totals"></p>
+        </div>
+      </div>
+    </div>
+  </section>
+
+  <!-- ============================ About ============================ -->
+  <section class="view view--about" id="view-about" role="tabpanel" aria-labelledby="tab-btn-about" hidden>
+    <div class="about">
+      <div class="st-card">
+        <span class="card-title">Model and engine</span>
+        <dl class="facts" id="facts-engine"></dl>
+      </div>
+      <div class="st-card">
+        <span class="card-title">This PC</span>
+        <dl class="facts" id="facts-hw"></dl>
+      </div>
+      <div class="st-card">
+        <span class="card-title">Connect your tools</span>
+        <p class="muted">Any OpenAI- or Anthropic-compatible client works with these addresses.</p>
+        <dl class="facts" id="facts-api"></dl>
+      </div>
+      <div class="st-card">
+        <span class="card-title">Settings</span>
+        <div class="st-field">
+          <label class="st-label" for="api-key">API key <output>only if the server was started with one</output></label>
+          <input class="st-input" id="api-key" type="password" autocomplete="off" placeholder="Not needed">
+        </div>
+        <div class="toggle-row"><span>Dark theme</span>
+          <button class="st-toggle" id="dark-toggle" role="switch" aria-checked="false" aria-label="Dark theme"></button></div>
+        <p class="muted small">Chats, settings and the key are kept in this browser only.
+          <a href="https://github.com/Niko1221/Strata" target="_blank" rel="noopener noreferrer">Strata on GitHub</a></p>
+      </div>
+    </div>
+  </section>
+</main>
+
+<!-- ============================ Sampling drawer ============================ -->
+<aside class="st-drawer" id="drawer" data-open="false" aria-label="Sampling and thinking" aria-hidden="true">
+  <div class="st-drawer__head"><span class="card-title">Sampling</span>
+    <button class="st-btn st-btn--icon" id="drawer-close" aria-label="Close">
+      <svg class="st-icon"><use href="/web/sprite.svg#i-chevron"/></svg></button></div>
+  <div class="st-drawer__body">
+    <div class="st-field">
+      <label class="st-label" for="s-thinking">Thinking <output id="o-thinking"></output></label>
+      <div class="seg" id="s-thinking" role="radiogroup" aria-label="Thinking">
+        <button type="button" role="radio" data-v="none">Off</button><button type="button" role="radio" data-v="low">Low</button>
+        <button type="button" role="radio" data-v="medium">Medium</button><button type="button" role="radio" data-v="high">High</button>
+      </div>
+    </div>
+    <div class="st-field">
+      <label class="st-label" for="s-temp">Temperature <output id="o-temp"></output></label>
+      <input class="st-slider" type="range" id="s-temp" min="0" max="1.5" step="0.05">
+      <span class="muted small">0 = always the most likely word (exact, repeatable)</span>
+    </div>
+    <div class="st-field">
+      <label class="st-label" for="s-topp">Top-p <output id="o-topp"></output></label>
+      <input class="st-slider" type="range" id="s-topp" min="0.05" max="1" step="0.05">
+    </div>
+    <div class="st-field">
+      <label class="st-label" for="s-topk">Top-k <output id="o-topk"></output></label>
+      <input class="st-slider" type="range" id="s-topk" min="1" max="64" step="1">
+    </div>
+    <div class="st-field">
+      <label class="st-label" for="s-max">Max tokens <output>empty = until done</output></label>
+      <input class="st-input" id="s-max" type="number" min="1" step="1" placeholder="Until done">
+    </div>
+    <div class="st-field">
+      <label class="st-label" for="s-seed">Seed <output>empty = random</output></label>
+      <input class="st-input" id="s-seed" type="number" min="1" step="1" placeholder="Random">
+    </div>
+    <div class="toggle-row"><span>Show thinking<br><span class="muted small">expanded while it streams</span></span>
+      <button class="st-toggle" id="s-show" role="switch" aria-checked="true" aria-label="Show thinking"></button></div>
+    <div class="toggle-row" id="esp-row" hidden><span>Experimental speed projection<br><span class="muted small">the
+      engine's control vector; off = the stock model. Switching reads the chat again once</span></span>
+      <button class="st-toggle" id="s-esp" role="switch" aria-checked="true" aria-label="Experimental speed projection"></button></div>
+    <div class="toggle-row"><span>Use for other apps too<br><span class="muted small">omp and other API clients get
+      these settings for anything they don't set themselves</span></span>
+      <button class="st-toggle" id="s-share" role="switch" aria-checked="false" aria-label="Use for other apps too"></button></div>
+    <div class="drawer-actions">
+      <button class="st-btn st-btn--secondary" id="s-reset" type="button">Reset</button>
+      <button class="st-btn st-btn--primary" id="s-apply" type="button">Apply</button>
+    </div>
+  </div>
+</aside>
+<div class="scrim" id="scrim" hidden></div>
+<div class="st-toasts" id="toasts" aria-live="polite"></div>
+<script src="/web/app.js"></script>
+</body>
+</html>
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+<symbol id="i-sun" viewBox="0 0 24 24" fill="none"><path opacity="0.12" d="M16 12C16 14.209 14.209 16 12 16C9.791 16 8 14.209 8 12C8 9.791 9.791 8 12 8C14.209 8 16 9.791 16 12Z" fill="currentColor"></path><path d="M16 12C16 14.209 14.209 16 12 16C9.791 16 8 14.209 8 12C8 9.791 9.791 8 12 8C14.209 8 16 9.791 16 12Z" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M12 2V4M12 20V22M4.93 4.93L6.34 6.34M17.66 17.66L19.07 19.07M2 12H4M20 12H22M4.93 19.07L6.34 17.66M17.66 6.34L19.07 4.93" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path></symbol>
+<symbol id="i-moon" viewBox="0 0 24 24" fill="none"><path opacity="0.12" d="M21 12.79A9 9 0 1 1 11.21 3A7 7 0 0 0 21 12.79Z" fill="currentColor"></path><path d="M21 12.79A9 9 0 1 1 11.21 3A7 7 0 0 0 21 12.79Z" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path></symbol>
+</svg>
@@ -0,0 +1,87 @@
+/* Strata UI — tokens. Derived from the Gainsty design system.
+   Light is default; dark via <html data-theme="dark">.
+   Values marked (derived) are not in the Gainsty source and were added for the app. */
+
+/* Outfit (SIL Open Font License 1.1, serve/web/fonts/OFL.txt): one variable font, weights 100-900 */
+@font-face{font-family:"Outfit";src:url("/fonts/outfit-latin-wght.woff2") format("woff2-variations");font-weight:100 900;font-display:swap;unicode-range:U+0000-00FF,U+0131,U+0152-0153,U+02BB-02BC,U+02C6,U+02DA,U+02DC,U+0304,U+0308,U+0329,U+2000-206F,U+20AC,U+2122,U+2191,U+2193,U+2212,U+2215,U+FEFF,U+FFFD}
+@font-face{font-family:"Outfit";src:url("/fonts/outfit-latin-ext-wght.woff2") format("woff2-variations");font-weight:100 900;font-display:swap;unicode-range:U+0100-02BA,U+02BD-02C5,U+02C7-02CC,U+02CE-02D7,U+02DD-02FF,U+0304,U+0308,U+0329,U+1D00-1DBF,U+1E00-1E9F,U+1EF2-1EFF,U+2020,U+20A0-20AB,U+20AD-20C0,U+2113,U+2C60-2C7F,U+A720-A7FF}
+
+:root{
+  /* type */
+  --st-font:"Outfit",system-ui,-apple-system,"Segoe UI",Roboto,sans-serif;
+  --st-font-mono:ui-monospace,"SF Mono","Cascadia Code",Consolas,"Liberation Mono",monospace; /* (derived) */
+  --st-fs-xs:12px;  --st-fs-sm:13px; --st-fs-md:14px; --st-fs-base:16px;
+  --st-fs-lg:18px;  --st-fs-xl:24px; --st-fs-stat:32px; --st-fs-display:40px;
+  --st-fw-regular:400; --st-fw-medium:500; --st-fw-bold:700; --st-fw-black:900;
+  --st-lh-tight:1.1; --st-lh-body:1.55;
+
+  /* spacing (4pt) */
+  --st-s-1:4px; --st-s-2:8px; --st-s-3:12px; --st-s-4:16px; --st-s-5:20px;
+  --st-s-6:24px; --st-s-8:32px; --st-s-10:40px; --st-s-12:48px; --st-s-16:64px;
+
+  /* radius */
+  --st-r-sm:8px; --st-r-md:14px; --st-r-lg:20px; --st-r-xl:24px; --st-r-pill:9999px;
+
+  /* motion (derived) */
+  --st-ease:cubic-bezier(.2,.7,.2,1); --st-dur:160ms;
+
+  /* color — light */
+  --st-bg:#f9fafb;
+  --st-surface:#ffffff;
+  --st-surface-2:#f7f7f7;
+  --st-line:#dee5ee;
+  --st-line-soft:#eeeeee;
+  --st-ink:#221e1f;
+  --st-ink-soft:#303030;
+  --st-ink-muted:#6f6f6f;        /* Gainsty #7a7a7a darkened slightly for 4.5:1 on white */
+  --st-icon:#2F384C;             /* Anron default */
+
+  --st-accent:#10b981;
+  --st-accent-hover:#0c8a60;
+  --st-accent-ink:#ffffff;
+  --st-accent-tint:#ecfdf5;
+  --st-accent-text:#0c8a60;      /* accent used as text on light */
+  --st-focus:#405de6;
+
+  --st-info:#0f97ff;     --st-info-tint:#e8f4ff;    --st-info-text:#0668c2;
+  --st-warn:#e39b0b;     --st-warn-tint:#fff6e0;    --st-warn-text:#8a5a00;   /* (derived) */
+  --st-danger:#e0283f;   --st-danger-tint:#fdecee;  --st-danger-text:#b01a2e; /* (derived) */
+
+  --st-code-bg:#1b1f23;  --st-code-ink:#e6e9ec;  /* code blocks stay dark in both themes (derived) */
+
+  --st-sh-card:0 18px 7px rgba(0,0,0,.01),0 10px 6px rgba(0,0,0,.02),0 4px 4px rgba(0,0,0,.03),0 1px 2px rgba(0,0,0,.04);
+  --st-sh-bar:0 54px 21px rgba(0,0,0,.01),0 30px 18px rgba(0,0,0,.03),0 13px 13px rgba(0,0,0,.05),0 3px 7px rgba(0,0,0,.06);
+  --st-sh-pop:0 10px 40px rgba(15,23,42,.08),0 1px 3px rgba(15,23,42,.06);
+  color-scheme:light;
+}
+
+/* color — dark (all derived) */
+[data-theme="dark"]{
+  --st-bg:#0e1113;
+  --st-surface:#161a1d;
+  --st-surface-2:#1d2226;
+  --st-line:#2a3036;
+  --st-line-soft:#22272c;
+  --st-ink:#eef1f3;
+  --st-ink-soft:#c7cdd3;
+  --st-ink-muted:#8e979f;
+  --st-icon:#c7cdd3;
+
+  --st-accent:#34d399;
+  --st-accent-hover:#4ade80;
+  --st-accent-ink:#052e20;
+  --st-accent-tint:rgba(52,211,153,.12);
+  --st-accent-text:#4ade80;
+  --st-focus:#7b93ff;
+
+  --st-info:#3aa9ff;     --st-info-tint:rgba(58,169,255,.12);  --st-info-text:#7cc4ff;
+  --st-warn:#f5b53d;     --st-warn-tint:rgba(245,181,61,.12);  --st-warn-text:#f8c96a;
+  --st-danger:#ff5a6e;   --st-danger-tint:rgba(255,90,110,.12);--st-danger-text:#ff8a98;
+
+  --st-code-bg:#0a0c0e;  --st-code-ink:#e6e9ec;
+
+  --st-sh-card:0 1px 2px rgba(0,0,0,.4);
+  --st-sh-bar:0 12px 32px rgba(0,0,0,.45),0 2px 6px rgba(0,0,0,.35);
+  --st-sh-pop:0 16px 48px rgba(0,0,0,.55),0 1px 3px rgba(0,0,0,.4);
+  color-scheme:dark;
+}
@@ -21,6 +21,8 @@ What the first run does (each step is skipped when it is already done):

 Options: --family qwen|swift, --model Q2_0|IQ2_XS|IQ3_XXS|IQ3_S, --context 32768, --vision yes|no|gpu|cpu, --port 8080, --yes (recommended
 answers, no questions), --setup (install another model / change settings instead of starting), --no-start,
+--host 0.0.0.0 --api-key KEY (reach it from other devices on your network), --experimental-speed-projection on|off
+(EXPERIMENTAL, off by default),
 --models-dir DIR, --gguf-dir DIR (use GGUF files you already have), --build (compile instead of the ready-made
 engine), --check (only check this PC).
 """
@@ -28,6 +30,7 @@ from __future__ import annotations

 import argparse
 import ctypes
+import hashlib
 import json
 import os
 import platform
@@ -54,8 +57,8 @@ PREBUILT_ASSET = "strata-windows-x64.zip" if WIN else "strata-linux-x64.zip"
 # the CUDA libraries the ready-made engine loads (the same CUDA 13.0 it is built with), from NVIDIA's pip packages
 CUDA_WHEELS = ["nvidia-cublas==13.0.2.14", "nvidia-cuda-runtime==13.0.96"]
 MIN_DRIVER = 580                       # CUDA 13.0
-MIN_ENGINE = (0, 1, 6)                 # KV streaming (--kv-resident), v0.1.5; its drafter fallback, v0.1.6
-PY_PACKAGES = ["numpy", "jinja2", "regex", "pyyaml", "tqdm", "requests", "cmake", "ninja", "pillow"]
+MIN_ENGINE = (0, 1, 14)                # v0.1.14: no host CUDA call inside a verify window (#31); v0.1.13: --prefill auto
+PY_PACKAGES = ["numpy", "jinja2", "regex", "pyyaml", "tqdm", "requests", "cmake", "ninja", "pillow", "psutil"]

 MODELS = {
    "Q2_0": {"about": "2-bit, the fastest", "download_gb": 66.4, "ram_gb": 48, "arena_gb": 34.0},
@@ -86,6 +89,8 @@ FAMILIES = {
              "license": "Swift Open License 1.0: https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF"},
 }
 MMPROJ = "mmproj-Qwen3.8-Flash-Next-BF16.gguf"
+# EXPERIMENTAL, off by default (setup asks): a control vector shipped with the repository, see its README
+ESP_VECTOR = ROOT / "data" / "experimental-speed-projection" / "Qwen3.8-Flash-Next-experimental-speed-projection.gguf"
 # the image encoder on the GPU (~1.2 GB at 1024 image tokens) warms up before the engine starts, so the engine
 # sizes its expert slots around it and the default reserve (700 MiB) is enough; engines before 0.1.2 need more
 VISION = {"gpu": {"max_tokens": 1024, "reserve_mib": 700},
@@ -244,6 +249,7 @@ def find_nvcc():
            cands += [str(p / "bin" / "nvcc.exe") for p in sorted(base.iterdir(), reverse=True)]
    else:
        cands += [str(p / "bin" / "nvcc") for p in sorted(Path("/usr/local").glob("cuda*"), reverse=True)]
+        cands += [str(p / "bin" / "nvcc") for p in sorted(Path("/opt").glob("cuda*"), reverse=True)]   # Arch (#46)
    best = (None, None)
    for c in dict.fromkeys(cands):                     # every toolkit found; the newest wins
        if c and Path(c).exists():
@@ -471,7 +477,13 @@ def update_installed_engine(url_base) -> None:
    meta_text = info.read_text()
    meta = json.loads(meta_text)
    ver = tuple(int(x) for x in str(meta.get("version", "0")).split(".")[:3] if x.isdigit())
-    if meta.get("source") == "local" or ver >= MIN_ENGINE:
+    local = meta.get("source") == "local"
+    vision = meta.get("vision") or "none"
+    if local:                                          # compiled here: is it older than the source (a git pull)?
+        if meta.get("src") == source_hash(ENGINE_SOURCES) and \
+                (vision == "none" or meta.get("vision_src") == source_hash(VISION_SOURCES)):
+            return
+    elif ver >= MIN_ENGINE:
        return
    try:                                               # a running engine cannot be replaced (Windows keeps it locked)
        for x in (EXE, VEXE):
@@ -479,9 +491,17 @@ def update_installed_engine(url_base) -> None:
                with open(eng / x, "r+b"):
                    pass
    except OSError:
-        warn(f"engine {meta.get('version')} is in use: close the model window and run this again to update it")
+        warn(f"engine {meta.get('version') or ''} is in use: close the model window and run this again to update it")
        return
    gpu = gpu_info()
+    if local:
+        try:                                           # a failed compile must not stop the model from starting
+            if gpu is None:
+                raise RuntimeError("no NVIDIA GPU found")
+            build_engine(gpu, vision, False, get_llama_cpp())
+        except (Exception, SystemExit) as e:
+            warn(f"could not compile the updated engine{'' if isinstance(e, SystemExit) else f' ({e})'}: starting the installed one")
+        return
    new = None
    if gpu is not None:
        try:
@@ -531,8 +551,9 @@ def install_build_tools(gpu, yes):
    else:
        apt = shutil.which("apt-get")
        if apt is None:
-            fail("automatic install is only done on Ubuntu/Debian",
-                 "install g++ and the CUDA Toolkit 13 (https://developer.nvidia.com/cuda-downloads), then run it again")
+            fail("missing: " + " and ".join(missing) + " (the automatic install is only done on Ubuntu/Debian)",
+                 "install them with your distribution's packages (Arch: pacman -S base-devel cuda; nvcc is found on "
+                 "PATH, in /usr/local/cuda* and in /opt/cuda*), then run it again")
        if not have_cc:
            run(["sudo", "apt-get", "install", "-y", "build-essential"])
        if nvcc is None or cuda_v < need_cuda:
@@ -563,35 +584,61 @@ def cmake_build(src, bdir, target, defs, vcvars, bat_name):
    conf = [cmake, "-G", "Ninja", f"-DCMAKE_MAKE_PROGRAM={ninja}", "-S", str(src), "-B", str(bdir),
            "-DCMAKE_BUILD_TYPE=Release", *defs]
    build = [cmake, "--build", str(bdir), "--target", target, "-j", str(max(2, (os.cpu_count() or 4) // 2))]
+    # A failed build is tried once more: CUDA 13.0's ptxas now and then fails to parse a PTX file it just wrote, and
+    # the same command then gets past it (issue #45); a second attempt only compiles what is still missing.
    if WIN:
        bat = ROOT / bat_name
        q = lambda c: " ".join(f'"{x}"' if " " in str(x) else str(x) for x in c)  # noqa: E731
-        bat.write_text(f'@echo off\r\ncall "{vcvars}" >nul\r\n{q(conf)} || exit /b 1\r\n{q(build)} || exit /b 1\r\n',
+        bat.write_text(f'@echo off\r\ncall "{vcvars}" >nul\r\n{q(conf)} || exit /b 1\r\n{q(build)} && exit /b 0\r\n'
+                       f'echo   (the build stopped - trying it once more)\r\n{q(build)} || exit /b 1\r\n',
                       encoding="utf-8")
        run(["cmd", "/c", str(bat)])
    else:
        run(conf)
-        run(build)
+        if run(build, check=False).returncode != 0:
+            say("  (the build stopped - trying it once more)")
+            run(build)
+
+
+ENGINE_SOURCES = ("CMakeLists.txt", "src", "include", "third_party/ggml")
+VISION_SOURCES = ("tools/vision",)
+
+
+def source_hash(parts) -> str:
+    """A fingerprint of the files a compiled engine is built from, kept in engine/BUILD.json: when a `git pull`
+    changes them, the engine is compiled again (issue #31)."""
+    h = hashlib.sha256(LLAMA_CPP_COMMIT.encode())
+    for part in parts:
+        base = ROOT / part
+        for f in [base] if base.is_file() else sorted(x for x in base.rglob("*") if x.is_file()):
+            h.update(f.relative_to(ROOT).as_posix().encode() + b"\0" + f.read_bytes().replace(b"\r\n", b"\n"))
+    return h.hexdigest()[:16]


 def build_engine(gpu, vision, yes, llama) -> Path:
-    """Compile the engine (and, for images, the encoder) for this GPU; the results go to engine/."""
+    """Compile the engine (and, for images, the encoder) for this GPU; the results go to engine/.  A compiled
+    engine whose source files changed since (a `git pull`) is compiled again: only the changed files, a few minutes."""
    eng = ROOT / "engine"
    eng.mkdir(exist_ok=True)
    stamp = eng / "BUILD.json"
    meta = json.loads(stamp.read_text()) if stamp.exists() else {}
    want_vision = vision != "none"
-    if meta.get("source") == "local" and (eng / EXE).exists() and (not want_vision or (eng / VEXE).exists()):
+    local = meta.get("source") == "local"
+    src, vsrc = source_hash(ENGINE_SOURCES), source_hash(VISION_SOURCES)
+    engine_ok = local and (eng / EXE).exists() and meta.get("src") == src
+    vision_ok = not want_vision or ((eng / VEXE).exists() and (not local or meta.get("vision_src") == vsrc))
+    if engine_ok and vision_ok:
        ok("engine already built for this PC")
        return eng
    nvcc, vcvars = install_build_tools(gpu, yes)
-    if not (eng / EXE).exists() or meta.get("source") != "local":
-        say("  Compiling the Strata engine for your GPU (10-20 minutes, once) ...")
+    if not engine_ok:
+        say("  The engine's source changed: compiling it again (only what changed, a few minutes) ..."
+            if local and (eng / EXE).exists() else "  Compiling the Strata engine for your GPU (10-20 minutes, once) ...")
        cmake_build(ROOT, ROOT / "build", "strata",
                    ["-DSTRATA_ENABLE_CUDA=ON", "-DSTRATA_BUILD_TESTS=OFF", f"-DCMAKE_CUDA_ARCHITECTURES={gpu['arch']}",
                     f"-DCMAKE_CUDA_COMPILER={nvcc}", f"-DSTRATA_GGML_DIR={llama}"], vcvars, "build-strata.bat")
        shutil.copy2(ROOT / "build" / EXE, eng / EXE)
-    if want_vision and not (eng / VEXE).exists():
+    if not vision_ok:
        say("  Compiling the image encoder" + (" with CUDA (10-20 minutes, once) ..." if vision == "gpu" else " ..."))
        defs = [f"-DLLAMA_DIR={llama}", f"-DSTRATA_VISION_CUDA={'ON' if vision == 'gpu' else 'OFF'}"]
        if vision == "gpu":
@@ -600,8 +647,9 @@ def build_engine(gpu, vision, yes, llama) -> Path:
        shutil.copy2(ROOT / "build-vision" / "bin" / VEXE, eng / VEXE)
    bindir = Path(nvcc).parent                            # the toolkit's own libraries (bin, bin/x64, lib64)
    dirs = [str(d) for d in (bindir, bindir / "x64", bindir.parent / "lib64") if d.is_dir()]
-    stamp.write_text(json.dumps({"source": "local", "archs": [int(gpu["arch"])], "vision": vision,
-                                 "cuda_dirs": dirs}, indent=1))
+    stamp.write_text(json.dumps({"source": "local", "version": source_version(), "archs": [int(gpu["arch"])],
+                                 "vision": vision,
+                                 "cuda_dirs": dirs, "src": src, "vision_src": vsrc if want_vision else None}, indent=1))
    ok(f"engine compiled: {eng / EXE}")
    return eng

@@ -611,8 +659,61 @@ def installed_configs():
    return sorted(ROOT.glob("strata-*.json"), key=lambda p: p.stat().st_mtime, reverse=True)


+def source_version() -> str:
+    """The engine version the source tree builds (CMakeLists.txt's project version)."""
+    m = re.search(r"project\(strata VERSION ([\d.]+)", (ROOT / "CMakeLists.txt").read_text(encoding="utf-8"))
+    return m.group(1) if m else "0"
+
+
+def engine_version(exe: Path) -> tuple:
+    """The version in the engine folder's BUILD.json, or else the one compiled into the binary.  A locally compiled
+    engine is not necessarily the source's version: when compiling a `git pull` fails, the previous engine is kept
+    (issue #49)."""
+    try:
+        meta = json.loads((Path(exe).parent / "BUILD.json").read_text())
+    except (OSError, ValueError):
+        meta = {}
+    v = str(meta.get("version") or "")
+    if not v:                                          # the version compiled into the binary: 0.1.13 and newer
+        try:                                           # carry it, so a binary without it is older
+            m = re.search(rb"engine=(\d+\.\d+\.\d+)\n", Path(exe).read_bytes())
+            v = m.group(1).decode() if m else "0.1.12"
+        except OSError:
+            v = "0"
+    return tuple(int(x) for x in v.split(".")[:3] if x.isdigit())
+
+
+def is_wsl() -> bool:
+    return sys.platform.startswith("linux") and "microsoft" in platform.uname().release.lower()
+
+
+def upgrade_config(cfg_path: Path, cfg: dict) -> dict:
+    """Configs written before v0.1.13 read prompts in fixed 2048-token chunks; the engine now picks the chunk
+    itself (`--prefill auto`: up to 8192, as the free VRAM allows - about 2x faster on long prompts).  Under WSL,
+    KV streaming is dropped: its RAM copy must be pinned, and the driver pins only about 1 GB there."""
+    a = cfg.get("args", [])
+    changed = False
+    ver = engine_version(cfg["exe"]) if "--prefill" in a else (0, 0, 0)
+    if "--prefill" in a and a[a.index("--prefill") + 1] == "2048" and ver >= (0, 1, 13):
+        a[a.index("--prefill") + 1] = "auto"
+        changed = True
+        ok("prompt reading: the engine now picks its chunk size (--prefill auto)")
+    elif "--prefill" in a and a[a.index("--prefill") + 1] == "auto" and (0, 0, 0) < ver < (0, 1, 13):
+        a[a.index("--prefill") + 1] = "2048"           # an older engine kept after a failed update (issue #49)
+        changed = True
+        warn(f"the installed engine is {'.'.join(map(str, ver))}: prompts are read in 2048-token chunks until it is updated")
+    if is_wsl() and "--kv-resident" in a:
+        i = a.index("--kv-resident")
+        del a[i:i + 2]
+        changed = True
+        ok("WSL: KV streaming off (the driver pins only about 1 GB of RAM); the KV cache stays in VRAM")
+    if changed:
+        cfg_path.write_text(json.dumps(cfg, indent=1), encoding="utf-8")
+    return cfg
+
+
 def start(cfg_path: Path, port: int | None, open_browser=True) -> int:
-    cfg = json.loads(cfg_path.read_text(encoding="utf-8-sig"))
+    cfg = upgrade_config(cfg_path, json.loads(cfg_path.read_text(encoding="utf-8-sig")))
    missing = [p for p in [cfg["exe"], *[a for a in cfg["args"] if a.endswith(".gguf")]] if not Path(p).exists()]
    if missing:
        fail(f"{cfg_path.name} refers to missing files: {missing[0]}", "run it again with --setup to repair")
@@ -647,9 +748,18 @@ def main() -> int:
    ap.add_argument("--family", choices=list(FAMILIES), help="qwen = Qwen3.8-Flash-Next, swift = Swift 1.5")
    ap.add_argument("--model", choices=list(MODELS))
    ap.add_argument("--context", type=int)
+    ap.add_argument("--kv", choices=["int8", "q4_0"],
+                    help="KV cache precision above 8K context: int8 (default) or q4_0 (half the memory, a little less "
+                         "precise)")
    ap.add_argument("--vision", choices=["yes", "no", "none", "gpu", "cpu"],
                    help="let the model read images (yes = the encoder on the GPU)")
-    ap.add_argument("--port", type=int, default=8080)
+    ap.add_argument("--experimental-speed-projection", metavar="on|off|GGUF",
+                    help="EXPERIMENTAL, off by default: the control vector in data/experimental-speed-projection "
+                         "(or another GGUF) as a projection on layers 4-44; see docs/DETAILS.md")
+    ap.add_argument("--port", type=int, help="the server's port (default: the one the install was set up with, 8080 for a new one)")
+    ap.add_argument("--host", help="where the server listens: 127.0.0.1 = this PC only (default), 0.0.0.0 = also other "
+                                   "devices on your network (issue #26; set --api-key too)")
+    ap.add_argument("--api-key", help="require this key from clients (recommended with --host 0.0.0.0)")
    ap.add_argument("--models-dir", default=str(ROOT / "models"), help="where the GGUF files go (~70 GB)")
    ap.add_argument("--gguf-dir", help="use GGUF files you already have (a folder with the two shards)")
    ap.add_argument("--yes", action="store_true", help="accept the recommended answers")
@@ -661,6 +771,7 @@ def main() -> int:
    ap.add_argument("--check", action="store_true", help="only check this PC and exit")
    ap.add_argument("--skip-build", action="store_true", help=argparse.SUPPRESS)
    a = ap.parse_args()
+    port = a.port or 8080                              # a new install's port (issue #32: --port for an existing one)

    say("Strata - Qwen3.8-Flash-Next on a normal PC (NVIDIA GPU + system RAM + CPU)")

@@ -670,14 +781,14 @@ def main() -> int:
        if not a.build:
            update_installed_engine(a.prebuilt)
        if len(have) == 1:
-            return start(have[0], None)
+            return start(have[0], a.port)
        say()
        for i, c in enumerate(have, 1):
            say(f"  {i}) {json.loads(c.read_text(encoding='utf-8-sig')).get('model_name', c.stem)}")
        say(f"  {len(have) + 1}) install another model / change settings")
        pick = int(ask("Which one?", [str(i) for i in range(1, len(have) + 2)], "1", a.yes))
        if pick <= len(have):
-            return start(have[pick - 1], None)
+            return start(have[pick - 1], a.port)

    # ---- 1. the PC
    step(1, "checking your PC")
@@ -696,7 +807,14 @@ def main() -> int:
        warn("less than 12 GB of VRAM: Strata will run, but most experts stay on the CPU and it will be slow")
    ram = ram_gb()
    cpu, avx2, avx512 = cpu_info()
-    ok(f"RAM: {ram:.0f} GB")
+    need = min(d["ram_gb"] for d in MODELS.values())
+    if ram < need - 4 and not a.check:
+        # every model keeps ALL its experts in RAM (34+ GB); VRAM only holds a copy of the most-used ones, so a
+        # bigger GPU does not lower this
+        fail(f"RAM: {ram:.0f} GB - the smallest model (Q2_0 / IQ2_XS) needs about {need} GB",
+             "Strata keeps all of the model's experts in RAM (34-50 GB, whatever the GPU) and the GPU holds a copy "
+             "of the most-used ones: it needs 48 GB of RAM or more")
+    ok(f"RAM: {ram:.0f} GB" if ram >= need - 4 else f"RAM: {ram:.0f} GB (less than the {need} GB the smallest model needs)")
    ok(f"CPU: {cpu} ({'AVX-512' if avx512 else 'AVX2' if avx2 else 'no AVX2'})")
    if not avx2:
        fail("this CPU has no AVX2; Strata needs at least AVX2")
@@ -752,6 +870,17 @@ def main() -> int:
             "+ the context): using 128K")
        ctx = 131072
    ok(f"context: {ctx} tokens")
+    # the KV cache (the model's memory of the conversation): 8-bit, or 4-bit after a Hadamard rotation (PR #21)
+    kv = "fp16" if ctx <= 8192 else (a.kv or "int8")
+    if ctx > 8192 and not a.kv and not a.yes:
+        say()
+        say("  KV cache precision (the model's memory of the conversation):")
+        say("  1) 8-bit   (recommended: what every published number was measured with)")
+        say("  2) 4-bit   half the memory (about 4% faster at 128K), but measurably less precise on long")
+        say("             documents; long-context lookups (needle tests) still pass")
+        kv = ["int8", "q4_0"][int(ask("KV cache?", ["1", "2"], "1", a.yes)) - 1]
+    if ctx > 8192:
+        ok(f"KV cache: {'8-bit' if kv == 'int8' else '4-bit (Hadamard-rotated)'}")
    if a.vision:
        vision = {"yes": "gpu", "no": "none"}.get(a.vision, a.vision)
    else:
@@ -760,6 +889,25 @@ def main() -> int:
        say("  download and keeps ~1.4 GB of VRAM free for the image encoder, so text is a few % slower.")
        vision = "gpu" if ask("Do you want images?", ["y", "n"], "n", a.yes) == "y" else "none"
    ok("images: " + {"none": "off", "gpu": "on", "cpu": "on (encoder on the CPU)"}[vision])
+    # EXPERIMENTAL: the experimental-speed-projection control vector (data/experimental-speed-projection), off unless
+    # chosen here; with it loaded, the web app and the API switch it off per request
+    esp = None
+    esp_choice = (a.experimental_speed_projection or "").strip()
+    if family == "qwen":
+        if not esp_choice:
+            say()
+            say("  EXPERIMENTAL - speed projection: a small control vector applied while the model runs (layers 4-44).")
+            say("  It changes how the model answers: its package describes it as a refusal-direction projection (the")
+            say("  model declines far fewer requests). Off unless you choose it; when on, the web app can switch it off")
+            say("  per chat. Details: data/experimental-speed-projection/README.md")
+            esp_choice = "on" if ask("Turn on the experimental speed projection?", ["y", "n"], "n", a.yes) == "y" else "off"
+        if esp_choice.lower() not in ("off", "no", "n", "0"):
+            esp = ESP_VECTOR if esp_choice.lower() in ("on", "yes", "y", "1") else Path(esp_choice).expanduser().resolve()
+            if not esp.is_file():
+                fail(f"the experimental speed projection's vector is missing: {esp}")
+        ok("experimental speed projection: " + ("ON (experimental)" if esp else "off"))
+    elif esp_choice.lower() not in ("", "off", "no", "n", "0"):
+        warn("the experimental speed projection is made for the original Qwen3.8-Flash-Next, not Swift 1.5: left off")
    models_dir = Path(a.gguf_dir) if a.gguf_dir else Path(a.models_dir) / tag
    shards = [models_dir / fam["file"].format(q=model, i=i) for i in (1, 2)]
    have_model = all(s.exists() and (done(s) or a.gguf_dir) for s in shards)
@@ -770,7 +918,7 @@ def main() -> int:

    # ---- 3. python packages
    step(3, "Python packages")
-    pip_install(PY_PACKAGES, "numpy, jinja2, regex, pyyaml, tqdm, requests, cmake, ninja, pillow")
+    pip_install(PY_PACKAGES, "numpy, jinja2, regex, pyyaml, tqdm, requests, cmake, ninja, pillow, psutil")

    # ---- 4. the engine
    step(4, "the Strata engine")
@@ -859,22 +1007,33 @@ def main() -> int:
        fail("the model has no per_layer_token_embd tensor (is this a Qwen3.8-Flash-Next GGUF?)")
    args = ["--pack", str(pack), "--native", str(shards[0]), "--ple-gguf", str(ple),
            "--expert-profile", str(ROOT / "data" / "expert-profile.bin"), "--expert-cache", "auto",
-            "--prefill", "2048", "--spec", "4", "--spec-min-p", "0.5", "--mtp", str(rt),
+            "--prefill", "auto", "--spec", "4", "--spec-min-p", "0.5", "--mtp", str(rt),
            "--max-context", str(ctx)]
    if ctx > 8192:
-        args += ["--kv", "int8"]
+        args += ["--kv", kv]
    # KV streaming: from 64K up the whole KV cache lives in RAM and only the part the attention reads (32K positions
    # per layer) stays in VRAM; the VRAM it frees holds more experts (+6% at 128K, +23% at 262K with Q2_0). It
-    # costs ~13.7 KB of RAM per context token (1.7 GB at 128K, 3.4 GB at 262K), so only when that fits.
-    kv_ram_gb = ctx * 13728 / 1e9
-    if ctx >= 65536 and ram >= MODELS[model]["ram_gb"] + kv_ram_gb + 1:
+    # costs ~13.7 KB of RAM per context token with 8-bit KV (1.7 GB at 128K), 7.5 KB with 4-bit, so only when it fits.
+    kv_ram_gb = ctx * (13 * (576 if kv == "q4_0" else 1056)) / 1e9   # 12 QSA layers + the draft layer
+    # the RAM copy must be pinned, and under WSL the NVIDIA driver pins only about 1 GB in all
+    if is_wsl() and ctx >= 65536:
+        ok("WSL: KV streaming off (the driver pins only about 1 GB of RAM); the KV cache stays in VRAM")
+    elif ctx >= 65536 and ram >= MODELS[model]["ram_gb"] + kv_ram_gb + 1:
        args += ["--kv-resident", "32768"]
        ok(f"KV streaming on: the context's KV cache lives in RAM ({kv_ram_gb:.1f} GB), more experts fit in VRAM")
    if vision != "none":
        args += ["--vision", "--vram-reserve-mib", str(VISION[vision]["reserve_mib"])]
+    if esp is not None:
+        # the package's profile, with llama.cpp's flags (the engine takes the same ones)
+        args += ["--control-vector-scaled", f"{esp}:1.0", "--control-vector-layer-range", "4", "44",
+                 "--cvec-mode", "project", "--cvec-dir", "per-layer"]
    cfg = {"exe": str(eng / EXE), "args": args, "cwd": str(ROOT), "tokenizer": str(pack / "tokenizer"),
           "model_name": f"{fam['name']}-{model.lower()}", "log": str(ROOT / f"strata-{tag.lower()}.log"),
-           "lib_dirs": lib_dirs, "port": a.port}
+           "lib_dirs": lib_dirs, "port": port}
+    if a.host:
+        cfg["host"] = a.host
+    if a.api_key:
+        cfg["api_key"] = a.api_key
    if vision != "none":
        cfg["vision"] = {"exe": str(eng / VEXE), "mmproj": str(mmproj), "model": str(shards[0]),
                         "gpu": vision == "gpu", "max_tokens": VISION[vision]["max_tokens"]}
@@ -882,19 +1041,22 @@ def main() -> int:
            cfg["vision"]["threads"] = max(1, (os.cpu_count() or 8) // 2)
    cfg_path = ROOT / f"strata-{tag.lower()}.json"
    cfg_path.write_text(json.dumps(cfg, indent=1), encoding="utf-8")
-    script = write_run_script(tag, cfg_path, a.port)
+    script = write_run_script(tag, cfg_path, port)
    ok(f"start script: {script.name}")

    say()
    say("All set.")
-    say(f"  API (OpenAI):     http://127.0.0.1:{a.port}/v1   (any API key; model name: anything)")
-    say(f"  API (Anthropic):  http://127.0.0.1:{a.port}/v1/messages")
+    say(f"  API (OpenAI):     http://127.0.0.1:{port}/v1   (any API key; model name: anything)")
+    say(f"  API (Anthropic):  http://127.0.0.1:{port}/v1/messages")
+    if a.host and a.host not in ("127.0.0.1", "localhost"):
+        say(f"  Other devices:    the server window prints this PC's address (http://<IP>:{port}/)"
+            + ("" if a.api_key else " - no API key set: anyone on your network can use it"))
    say(f"  Next time:        just run {'START-HERE.bat' if WIN else './setup.sh'} (or {script.name}) - it starts right away")
    if vision != "none":
        say("  Images:           send them in the chat page, in chat.py (/image <path>) or over the API")
    if a.no_start:
        return 0
-    return start(cfg_path, a.port)
+    return start(cfg_path, port)


 if __name__ == "__main__":
@@ -2,10 +2,17 @@
 # Strata for Linux: the first run installs everything and starts the model; later runs just start it.
 # Needs only an NVIDIA driver. Python (with venv) is installed through apt/dnf if it is missing (asks for sudo).
 cd "$(dirname "$0")" || exit 1
+# Python 3.10+ that can make a venv WITH pip: Debian/Ubuntu ship `venv` without `ensurepip` (that is the separate
+# python3-venv package), and a venv made without it has no pip
+ok_py() { "$1" -c 'import sys, venv, ensurepip; sys.exit(0 if sys.version_info >= (3, 10) else 1)' 2>/dev/null; }
+# a .venv from an earlier run that failed half-way has a python but no pip: start it again
+if [ -x .venv/bin/python ] && ! .venv/bin/python -m pip --version >/dev/null 2>&1; then
+  rm -rf .venv
+fi
 if [ ! -x .venv/bin/python ]; then
  PY=""
  for c in python3 python; do
-    if command -v $c >/dev/null 2>&1 && $c -c 'import sys, venv; sys.exit(0 if sys.version_info >= (3, 10) else 1)' 2>/dev/null; then
+    if command -v $c >/dev/null 2>&1 && ok_py $c; then
      PY=$c; break
    fi
  done
@@ -19,12 +26,13 @@ if [ ! -x .venv/bin/python ]; then
      sudo pacman -S --noconfirm python python-pip
    fi
    PY=python3
-    if ! $PY -c 'import sys, venv; sys.exit(0 if sys.version_info >= (3, 10) else 1)' 2>/dev/null; then
-      echo "Please install Python 3.10 or newer (with venv), then run ./setup.sh again."
+    if ! ok_py $PY; then
+      echo "Please install Python 3.10 or newer with venv (Ubuntu/Debian: sudo apt install python3-venv), then run"
+      echo "./setup.sh again."
      exit 1
    fi
  fi
  # a private environment inside this folder (system Python stays untouched; newer distros refuse global pip)
-  $PY -m venv .venv || { echo "could not create .venv: sudo apt install python3-venv"; exit 1; }
+  $PY -m venv .venv || { rm -rf .venv; echo "could not create .venv: sudo apt install python3-venv"; exit 1; }
 fi
 exec .venv/bin/python setup.py "$@"
@@ -7,6 +7,7 @@
 #include "strata/kernels/elementwise.hpp"
 #include "strata/kernels/gdn.hpp"
 #include "strata/kernels/qsa.hpp"
+#include "strata/kernels/kv_q4.hpp"
 #include "strata/kernels/kv_q8.hpp"
 #include "strata/kernels/quantize_act.hpp"
 #include "strata/kernels/rope.hpp"
@@ -24,6 +25,7 @@
 #include "strata/kernels/native_rope.hpp"
 #include "strata/kernels/native_flash_attn.hpp"
 #include "strata/kernels/fused_gr.hpp"
+#include "strata/kernels/cvec.hpp"
 #include "strata/kernels/qsa_decode_attn.hpp"
 #include "strata/kernels/fused_gdn.hpp"
 #include "strata/kernels/qsa_select.hpp"
@@ -170,9 +172,11 @@ if (!w.wants_q8k()) {        s_gemv_q8_0_split(x80, p.codes, p.scales, p.offset,
 // namespace
 void layer_set_native_bf16(bool enabled) { native_bf16_projections = enabled; }
 void layer_set_native_flash_attn_short(bool enabled) { native_flash_attn_short = enabled; }
-namespace { bool g_kv_int8 = false; }
+namespace { bool g_kv_int8 = false, g_kv_q4 = false; }
 void qsa_set_kv_int8(bool enabled) { g_kv_int8 = enabled; }
 bool qsa_kv_int8() { return g_kv_int8; }
+void qsa_set_kv_q4(bool enabled) { g_kv_q4 = enabled; }
+bool qsa_kv_q4() { return g_kv_q4; }
 uint64_t gdn_buffers_bytes(const ModelGeometry& g) {    const int64_t C = g.ssm_conv_channels;    const int64_t V = g.ssm_value_dim;    const uint64_t parts[] = {        q8k_bytes(g.n_embd),
 // x_q8k
 (uint64_t) (g.n_embd / 32) * 34,
@@ -507,6 +511,7 @@ KvPlan kv_plan(const QsaShapes& s, int64_t max_cells, int64_t ring_cells) {
    return p;
 }
 uint64_t kv_pool_bytes(const QsaShapes& s, int64_t pages) {
+    if (g_kv_q4) return (uint64_t) pages * s.page_size * strata::kernels::kv_q4_bytes_per_cell(s) + 64;
    return g_kv_int8 ? (uint64_t) pages * s.page_size * strata::kernels::kv_q8_bytes_per_cell(s) + 64
                     : (uint64_t) pages * s.n_head_kv * s.page_size * s.head_dim * 2 * 2;
 }
@@ -540,11 +545,16 @@ uint64_t qsa_state_init(const ModelGeometry& g, int64_t max_cells, void* base, Q
    const KvPlan p = kv_plan(s, max_cells, ring_cells);
    const int64_t pages = p.pages;
    Cursor c{(uint8_t*) base};
-    st.kv_int8 = g_kv_int8;
+    st.kv_int8 = g_kv_int8 && !g_kv_q4;
+    st.kv_q4 = g_kv_q4;
    st.kv_mode = p.mode;
    st.n_slots = p.slots;
    const uint64_t rows = (uint64_t) p.slots * s.n_head_kv * s.page_size;   // VRAM rows: the slots
-    if (g_kv_int8) {
+    const uint64_t q4_row = strata::kernels::kv_q4_bytes_per_head((int) s.head_dim);
+    if (st.kv_q4) {
+        st.k_q4 = c.take<uint8_t>(rows * q4_row);
+        st.v_q4 = c.take<uint8_t>(rows * q4_row);
+    } else if (g_kv_int8) {
        st.k_q = c.take<int8_t>(rows * s.head_dim);
        st.v_q = c.take<int8_t>(rows * s.head_dim);
        st.k_scale = c.take<uint16_t>(rows * (s.head_dim / strata::kernels::KV_Q8_GROUP));
@@ -595,18 +605,24 @@ uint64_t qsa_state_init(const ModelGeometry& g, int64_t max_cells, void* base, Q
    st.host = strata::kernels::KvHostPools{};
    if (p.mode != 0) {
        const uint64_t hrows = (uint64_t) pages * s.n_head_kv * s.page_size;
-        const uint64_t bytes = (uint64_t) pages * strata::kernels::kv_block_bytes(s, g_kv_int8) + 4 * 256;
+        const uint64_t bytes = (uint64_t) pages * strata::kernels::kv_block_bytes(s, qsa_kv_format(st)) + 4 * 256;
        uint8_t* h = nullptr;
        uint8_t* d = nullptr;
        if (cudaHostAlloc((void**) &h, bytes, cudaHostAllocMapped | cudaHostAllocPortable) != cudaSuccess ||
            cudaHostGetDevicePointer((void**) &d, h, 0) != cudaSuccess) {
+            // under WSL the NVIDIA driver pins only ~1 GiB in all, which is less than 128K of 8-bit KV needs
            if (p.mode == 1) std::fprintf(stderr, "strata: KV streaming: cannot pin %.2f GiB of RAM for a layer's KV copy "
-                                 "(lower the context, or run without --kv-resident)\n", (double) bytes / 1073741824.0);
+                                 "(%.2f GiB pinned so far) - lower the context, or run without --kv-resident (under "
+                                 "WSL the driver pins only about 1 GiB in all)\n", (double) bytes / 1073741824.0,
+                                 (double) g_kv_host_bytes / 1073741824.0);
            return 0;
        }
        g_kv_host_bytes += bytes;
        Cursor hc{d};
-        if (g_kv_int8) {
+        if (st.kv_q4) {
+            st.host.k_q4 = hc.take<uint8_t>(hrows * q4_row);
+            st.host.v_q4 = hc.take<uint8_t>(hrows * q4_row);
+        } else if (g_kv_int8) {
            st.host.k_q = hc.take<int8_t>(hrows * s.head_dim);
            st.host.v_q = hc.take<int8_t>(hrows * s.head_dim);
            st.host.k_scale = hc.take<uint16_t>(hrows * (s.head_dim / strata::kernels::KV_Q8_GROUP));
@@ -645,7 +661,10 @@ void qsa_state_zero(const QsaState& st, const ModelGeometry& g, void* stream) {
    const QsaShapes s = qsa_shapes(g);
    cudaStream_t cs = (cudaStream_t) stream;
    const size_t rows = (size_t) st.n_slots * s.n_head_kv * s.page_size;
-    if (st.kv_int8) {
+    if (st.kv_q4) {
+        cudaMemsetAsync(st.k_q4, 0, rows * strata::kernels::kv_q4_bytes_per_head((int) s.head_dim), cs);
+        cudaMemsetAsync(st.v_q4, 0, rows * strata::kernels::kv_q4_bytes_per_head((int) s.head_dim), cs);
+    } else if (st.kv_int8) {
        cudaMemsetAsync(st.k_q, 0, rows * s.head_dim, cs);
        cudaMemsetAsync(st.v_q, 0, rows * s.head_dim, cs);
        cudaMemsetAsync(st.k_scale, 0, rows * (s.head_dim / strata::kernels::KV_Q8_GROUP) * 2, cs);
@@ -666,7 +685,8 @@ void qsa_state_zero(const QsaState& st, const ModelGeometry& g, void* stream) {
 strata::kernels::QsaAttnPools qsa_attn_pools(const QsaState& st) {
    strata::kernels::QsaAttnPools pools;
    pools.page_table = st.page_table;
-    if (st.kv_int8) { pools.k_q = st.k_q; pools.v_q = st.v_q; pools.k_scale = st.k_scale; pools.v_scale = st.v_scale; }
+    if (st.kv_q4) { pools.k_q4 = st.k_q4; pools.v_q4 = st.v_q4; }
+    else if (st.kv_int8) { pools.k_q = st.k_q; pools.v_q = st.v_q; pools.k_scale = st.k_scale; pools.v_scale = st.v_scale; }
    else { pools.k_pool = st.k_pool; pools.v_pool = st.v_pool; }
    return pools;
 }
@@ -674,7 +694,7 @@ strata::kernels::QsaAttnPools qsa_attn_pools(const QsaState& st) {
 void qsa_kv_resolve(const QsaState& st, const ModelGeometry& g, const int32_t* ids, const int32_t* steps, int64_t n_q,
                    int64_t cap, void* stream) {
    if (st.kv_mode != 1) return;
-    strata::kernels::kv_stream_resolve(st.map, qsa_attn_pools(st), st.host, st.kv_int8, ids, steps, n_q, cap,
+    strata::kernels::kv_stream_resolve(st.map, qsa_attn_pools(st), st.host, qsa_kv_format(st), ids, steps, n_q, cap,
                                       qsa_shapes(g), stream);
 }

@@ -846,7 +866,11 @@ if (!gemv_quantized(*w_attnk, p_k, f_k, b.x_q8_0, b.x_q8k, b.kcur, g.n_embd, g.n
 // EVERY ENTRY POINT FROM HERE ON IS THE CAPTURABLE ONE: the per-token counts come from `st.step` and every
 // launch is sized from a capacity in `st`/`b`, so this sequence can be captured and replayed.  The
 // host-scalar wrappers would be correct here today and silently wrong in a graph.
-if (st.kv_int8) kv_append_q8_step(st.k_q, st.v_q, st.k_scale, st.v_scale, st.page_table, st.step, b.kcur, b.vcur, s, stream, &st.host);    else kv_append_step(st.k_pool, st.v_pool, st.page_table, st.step, b.kcur, b.vcur, s, stream, &st.host);    {        const uint64_t nvk = (uint64_t) g.n_head_kv * g.head_dim;        const uint64_t base = (uint64_t) 2 * g.n_embd + 2 * g.hc + (uint64_t) g.n_head * g.head_dim + 2 * nvk + 8;        if (!st.kv_int8) dump_slot(dump, g, layer, (const float*) st.k_pool, base, nvk / 2, stream);        if (!st.kv_int8) dump_slot(dump, g, layer, (const float*) st.v_pool, base + nvk / 2, nvk / 2, stream);        dump_slot(dump, g, layer, b.vcur, base + nvk, nvk, stream);        dump_slot(dump, g, layer, b.kcur, base + 2 * nvk, nvk, stream);    }    {        const QsaIndexerBuffers ib{st.idx_tail, st.idx_dead, st.idx_pooled, st.idx_block_pos};        if (native_qsa_indexer_enabled()) {
+if (st.kv_q4) {
+    strata::kernels::fwht256_inplace_cuda(b.kcur, g.n_head_kv, stream);   // Q4_0: rotated K and V (kv_q4.hpp)
+    strata::kernels::fwht256_inplace_cuda(b.vcur, g.n_head_kv, stream);
+    strata::kernels::kv_append_q4_step(st.k_q4, st.v_q4, st.page_table, st.step, b.kcur, b.vcur, s, stream, &st.host);
+} else if (st.kv_int8) kv_append_q8_step(st.k_q, st.v_q, st.k_scale, st.v_scale, st.page_table, st.step, b.kcur, b.vcur, s, stream, &st.host);    else kv_append_step(st.k_pool, st.v_pool, st.page_table, st.step, b.kcur, b.vcur, s, stream, &st.host);    {        const uint64_t nvk = (uint64_t) g.n_head_kv * g.head_dim;        const uint64_t base = (uint64_t) 2 * g.n_embd + 2 * g.hc + (uint64_t) g.n_head * g.head_dim + 2 * nvk + 8;        if (!st.kv_int8) dump_slot(dump, g, layer, (const float*) st.k_pool, base, nvk / 2, stream);        if (!st.kv_int8) dump_slot(dump, g, layer, (const float*) st.v_pool, base + nvk / 2, nvk / 2, stream);        dump_slot(dump, g, layer, b.vcur, base + nvk, nvk, stream);        dump_slot(dump, g, layer, b.kcur, base + 2 * nvk, nvk, stream);    }    {        const QsaIndexerBuffers ib{st.idx_tail, st.idx_dead, st.idx_pooled, st.idx_block_pos};        if (native_qsa_indexer_enabled()) {
    try {
        native_qsa_indexer_append(b.idx_raw, st.step + kStepPos, pos_base,
            (const float*) w_ikn->data, RMS_EPS, ib, s, st.max_cells, (float) qsa_freq_base(), stream);
@@ -880,14 +904,13 @@ int64_t max_blocks = (st.max_cells / s.idx_block) + 2;
    }
    // KV streaming: every block the selection names is made resident before anything reads it
    qsa_kv_resolve(st, g, b.ids, st.step, 1, cap, stream);
+    if (st.kv_q4) strata::kernels::fwht256_inplace_cuda(b.qcur, g.n_head, stream);   // <Hq, Hk> = <q, k>
    if (g_fast_attn && !native_flash_attn_short && dump == nullptr) {
-        strata::kernels::QsaAttnPools pools;
-        pools.k_pool = st.k_pool; pools.v_pool = st.v_pool; pools.k_q = st.k_q; pools.v_q = st.v_q;
-        pools.k_scale = st.k_scale; pools.v_scale = st.v_scale; pools.page_table = st.page_table;
-        if (!st.kv_int8) { pools.k_q = nullptr; pools.v_q = nullptr; }
+        const strata::kernels::QsaAttnPools pools = qsa_attn_pools(st);
        strata::kernels::qsa_decode_attn_step(b.qcur, pools, b.ids, st.step, cap, s, b.attn_scratch, b.attn, stream);
    } else {
-    if (st.kv_int8) kv_gather_q8_step(st.k_q, st.v_q, st.k_scale, st.v_scale, st.page_table, b.ids, st.step, cap, s,                                 b.k_scratch, b.v_scratch, stream);    else kv_gather_step(st.k_pool, st.v_pool, st.page_table, b.ids, st.step, cap, s, b.k_scratch, b.v_scratch,                   stream);    if (native_flash_attn_short) {
+    if (st.kv_q4) strata::kernels::kv_gather_q4_step(st.k_q4, st.v_q4, st.page_table, b.ids, st.step, cap, s, b.k_scratch, b.v_scratch, stream);
+    else if (st.kv_int8) kv_gather_q8_step(st.k_q, st.v_q, st.k_scale, st.v_scale, st.page_table, b.ids, st.step, cap, s,                                 b.k_scratch, b.v_scratch, stream);    else kv_gather_step(st.k_pool, st.v_pool, st.page_table, b.ids, st.step, cap, s, b.k_scratch, b.v_scratch,                   stream);    if (native_flash_attn_short) {
    if (st.max_cells < 1 || st.max_cells > 256 || !st.attention_status || !st.host_step) {
        err = v.name("native_flash_attn") + ": short adapter requires context <=256 and persistent status storage";
        return false;
@@ -902,6 +925,7 @@ int64_t max_blocks = (st.max_cells / s.idx_block) + 2;
    }
 } else qsa_attend_step(b.qcur, b.k_scratch, b.v_scratch, st.step, cap, s, b.attn, nullptr, stream);
    }
+    if (st.kv_q4) strata::kernels::fwht256_inplace_cuda(b.attn, g.n_head, stream);   // the output back: H is self-inverse
    dump_slot(dump, g, layer, b.attn, (uint64_t) 2 * g.n_embd + 2 * g.hc,                            (uint64_t) g.n_head * g.head_dim, stream);    {        const uint64_t vs = (uint64_t) 2 * g.n_embd + 2 * g.hc + (uint64_t) g.n_head * g.head_dim +                            (uint64_t) 2 * g.n_head_kv * g.head_dim;        dump_slot(dump, g, layer, (const float*) b.v_scratch, vs,                            (uint64_t) g.n_head_kv * g.head_dim / 2, stream);        dump_slot(dump, g, layer, (const float*) b.ids, vs + (uint64_t) g.n_head_kv * g.head_dim / 2, 4, stream);        dump_slot(dump, g, layer, (const float*) st.step,                            vs + (uint64_t) g.n_head_kv * g.head_dim / 2 + 4, 4, stream);    }
 // ---- 9. Gate the attention output before its projection. Native CUDA keeps F32
 // sigmoid/multiply arithmetic and uses Q8_1 for the native quantized projection.
@@ -1087,7 +1111,8 @@ bool block_layer_pre(const WeightTable& tables, const ModelGeometry& g, int64_t
    // the device half: `ple_block` reading `emb_dev`, and the history shift.
    const bool fused = g_fused_gr && stage_prefix == 0 && half == 0 &&
                       strata::kernels::fused_gr_supported(g.n_embd, g.hc, g.hc_lr);
-    bool pending_ffn = fused && layer > 0;   // layer-1's FFN write has not been applied to R yet
+    // layer-1's FFN write has not been applied to R yet - unless a control vector follows it, which needs R
+    bool pending_ffn = fused && layer > 0 && !strata::kernels::cvec().covers(layer - 1);
    if (ple != nullptr && ple->ready() && layer == 1 && (half == 0 || half == 1)) {
        if (pending_ffn) {
            const strata::kernels::GrShapes gs0{g.n_embd, g.hc, g.hc_lr};
@@ -1222,10 +1247,23 @@ st_begin(layer, 5, stream);
    if (g_shared_early ? !moe_combine_parts(g, layer, k, mb, parts, bb.block_out, stream, err)
                       : !moe_finish(tables, g, layer, k, mb, bb.mixed, parts, bb.block_out, stream, err)) return false;
    st_end(layer, 5, stream);    dump_half(bb, g, layer, bb.block_out, (uint64_t) g.n_embd, g.n_embd, stream);    dump_half(bb, g, layer, bb.inject, (uint64_t) 2 * g.n_embd + g.hc, g.hc, stream);    st_begin(layer, 6, stream);
-    if (!(g_fused_gr && strata::kernels::fused_gr_supported(g.n_embd, g.hc, g.hc_lr)))
-        gr_write(bb.R, bb.block_out, bb.inject, gs, bb.R, stream);
-    else if (layer == g.n_layers - 1)
-        gr_write(bb.R, bb.block_out, bb.inject2, gs, bb.R, stream);   // materialise R for the head
+    const bool steer = strata::kernels::cvec().covers(layer);   // --control-vector-scaled: after this write
+    try {
+        if (!(g_fused_gr && strata::kernels::fused_gr_supported(g.n_embd, g.hc, g.hc_lr))) {
+            gr_write(bb.R, bb.block_out, bb.inject, gs, bb.R, stream);
+            if (steer) strata::kernels::cvec_apply(bb.R, layer, 1, g.hc * g.n_embd, nullptr, 0, nullptr, 0, false, stream);
+        } else if (layer == g.n_layers - 1) {
+            gr_write(bb.R, bb.block_out, bb.inject2, gs, bb.R, stream);   // materialise R for the head
+            if (steer) strata::kernels::cvec_apply(bb.R, layer, 1, g.hc * g.n_embd, nullptr, 0, nullptr, 0, false, stream);
+        } else if (steer) {
+            // the write the next layer's fused read would have folded, then the vector
+            strata::kernels::cvec_apply(bb.R, layer, 1, g.hc * g.n_embd, bb.block_out, g.n_embd, bb.inject2, g.hc,
+                                        true, stream);
+        }
+    } catch (const std::exception& e) {
+        err = std::string("block_layer_post: ") + e.what();
+        return false;
+    }
    st_end(layer, 6, stream);    return true;}
 bool block_layer(const WeightTable& tables, const ModelGeometry& g, int64_t layer, int64_t pos, int32_t pos_base,                 const GdnBuffers& gb, const QsaState& qst, const QsaBuffers& qb, const MoEBuffers& mb,                 int64_t k, const BlockBuffers& bb, const float* parts, void* stream, std::string& err,                 const Doorbell* db, const PleRun* ple) {
 // The two halves back to back.  `parts` must be THIS layer's experts and must be ready before the call -
@@ -7,6 +7,7 @@
 #include "strata/kernels/elementwise.hpp"
 #include "strata/kernels/fused_gr.hpp"
 #include "strata/kernels/gr.hpp"
+#include "strata/kernels/kv_q4.hpp"
 #include "strata/kernels/kv_q8.hpp"
 #include "strata/kernels/native_moe.hpp"
 #include "strata/kernels/native_mmvq.hpp"
@@ -338,7 +339,12 @@ bool MtpDrafter::record_forward(int T, int step_row0, cudaStream_t cs, std::stri
        native_mmvq(GGML_Q8_0, q8("self_attn.v_proj.weight"), xq_, vcur_, (int) N, (int) (NKV * HD), T, cs);
        for (int t = 0; t < T; ++t) {
            norm_rope(kcur_ + t * NKV * HD, f32("self_attn.k_norm.weight"), (int) NKV, (int) HD, pos + t * NH);
-            if (st_.kv_int8)
+            if (st_.kv_q4) {   // Q4_0 KV (kv_q4.hpp): rotated K and V
+                fwht256_inplace_cuda(kcur_ + t * NKV * HD, NKV, cs);
+                fwht256_inplace_cuda(vcur_ + t * NKV * HD, NKV, cs);
+                kv_append_q4_step(st_.k_q4, st_.v_q4, st_.page_table, step + t * 4, kcur_ + t * NKV * HD,
+                                  vcur_ + t * NKV * HD, s, cs, &st_.host);
+            } else if (st_.kv_int8)
                kv_append_q8_step(st_.k_q, st_.v_q, st_.k_scale, st_.v_scale, st_.page_table, step + t * 4,
                                  kcur_ + t * NKV * HD, vcur_ + t * NKV * HD, s, cs, &st_.host);
            else
@@ -355,10 +361,12 @@ bool MtpDrafter::record_forward(int T, int step_row0, cudaStream_t cs, std::stri
                return false;
            }
            norm_rope(qc, f32("self_attn.q_norm.weight"), (int) NH, (int) HD, pos + t * NH);
+            if (st_.kv_q4) fwht256_inplace_cuda(qc, NH, cs);
        }
        const QsaAttnPools pools = qsa_attn_pools(st_);
        if (window_ > 0) window_ids(const_cast<int32_t*>(step), T, (int) window_, ident_, cap_, cs);
        qsa_decode_attn_batch(qcur_, pools, ident_, step, cap_, s, attn_scratch_, attn_, T, cs);
+        if (st_.kv_q4) fwht256_inplace_cuda(attn_, (int64_t) T * NH, cs);
        for (int t = 0; t < T; ++t)
            native_qsa_gate_apply(attn_ + t * NH * HD, qfull_ + t * NH * 2 * HD, attn32_ + t * NH * HD, (int) NH, (int) HD, cs);
        native_quantize_q8_1(attn32_, xq_, (int) (NH * HD), T, cs);
@@ -507,7 +515,7 @@ void MtpDrafter::kv_restore(int64_t upto) {
    // the ring's blocks below `upto`, from the host copy: a checkpoint resume may have left later cells in them
    const strata::kernels::QsaShapes s = shapes_of(*g_);
    const int64_t b1 = (upto + s.page_size - 1) / s.page_size, b0 = std::max<int64_t>(0, b1 - st_.n_slots);
-    strata::kernels::kv_ring_restore(qsa_attn_pools(st_), st_.host, st_.kv_int8, b0, b1, st_.n_slots, s, cs_);
+    strata::kernels::kv_ring_restore(qsa_attn_pools(st_), st_.host, qsa_kv_format(st_), b0, b1, st_.n_slots, s, cs_);
    cudaStreamSynchronize(cs_);
 }

@@ -29,20 +29,46 @@ namespace {
 // A 2 MB-aligned reservation.  Large pages first, then the largest alignment the OS will give us for free.
 void* reserve(uint64_t bytes, PageBacking& got, std::string& note) {
 #ifdef _WIN32
+    // MEM_LARGE_PAGES needs SeLockMemoryPrivilege.  Having it assigned to the account is not enough: the
+    // PROCESS must enable it in its own token (AdjustTokenPrivileges) before VirtualAlloc, or the call fails.
+    // An account without the assignment, or a failure to enable, leaves the process as it was: VirtualAlloc
+    // then refuses and the 4 KB fallback below runs - that is the EXPECTED outcome on a desktop.
+    {
+        HANDLE tok = nullptr;
+        if (OpenProcessToken(GetCurrentProcess(), TOKEN_ADJUST_PRIVILEGES | TOKEN_QUERY, &tok)) {
+            TOKEN_PRIVILEGES tp{};
+            tp.PrivilegeCount = 1;
+            if (LookupPrivilegeValueW(nullptr, L"SeLockMemoryPrivilege", &tp.Privileges[0].Luid)) {
+                tp.Privileges[0].Attributes = SE_PRIVILEGE_ENABLED;
+                if (!AdjustTokenPrivileges(tok, FALSE, &tp, 0, nullptr, nullptr) && GetLastError() != ERROR_NOT_ALL_ASSIGNED)
+                    (void) 0;   // nothing actionable: the large-page attempt below reports the outcome
+            }
+            CloseHandle(tok);
+        }
+    }
    // MEM_LARGE_PAGES needs SeLockMemoryPrivilege; a normal account does not have it and VirtualAlloc then
    // fails with ERROR_PRIVILEGE_NOT_HELD.  That is the EXPECTED outcome on a desktop, not an error.
    SIZE_T large = GetLargePageMinimum();
-    if (large > 0) {
-        void* p = VirtualAlloc(nullptr, (SIZE_T) bytes, MEM_RESERVE | MEM_COMMIT | MEM_LARGE_PAGES,
+    // A/B switch: STRATA_NO_LARGEPAGES=1 skips the large-page attempt, same run, same boot.
+    if (large > 0 && std::getenv("STRATA_NO_LARGEPAGES") == nullptr) {
+        // MEM_LARGE_PAGES requires the allocation size to be an exact multiple of the large page size -
+        // anything else is ERROR_INVALID_PARAMETER (87), which reads like a privilege problem but is not.
+        // Round up: the slack is under 2 MB and the tail stays unused.
+        const SIZE_T lbytes = (SIZE_T) (((SIZE_T) bytes + large - 1) / large * large);
+        void* p = VirtualAlloc(nullptr, lbytes, MEM_RESERVE | MEM_COMMIT | MEM_LARGE_PAGES,
                               PAGE_READWRITE);
        if (p) {
            got = PageBacking::LargePages;
            note = "large pages (" + std::to_string((unsigned long long) large) + " B)";
            return p;
        }
-        note = "large pages refused (GetLargePageMinimum=" + std::to_string((unsigned long long) large) +
-               ", VirtualAlloc error " + std::to_string((unsigned long long) GetLastError()) +
-               " - needs SeLockMemoryPrivilege); using 4 KB pages";
+        // 1450 (ERROR_NO_SYSTEM_RESOURCES) is the large-page pool saying no, 87 is a size that is not a
+        // multiple of the minimum, 1314 is the privilege: without the byte count the three read as one bug.
+        note = "large pages refused for " + std::to_string((unsigned long long) lbytes) + " B (GetLargePageMinimum=" +
+               std::to_string((unsigned long long) large) + ", VirtualAlloc error " +
+               std::to_string((unsigned long long) GetLastError()) + "); using 4 KB pages";
+    } else if (std::getenv("STRATA_NO_LARGEPAGES") != nullptr) {
+        note = "large pages skipped (STRATA_NO_LARGEPAGES); using 4 KB pages";
    } else {
        note = "this system has no large-page minimum; using 4 KB pages";
    }
@@ -1,5 +1,6 @@
 // src/core/session.cpp - one token through all 48 layers.  See the header for why the graphs are per-layer.
 #include "strata/core/session.hpp"
+#include "strata/core/progress.hpp"

 #include "strata/kernels/qsa.hpp"
 #include "strata/kernels/elementwise.hpp"
@@ -87,9 +88,12 @@ uint64_t session_init(const ModelGeometry& g, int64_t max_cells, int64_t k, void
    s.qsa_buf_arena = take(qsa_buffers_bytes(g, max_cells));

    uint8_t* qp = (uint8_t*) s.qsa_state_arena;
+    // a layer whose pinned RAM could not be had (KV streaming's host copy) is half-built: going on would have the
+    // attention read null host pointers at the first request ("illegal memory access"), so the session fails here
    for (int64_t i = 0; i < g.n_qsa_layers(); ++i)
-        qsa_state_init(g, max_cells, qp + (i == 0 ? 0 : first + (uint64_t) (i - 1) * rest), s.qsa_states[i],
-                       i == 0 ? nullptr : &s.qsa_states[0]);
+        if (qsa_state_init(g, max_cells, qp + (i == 0 ? 0 : first + (uint64_t) (i - 1) * rest), s.qsa_states[i],
+                           i == 0 ? nullptr : &s.qsa_states[0]) == 0)
+            return 0;
    qsa_buffers_init(g, max_cells, s.qsa_buf_arena, s.qsa_bufs);

    s.moe_arena = take(moe_buffers_bytes(g, k));
@@ -888,6 +892,7 @@ bool session_run_token(const ModelGeometry& g, int64_t pos, int32_t pos_base, Se
        const auto t0 = Clock::now();
        auto last_flush = t0;
        uint32_t spins = 0;
+        progress_at("token: waiting for the GPU to reach layer", l);
        while (*seq < want) {
            STRATA_SPIN_PAUSE();
            if ((++spins & 1023u) != 0) continue;
@@ -909,6 +914,7 @@ bool session_run_token(const ModelGeometry& g, int64_t pos, int32_t pos_base, Se
            }
        }
        const auto t1 = Clock::now();
+        progress_at("token: the CPU experts of layer", l);
        if (pool != nullptr) pool(user, s.db->h_x_f, s.db->h_ids, s.db->h_weights, g.n_embd, s.k, y_miss_host);
        std::atomic_thread_fence(std::memory_order_seq_cst);
        _mm_sfence();
@@ -917,8 +923,11 @@ bool session_run_token(const ModelGeometry& g, int64_t pos, int32_t pos_base, Se
        tg.ms_wait += std::chrono::duration<double, std::milli>(t1 - t0).count();
        tg.ms_pool += std::chrono::duration<double, std::milli>(t2 - t1).count();
    }
+    progress_at("token: waiting for the GPU to finish the token");
    const cudaError_t se = cudaStreamSynchronize(cs);
    if (se != cudaSuccess) { err = std::string("session_run_token: ") + cudaGetErrorString(se); return false; }
+    progress_at("decode");
+    progress_beat();
    return true;
 }

@@ -11,7 +11,9 @@
 #include "strata/kernels/cpu/expert.hpp"
 #include "strata/kernels/elementwise.hpp"
 #include "strata/kernels/fused_gr.hpp"
+#include "strata/kernels/cvec.hpp"
 #include "strata/kernels/gr.hpp"
+#include "strata/kernels/kv_q4.hpp"
 #include "strata/kernels/kv_q8.hpp"
 #include "strata/kernels/native_mmvq.hpp"
 #include "strata/kernels/native_qsa.hpp"
@@ -26,11 +28,13 @@
 #include "strata/kernels/rope.hpp"
 #include "strata/kernels/s2_expert_grouped.hpp"
 #include "strata/kernels/sampler.hpp"
+#include "strata/core/progress.hpp"
 #include "strata/kernels/shared_expert.hpp"
 #include "strata/kernels/verify_kernels.hpp"

 #include <atomic>
 #include <chrono>
+#include <cmath>
 #include <cstdio>
 #include <cstdlib>
 #include <cstring>
@@ -89,7 +93,23 @@ bool native_of(const WeightRef* w, const std::string& name, std::string& err) {

 }  // namespace

+namespace {
+std::atomic<const Verifier*> g_diag_verifier{nullptr};
+void diag_active_verifier(std::FILE* f) {
+    if (const Verifier* v = g_diag_verifier.load()) v->diag(f);
+}
+}  // namespace
+
+void Verifier::diag(std::FILE* f) const {
+    auto rd = [](const uint32_t* p) { return p ? *(const volatile uint32_t*) p : 0u; };
+    std::fprintf(f, "  verify window: %d tokens at position %lld, host at layer step %u; the GPU rang %u; flags: "
+                    "served %u, plan (A) %u, copies (B) %u\n", last_t_, (long long) last_pos0_, cur_layer_ + 1,
+                 rd(h_seq_), rd(h_flag_), rd(h_flagA_), rd(h_flagB_));
+}
+
 Verifier::~Verifier() {
+    const Verifier* self = this;
+    g_diag_verifier.compare_exchange_strong(self, nullptr);
    if (cs_) cudaStreamSynchronize(cs_);
    for (auto& e : exec_)
        if (e) cudaGraphExecDestroy(e);
@@ -105,12 +125,16 @@ Verifier::~Verifier() {

 bool Verifier::init(const WeightTable& wt, const ModelGeometry& g, SessionState& ss, const VerifyHits& hits,
                    const NativeHead* head, int max_t, std::string& err) {
+    g_diag_verifier.store(this);
+    diag_verify_fn().store(&diag_active_verifier);
    wt_ = &wt;
    g_ = &g;
    ss_ = &ss;
    hits_ = hits;
    head_ = head;
    max_t_ = max_t;
+    sampling_.greedy = true;      // a fresh verifier samples greedily until set_sampling says otherwise
+    sampling_.temperature = 0.0f;
    if (max_t < 2 || max_t > strata::kernels::kVerifyMaxT || max_t > strata::kernels::cpu::MAXT) {
        err = "verify: the window must hold 2.." + std::to_string(strata::kernels::kVerifyMaxT) + " tokens";
        return false;
@@ -328,7 +352,9 @@ bool Verifier::record_window(int T, cudaStream_t cs, std::string& err) {
            wi[h] = need(v, (std::string(pfx[h]) + "inject.weight").c_str(), err);
            if (!wn[h] || !wd[h] || !wu[h] || !wi[h]) return false;
        }
-        bool pending = l > 0;   // the previous layer's FFN write, folded into this layer's first read
+        // the previous layer's FFN write, folded into this layer's first read (a control vector after it has
+        // already applied it)
+        bool pending = l > 0 && !cvec().covers(l - 1);
        if (l == 1 && ple_on) {
            float* normalized = (float*) ((uint8_t*) ss.ple.scratch + ple_block_scratch_bytes());
            for (int t = tb; t < te; ++t) {
@@ -419,10 +445,17 @@ bool Verifier::record_window(int T, cudaStream_t cs, std::string& err) {
                native_mmvq(wk->native_type, wk->native_data, xq_, kcur_ + tb * NKV * HD, (int) N, (int) (NKV * HD), n, cs);
                native_mmvq(wv->native_type, wv->native_data, xq_, vcur_ + tb * NKV * HD, (int) N, (int) (NKV * HD), n, cs);
                for (int t = tb; t < te; ++t) norm_rope(kcur_ + t * NKV * HD, wkn, (int) NKV, (int) HD, pos_ + t * NH);
+                if (st.kv_q4) {   // Q4_0 KV (kv_q4.hpp): K and V rotated before they are stored
+                    fwht256_inplace_cuda(kcur_ + tb * NKV * HD, (int64_t) n * NKV, cs);
+                    fwht256_inplace_cuda(vcur_ + tb * NKV * HD, (int64_t) n * NKV, cs);
+                }
                if (grp == 0) copy_from_mapped(tail_snap_ + (size_t) qi * TS, st.idx_tail, TS, cs);
                for (int t = tb; t < te; ++t) {
                    const int32_t* step_t = step_ + t * kStepCount;
-                    if (st.kv_int8)
+                    if (st.kv_q4)
+                        kv_append_q4_step(st.k_q4, st.v_q4, st.page_table, step_t, kcur_ + t * NKV * HD,
+                                          vcur_ + t * NKV * HD, s, cs, &st.host);
+                    else if (st.kv_int8)
                        kv_append_q8_step(st.k_q, st.v_q, st.k_scale, st.v_scale, st.page_table, step_t,
                                          kcur_ + t * NKV * HD, vcur_ + t * NKV * HD, s, cs, &st.host);
                    else
@@ -444,6 +477,7 @@ bool Verifier::record_window(int T, cudaStream_t cs, std::string& err) {
                        return false;
                    }
                    norm_rope(qc, wqn, (int) NH, (int) HD, pos_ + t * NH);
+                    if (st.kv_q4) fwht256_inplace_cuda(qc, NH, cs);   // <Hq, Hk> = <q, k>
                }
                for (int t = tb; t < te; ++t) {
                    float* qx = qidx_ + t * IQ * ID;
@@ -459,6 +493,7 @@ bool Verifier::record_window(int T, cudaStream_t cs, std::string& err) {
                const QsaAttnPools pools = qsa_attn_pools(st);
                qsa_decode_attn_batch(qcur_ + tb * NH * HD, pools, sel_ + (size_t) tb * cap_, step_ + tb * kStepCount, cap_,
                                      s, attn_scratch_ + (size_t) tb * attn_scratch_floats_, attn_ + tb * NH * HD, n, cs);
+                if (st.kv_q4) fwht256_inplace_cuda(attn_ + tb * NH * HD, (int64_t) n * NH, cs);   // back: H^-1 = H
                for (int t = tb; t < te; ++t) {
                    if (native_qsa_enabled())
                        native_qsa_gate_apply(attn_ + t * NH * HD, qfull_ + t * NH * 2 * HD, attn32_ + t * NH * HD,
@@ -557,8 +592,12 @@ bool Verifier::record_window(int T, cudaStream_t cs, std::string& err) {
            mb.weights = w_ + t * K; mb.shared = shared_ + t * N;
            if (!moe_combine_parts(g, l, K, mb, parts_ + (size_t) t * K * N, bo_ + t * N, cs, err)) return false;
        }
-        if (l == g.n_layers - 1)
+        if (l == g.n_layers - 1) {
            for (int t = tb; t < te; ++t) gr_write(Rt(t), bo_ + t * N, inj2_ + t * HC, gs, Rt(t), cs);
+            if (cvec().covers(l)) cvec_apply(Rt(tb), l, n, HC * N, nullptr, 0, nullptr, 0, false, cs);
+        } else if (cvec().covers(l)) {
+            cvec_apply(Rt(tb), l, n, HC * N, bo_ + tb * N, N, inj2_ + tb * HC, HC, true, cs);
+        }
        return true;
    };

@@ -594,6 +633,9 @@ bool Verifier::record_window(int T, cudaStream_t cs, std::string& err) {
                return false;
            }
        }
+        // Greedy, the default, is recorded here as before (no extra launch or sync per window). A request that
+        // samples or penalizes is sampled again host-side after the replay (run()) with its own parameters and a
+        // fresh draw counter: a captured sampler would bake them in and replay the same draws forever.
        SamplerParams sp;
        sp.greedy = true;
        sp.temperature = 0.0f;
@@ -750,6 +792,7 @@ bool Verifier::run(int T, const int32_t* tokens, int64_t pos0, PoolMultiFn pool,
        const Clock::time_point a = Clock::now();
        auto last_flush = a;
        uint32_t spins = 0;
+        progress_at("verify window: waiting for the GPU to reach layer", l);
        while (*seq < want) {
            _mm_pause();
            if ((++spins & 1023u) != 0) continue;
@@ -770,10 +813,12 @@ bool Verifier::run(int T, const int32_t* tokens, int64_t pos0, PoolMultiFn pool,
        cur_layer_ = want - 1;
        set_plan_slot(grp);
        const int tb = gtb[grp], n = gte[grp] - gtb[grp];
+        progress_at("verify window: the CPU experts of layer", l);
        if (pool != nullptr)
            pool(user, h_x_ + (size_t) tb * g.n_embd, h_ids_ + (size_t) tb * ss.k, n, ss.k,
                 h_ymiss_ + (size_t) tb * ss.k * g.n_embd, l);
        VDBG("layer %lld served\n", (long long) l);
+        progress_tick();
        std::atomic_thread_fence(std::memory_order_seq_cst);
        _mm_sfence();
        if (*(volatile uint32_t*) h_flagA_ != want) {        // the pool did not publish a plan: an empty one
@@ -790,12 +835,46 @@ bool Verifier::run(int T, const int32_t* tokens, int64_t pos0, PoolMultiFn pool,
        ms_wait += std::chrono::duration<double, std::milli>(b - a).count();
        ms_pool += ms_since(b);
    }
+    progress_at("verify window: waiting for the GPU to finish the window (flags A/B/M raised)", (int64_t) T);
    const cudaError_t se = cudaStreamSynchronize(cs_);
    if (se != cudaSuccess) { err = std::string("verify: ") + cudaGetErrorString(se); return false; }
+    progress_at("verify window: waiting for the expert copies", (int64_t) T);
    cudaStreamSynchronize(copy_);   // no host function of this window may raise flag B in the next one
+    // ---- a sampled or penalized request: the head's sampling again, host-side so its parameters are this call's
+    // own (a captured kernel would replay the same draws forever).  Row t's draw is Philox(seed, pos0 + t): tied to
+    // the POSITION it samples, not to how the text was cut into windows, so a seed replays the same text whatever
+    // the drafts were. Exact: a rejected row's draw is discarded, and no kept decision depends on a reused draw.
+    const bool sampled = !sampling_.greedy && sampling_.temperature > 0.0f;
+    if (sampled || hist_d_ != nullptr) {
+        SamplerParams sp = sampling_;
+        sp.counter = (uint64_t) pos0;
+        sample_tokens(head_logits_, T, (int) n_vocab_, hist_d_, hist_len_, sp, m_out_, cs_);
+        if (cudaStreamSynchronize(cs_) != cudaSuccess) {   // m_out_ is the mapped h_out_: synced, it is readable
+            err = "verify: the head sampling failed";
+            return false;
+        }
+    }
    for (int t = 0; t < T; ++t) out[t] = ((volatile int32_t*) h_out_)[t];
+    if (static const bool dbg = std::getenv("STRATA_DBG_NAN") != nullptr; dbg) {   // debug: the first non-finite head
+        static bool reported = false;
+        if (!reported) {
+            std::vector<float> h((size_t) T * (size_t) n_vocab_);
+            cudaMemcpy(h.data(), head_logits_, h.size() * 4, cudaMemcpyDeviceToHost);
+            for (int t = 0; t < T && !reported; ++t) {
+                int64_t bad = 0;
+                for (int64_t v = 0; v < n_vocab_; ++v) bad += !std::isfinite(h[(size_t) t * n_vocab_ + v]);
+                if (bad) {
+                    reported = true;
+                    std::fprintf(stderr, "strata dbg: verify window at position %lld, row %d: %lld of %lld logits non-finite "
+                                         "(token out %d)\n", (long long) pos0, t, (long long) bad, (long long) n_vocab_, out[t]);
+                }
+            }
+        }
+    }
    VDBG("window done\n");
    ++windows;
+    progress_at("decode");
+    progress_beat();
    return true;
 }

@@ -0,0 +1,419 @@
+// src/kernels/cpu/iq_avx2.cpp - the i-quant expert rows in 256-bit lanes, several tokens at once.
+//
+// The AVX-512 kernels' (iq_avx512.cpp) multi-token scheme on the CPUs without AVX-512 (AMD Zen 2/3, Intel
+// Core 12th-14th gen and Core Ultra): each 32-value chunk is decoded once - grid lookups, one sign vector,
+// one scale vector - and every token of the verify window applies them with a load, a `sign`, a `maddubs`,
+// a `madd` and an `add`.  ggml-cpu's own AVX2 dot products for these formats are single-token: every token
+// re-does the codebook lookups and the sign expansion.  The sign vector is ggml's bit_selector pattern
+// (ggml-cpu/arch/x86/quants.c): pshufb-broadcast of each sign byte, AND with the bit selector, CMPEQ, OR
+// with one, `vpsignb`.  The arithmetic is ggml's (ggml-cpu/quants.c, the `_generic` references) - only the
+// order of the float additions differs.
+//
+// Formats: IQ2_XXS (16), IQ2_XS (17), IQ3_XXS (18), IQ3_S (21), IQ2_S (22).  IQ1_M stays on ggml-cpu.
+#include "strata/kernels/cpu/iq_avx2.hpp"
+
+#define GGML_COMMON_DECL_CPP
+#define GGML_COMMON_IMPL_CPP
+#include "ggml-common.h"
+
+#include <immintrin.h>
+
+#include <cmath>
+#include <cstring>
+
+namespace strata::kernels::cpu {
+namespace {
+
+inline float h2f(uint16_t h) { return _mm_cvtss_f32(_mm_cvtph_ps(_mm_cvtsi32_si128((int) h))); }
+inline uint32_t u32(const uint8_t* p) { uint32_t v; std::memcpy(&v, p, 4); return v; }
+inline uint16_t u16(const uint8_t* p) { uint16_t v; std::memcpy(&v, p, 2); return v; }
+inline uint64_t u64(const uint8_t* p) { uint64_t v; std::memcpy(&v, p, 8); return v; }
+
+// 32 sign bits -> 32 bytes of -1 (bit set) / +1: ggml's bit_selector pattern, shared by all tokens.
+inline __m256i sgn_vec(uint32_t m) {
+    const __m128i bm = _mm_set1_epi32((int) m);
+    const __m128i lo = _mm_shuffle_epi8(bm, _mm_setr_epi8(0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1));
+    const __m128i hi = _mm_shuffle_epi8(bm, _mm_setr_epi8(2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3));
+    const __m256i bits = _mm256_inserti128_si256(_mm256_castsi128_si256(lo), hi, 1);
+    const __m256i sel = _mm256_setr_epi8(1, 2, 4, 8, 16, 32, 64, (char) 0x80,
+                                         1, 2, 4, 8, 16, 32, 64, (char) 0x80,
+                                         1, 2, 4, 8, 16, 32, 64, (char) 0x80,
+                                         1, 2, 4, 8, 16, 32, 64, (char) 0x80);
+    const __m256i nz = _mm256_cmpeq_epi8(_mm256_and_si256(bits, sel), sel);
+    return _mm256_or_si256(nz, _mm256_set1_epi8(1));
+}
+
+// The two 16-value scales of one 32-value half (IQ2_XS, IQ2_S): int16 lanes 0-7 = a (values 0-15),
+// 8-15 = b (values 16-31).  NOT a broadcast dword [a,b] - that would alternate the scales lane by lane.
+inline __m256i sc16(int a, int b) {
+    return _mm256_inserti128_si256(_mm256_castsi128_si256(_mm_set1_epi16((short) a)),
+                                   _mm_set1_epi16((short) b), 1);
+}
+// One scale for the whole 32-value half (IQ2_XXS, IQ3_XXS, IQ3_S).
+inline __m256i sc32(int s) { return _mm256_set1_epi16(s); }
+
+// keven_signs_q2xs (ggml keeps it static in arch/x86/quants.c): one u64 per 7-bit sign index, byte k
+// = 0xFF when bit k of ksigns_iq2xs[i] is set, 0x01 otherwise.  With this a whole 32-value sign vector
+// is four scalar loads and a set_epi64x - no ksigns byte packing chain and no bit_selector expansion.
+// Built once at load time from the shared ggml-common table so it can never drift from it.
+struct EvenSigns {
+    uint64_t v[128];
+    EvenSigns() {
+        for (int i = 0; i < 128; ++i) {
+            uint64_t r = 0;
+            for (int k = 0; k < 8; ++k)
+                r |= (uint64_t) (((ksigns_iq2xs[i] >> k) & 1) ? 0xFF : 0x01) << (8 * k);
+            v[i] = r;
+        }
+    }
+};
+static const EvenSigns even_signs;
+
+inline float hsum8(__m256 v) {
+    const __m128 lo = _mm256_castps256_ps128(v), hi = _mm256_extractf128_ps(v, 1);
+    __m128 s = _mm_add_ps(lo, hi);
+    s = _mm_hadd_ps(s, s);
+    s = _mm_hadd_ps(s, s);
+    return _mm_cvtss_f32(s);
+}
+
+// ---- per format: one 32-value half (values 64*j + 32*half .. +31) -> grid magnitudes, sign vector, scales
+template <int TY> struct Fmt32;
+
+template <> struct Fmt32<16> {   // IQ2_XXS: d, qs[32] u16
+    static constexpr int bytes = 66;
+    static constexpr float K = 0.125f;
+    static inline void decode(const uint8_t* b, int j, int half, __m256i& g, __m256i& sgn, __m256i& sc) {
+        const uint8_t* q = b + 2 + 16 * j + 8 * half;
+        const uint32_t w0 = u32(q), w1 = u32(q + 4);
+        g = _mm256_set_epi64x((long long) iq2xxs_grid[w0 >> 24], (long long) iq2xxs_grid[(w0 >> 16) & 255],
+                              (long long) iq2xxs_grid[(w0 >> 8) & 255], (long long) iq2xxs_grid[w0 & 255]);
+        sgn = _mm256_set_epi64x((long long) even_signs.v[(w1 >> 21) & 127], (long long) even_signs.v[(w1 >> 14) & 127],
+                                (long long) even_signs.v[(w1 >> 7) & 127], (long long) even_signs.v[w1 & 127]);
+        sc = sc32(2 * (int) (w1 >> 28) + 1);
+    }
+};
+
+template <> struct Fmt32<17> {   // IQ2_XS: d, qs[32] u16 (9-bit grid index + 7-bit sign index), scales[8]
+    static constexpr int bytes = 74;
+    static constexpr float K = 0.125f;
+    static inline void decode(const uint8_t* b, int j, int half, __m256i& g, __m256i& sgn, __m256i& sc) {
+        uint16_t v[8];
+        std::memcpy(v, b + 2 + 16 * j, 16);
+        const int o = 4 * half;
+        g = _mm256_set_epi64x((long long) iq2xs_grid[v[o + 3] & 511], (long long) iq2xs_grid[v[o + 2] & 511],
+                              (long long) iq2xs_grid[v[o + 1] & 511], (long long) iq2xs_grid[v[o] & 511]);
+        uint32_t s = 0;
+        for (int l = 0; l < 4; ++l) s |= (uint32_t) ksigns_iq2xs[v[o + l] >> 9] << (8 * l);
+        sgn = sgn_vec(s);
+        const uint8_t sb = b[66 + 2 * j + half];
+        sc = sc16(2 * (sb & 15) + 1, 2 * (sb >> 4) + 1);
+    }
+};
+
+template <> struct Fmt32<22> {   // IQ2_S: d, qs[64] (32 grid bytes, 32 sign bytes), qh[8], scales[8]
+    static constexpr int bytes = 82;
+    static constexpr float K = 0.125f;
+    static inline void decode(const uint8_t* b, int j, int half, __m256i& g, __m256i& sgn, __m256i& sc) {
+        const uint8_t* qs = b + 2 + 8 * j;
+        const uint8_t h = b[66 + 2 * j + half];
+        const int o = 4 * half;
+        g = _mm256_set_epi64x((long long) iq2s_grid[qs[o + 3] | ((h << 2) & 0x300)],
+                              (long long) iq2s_grid[qs[o + 2] | ((h << 4) & 0x300)],
+                              (long long) iq2s_grid[qs[o + 1] | ((h << 6) & 0x300)],
+                              (long long) iq2s_grid[qs[o] | ((h << 8) & 0x300)]);
+        const uint64_t m = u64(b + 2 + 32 + 8 * j);
+        sgn = sgn_vec(half ? (uint32_t) (m >> 32) : (uint32_t) m);
+        const uint8_t sb = b[74 + 2 * j + half];
+        sc = sc16(2 * (sb & 15) + 1, 2 * (sb >> 4) + 1);
+    }
+};
+
+template <> struct Fmt32<18> {   // IQ3_XXS: d, qs[64] grid bytes, 8 x u32 (4 x 7-bit sign index + 4-bit scale)
+    static constexpr int bytes = 98;
+    static constexpr float K = 0.25f;
+    static inline void decode(const uint8_t* b, int j, int half, __m256i& g, __m256i& sgn, __m256i& sc) {
+        const uint8_t* q = b + 2 + 16 * j + 8 * half;
+        g = _mm256_set_epi32((int) iq3xxs_grid[q[7]], (int) iq3xxs_grid[q[6]], (int) iq3xxs_grid[q[5]], (int) iq3xxs_grid[q[4]],
+                             (int) iq3xxs_grid[q[3]], (int) iq3xxs_grid[q[2]], (int) iq3xxs_grid[q[1]], (int) iq3xxs_grid[q[0]]);
+        const uint32_t w = u32(b + 2 + 64 + 8 * j + 4 * half);
+        sgn = _mm256_set_epi64x((long long) even_signs.v[(w >> 21) & 127], (long long) even_signs.v[(w >> 14) & 127],
+                                (long long) even_signs.v[(w >> 7) & 127], (long long) even_signs.v[w & 127]);
+        sc = sc32(2 * (int) (w >> 28) + 1);
+    }
+};
+
+template <> struct Fmt32<21> {   // IQ3_S: d, qs[64], qh[8], signs[32], scales[4]
+    static constexpr int bytes = 110;
+    static constexpr float K = 1.0f;
+    static inline void decode(const uint8_t* b, int j, int half, __m256i& g, __m256i& sgn, __m256i& sc) {
+        const uint8_t* q = b + 2 + 16 * j + 8 * half;
+        const uint32_t h = b[66 + 2 * j + half];
+#define G3(k) (int) iq3s_grid[q[k] | (((h >> k) & 1u) << 8)]
+        g = _mm256_set_epi32(G3(7), G3(6), G3(5), G3(4), G3(3), G3(2), G3(1), G3(0));
+#undef G3
+        const uint64_t m = u64(b + 74 + 8 * j);
+        sgn = sgn_vec(half ? (uint32_t) (m >> 32) : (uint32_t) m);
+        const uint8_t s = b[106 + j];
+        sc = sc32(half ? 2 * (s >> 4) + 1 : 2 * (s & 15) + 1);
+    }
+};
+
+template <int TY, int NT>
+inline void row_dot(const uint8_t* row, int nblocks, const block_q8_K* const* y, float* res) {
+    __m256 accf[NT];
+    for (int t = 0; t < NT; ++t) accf[t] = _mm256_setzero_ps();
+    for (int i = 0; i < nblocks; ++i) {
+        const uint8_t* blk = row + (size_t) i * Fmt32<TY>::bytes;
+        __m256i acci[NT];
+        for (int t = 0; t < NT; ++t) acci[t] = _mm256_setzero_si256();
+        for (int j = 0; j < 4; ++j) {
+            for (int half = 0; half < 2; ++half) {
+                __m256i g, sgn, sc;
+                Fmt32<TY>::decode(blk, j, half, g, sgn, sc);
+                const int off = 64 * j + 32 * half;
+                for (int t = 0; t < NT; ++t) {
+                    const __m256i yv = _mm256_loadu_si256((const __m256i*) (y[t][i].qs + off));
+                    const __m256i ys = _mm256_sign_epi8(yv, sgn);
+                    acci[t] = _mm256_add_epi32(acci[t], _mm256_madd_epi16(_mm256_maddubs_epi16(g, ys), sc));
+                }
+            }
+        }
+        const float dx = h2f(u16(blk)) * Fmt32<TY>::K;
+        for (int t = 0; t < NT; ++t)
+            accf[t] = _mm256_fmadd_ps(_mm256_set1_ps(dx * y[t][i].d), _mm256_cvtepi32_ps(acci[t]), accf[t]);
+    }
+    for (int t = 0; t < NT; ++t) res[t] = hsum8(accf[t]);
+}
+
+// ---- IQ2_XS (17) with ggml's vectorized sign decode (arch/x86/quants.c, ggml_vec_dot_iq2_xs_q8_K):
+// the 7-bit sign indices never touch ksigns_iq2xs - the 8th sign bit is reconstructed by parity (two
+// shifts, a xor and ggml's bit_helper pshufb) and the per-half sign vectors come out of shuffles of one
+// 32-byte load.  The 8 scale bytes become all 16 half-scales with ggml's unpack trick.  The grid lookups
+// stay scalar loads into set_epi64x (ggml does the same).  Every token still only pays a load, a sign,
+// a maddubs, a madd and an add per half, into alternating accumulators.
+template <int NT>
+inline void row_dot_iq2xs(const uint8_t* row, int nblocks, const block_q8_K* const* y, float* res) {
+    static const uint8_t bit_sel[32] = {
+        1, 2, 4, 8, 16, 32, 64, (uint8_t) 0x80, 1, 2, 4, 8, 16, 32, 64, (uint8_t) 0x80,
+        1, 2, 4, 8, 16, 32, 64, (uint8_t) 0x80, 1, 2, 4, 8, 16, 32, 64, (uint8_t) 0x80 };
+    static const char shuf_even[32] = {   // sign bits of u16 0,1,2,3 (even bytes of the sign register)
+        0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2,
+        4, 4, 4, 4, 4, 4, 4, 4, 6, 6, 6, 6, 6, 6, 6, 6 };
+    static const char shuf_odd[32] = {    // sign bits of u16 4,5,6,7
+        8, 8, 8, 8, 8, 8, 8, 8, 10, 10, 10, 10, 10, 10, 10, 10,
+        12, 12, 12, 12, 12, 12, 12, 12, 14, 14, 14, 14, 14, 14, 14, 14 };
+    static const uint8_t bit_help[32] = {  // 0x80 when the 4-bit index has odd popcount (parity bit 7)
+        0x00, (uint8_t) 0x80, (uint8_t) 0x80, 0x00, (uint8_t) 0x80, 0x00, 0x00, (uint8_t) 0x80,
+        (uint8_t) 0x80, 0x00, 0x00, (uint8_t) 0x80, 0x00, (uint8_t) 0x80, (uint8_t) 0x80, 0x00,
+        0x00, (uint8_t) 0x80, (uint8_t) 0x80, 0x00, (uint8_t) 0x80, 0x00, 0x00, (uint8_t) 0x80,
+        (uint8_t) 0x80, 0x00, 0x00, (uint8_t) 0x80, 0x00, (uint8_t) 0x80, (uint8_t) 0x80, 0x00 };
+    static const uint8_t k_sc_shuffle[128] = {  // half h -> scale bytes 2h (lanes 0-7), 2h+1 (lanes 8-15)
+        0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
+        2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3,
+        4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5,
+        6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7,
+        8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9,
+        10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11,
+        12, 12, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 13,
+        14, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 15 };
+    const __m256i bsel = _mm256_loadu_si256((const __m256i*) bit_sel);
+    const __m256i shev = _mm256_loadu_si256((const __m256i*) shuf_even);
+    const __m256i shod = _mm256_loadu_si256((const __m256i*) shuf_odd);
+    const __m256i bhelp = _mm256_loadu_si256((const __m256i*) bit_help);
+    const __m256i m511 = _mm256_set1_epi16(511);
+    const __m128i m4 = _mm_set1_epi8(0xf), m1 = _mm_set1_epi8(1);
+    const __m256i one8 = _mm256_set1_epi8(1);
+
+    __m256 accf[NT];
+    for (int t = 0; t < NT; ++t) accf[t] = _mm256_setzero_ps();
+    for (int i = 0; i < nblocks; ++i) {
+        const uint8_t* blk = row + (size_t) i * 74;
+        // the 8 scale bytes -> 16 half-scales of 2*s+1, interleaved [a0, b0, a1, b1, ...] (ggml's unpack)
+        __m128i st = _mm_set1_epi64x((long long) u64(blk + 66));
+        st = _mm_unpacklo_epi8(_mm_and_si128(st, m4), _mm_and_si128(_mm_srli_epi16(st, 4), m4));
+        const __m128i scales = _mm_add_epi8(_mm_slli_epi16(st, 1), m1);
+        __m256i acc[NT][2];
+        for (int t = 0; t < NT; ++t) acc[t][0] = acc[t][1] = _mm256_setzero_si256();
+        for (int jj = 0; jj < 2; ++jj) {   // 128 values (16 u16) per step
+            const __m256i q2 = _mm256_loadu_si256((const __m256i*) (blk + 2 + 32 * jj));
+            const __m256i p7 = _mm256_srli_epi16(q2, 9);              // 7 sign bits per u16, even bytes
+            const __m256i p3 = _mm256_srli_epi16(q2, 13);             // sign index bits 4-6
+            const __m256i fsb = _mm256_or_si256(p7,
+                _mm256_shuffle_epi8(bhelp, _mm256_xor_si256(p7, p3)));  // + the parity sign bit 7
+            const __m256i s01 = _mm256_broadcastsi128_si256(_mm256_castsi256_si128(fsb)); // u16 0-7
+            const __m256i s23 = _mm256_broadcastsi128_si256(_mm256_extracti128_si256(fsb, 1)); // u16 8-15
+            alignas(32) uint16_t gi[16];
+            _mm256_store_si256((__m256i*) gi, _mm256_and_si256(q2, m511));
+            for (int h = 0; h < 4; ++h) {   // the four 32-value halves of this group
+                const int H = 4 * jj + h;   // global half index -> scales byte pair
+                const __m256i g = _mm256_set_epi64x((long long) iq2xs_grid[gi[4 * h + 3]],
+                                                   (long long) iq2xs_grid[gi[4 * h + 2]],
+                                                   (long long) iq2xs_grid[gi[4 * h + 1]],
+                                                   (long long) iq2xs_grid[gi[4 * h]]);
+                const __m256i sb = _mm256_shuffle_epi8(h < 2 ? s01 : s23, (h & 1) ? shod : shev);
+                const __m256i sgn = _mm256_or_si256(
+                    _mm256_cmpeq_epi8(_mm256_and_si256(sb, bsel), bsel), one8);
+                const __m256i sc = _mm256_cvtepi8_epi16(
+                    _mm_shuffle_epi8(scales, _mm_loadu_si128((const __m128i*) k_sc_shuffle + H)));
+                const int off = 128 * jj + 32 * h;
+                for (int t = 0; t < NT; ++t) {
+                    const __m256i yv = _mm256_loadu_si256((const __m256i*) (y[t][i].qs + off));
+                    const __m256i ys = _mm256_sign_epi8(yv, sgn);
+                    acc[t][h & 1] = _mm256_add_epi32(acc[t][h & 1],
+                        _mm256_madd_epi16(_mm256_maddubs_epi16(g, ys), sc));
+                }
+            }
+        }
+        const float dx = h2f(u16(blk)) * 0.125f;
+        for (int t = 0; t < NT; ++t)
+            accf[t] = _mm256_fmadd_ps(_mm256_set1_ps(dx * y[t][i].d),
+                _mm256_cvtepi32_ps(_mm256_add_epi32(acc[t][0], acc[t][1])), accf[t]);
+    }
+    for (int t = 0; t < NT; ++t) res[t] = hsum8(accf[t]);
+}
+
+template <int TY, int NT>
+inline void row_dot_any(const uint8_t* row, int nblocks, const block_q8_K* const* y, float* res) {
+    if constexpr (TY == 17) row_dot_iq2xs<NT>(row, nblocks, y, res);
+    else                    row_dot<TY, NT>(row, nblocks, y, res);
+}
+
+template <int TY, int NT>
+void gu_rows(const uint8_t* blob, size_t gu_row, size_t up_off, int n, const void* const* act, float* const* ff,
+             int r0, int r1) {
+    const block_q8_K* y[NT];
+    for (int t = 0; t < NT; ++t) y[t] = (const block_q8_K*) act[t];
+    const int nb = n / QK_K;
+    float g[NT], u[NT];
+    for (int r = r0; r < r1; ++r) {
+        row_dot_any<TY, NT>(blob + (size_t) r * gu_row, nb, y, g);
+        row_dot_any<TY, NT>(blob + up_off + (size_t) r * gu_row, nb, y, u);
+        for (int t = 0; t < NT; ++t) ff[t][r] = (g[t] / (1.f + std::exp(-g[t]))) * u[t];
+    }
+}
+
+template <int TY, int NT>
+void dot_rows(const uint8_t* w, size_t row_bytes, int n, const void* const* act, float* const* out, int r0, int r1) {
+    const block_q8_K* y[NT];
+    for (int t = 0; t < NT; ++t) y[t] = (const block_q8_K*) act[t];
+    float res[NT];
+    for (int r = r0; r < r1; ++r) {
+        row_dot_any<TY, NT>(w + (size_t) r * row_bytes, n / QK_K, y, res);
+        for (int t = 0; t < NT; ++t) out[t][r] = res[t];
+    }
+}
+
+template <int TY>
+void gu_rows_nt(int nt, const uint8_t* blob, size_t gu_row, size_t up_off, int n, const void* const* act,
+                float* const* ff, int r0, int r1) {
+    switch (nt) {
+        case 1: gu_rows<TY, 1>(blob, gu_row, up_off, n, act, ff, r0, r1); break;
+        case 2: gu_rows<TY, 2>(blob, gu_row, up_off, n, act, ff, r0, r1); break;
+        case 3: gu_rows<TY, 3>(blob, gu_row, up_off, n, act, ff, r0, r1); break;
+        case 4: gu_rows<TY, 4>(blob, gu_row, up_off, n, act, ff, r0, r1); break;
+        case 5: gu_rows<TY, 5>(blob, gu_row, up_off, n, act, ff, r0, r1); break;
+        case 6: gu_rows<TY, 6>(blob, gu_row, up_off, n, act, ff, r0, r1); break;
+        case 7: gu_rows<TY, 7>(blob, gu_row, up_off, n, act, ff, r0, r1); break;
+        default: gu_rows<TY, 8>(blob, gu_row, up_off, n, act, ff, r0, r1); break;
+    }
+}
+
+template <int TY>
+void dot_rows_nt(int nt, const uint8_t* w, size_t row_bytes, int n, const void* const* act, float* const* out, int r0,
+                 int r1) {
+    switch (nt) {
+        case 1: dot_rows<TY, 1>(w, row_bytes, n, act, out, r0, r1); break;
+        case 2: dot_rows<TY, 2>(w, row_bytes, n, act, out, r0, r1); break;
+        case 3: dot_rows<TY, 3>(w, row_bytes, n, act, out, r0, r1); break;
+        case 4: dot_rows<TY, 4>(w, row_bytes, n, act, out, r0, r1); break;
+        default: for (int t0 = 0; t0 < nt; t0 += 4) {
+            const int k = nt - t0 < 4 ? nt - t0 : 4;
+            dot_rows_nt<TY>(k, w, row_bytes, n, act + t0, out + t0, r0, r1);
+        }
+    }
+}
+
+}  // namespace
+
+bool iq256_supported(int type) noexcept {
+    return type == 16 || type == 17 || type == 18 || type == 21 || type == 22;
+}
+
+void iq256_gu_rows(int type, const uint8_t* blob, size_t gu_row, size_t up_off, int n, const void* const* act, int nt,
+                   float* const* ff, int r0, int r1) {
+    switch (type) {
+        case 16: gu_rows_nt<16>(nt, blob, gu_row, up_off, n, act, ff, r0, r1); break;
+        case 17: gu_rows_nt<17>(nt, blob, gu_row, up_off, n, act, ff, r0, r1); break;
+        case 18: gu_rows_nt<18>(nt, blob, gu_row, up_off, n, act, ff, r0, r1); break;
+        case 21: gu_rows_nt<21>(nt, blob, gu_row, up_off, n, act, ff, r0, r1); break;
+        case 22: gu_rows_nt<22>(nt, blob, gu_row, up_off, n, act, ff, r0, r1); break;
+        default: break;
+    }
+}
+
+void iq256_rows(int type, const uint8_t* w, size_t row_bytes, int n, const void* const* act, int nt, float* const* out,
+                int r0, int r1) {
+    switch (type) {
+        case 16: dot_rows_nt<16>(nt, w, row_bytes, n, act, out, r0, r1); break;
+        case 17: dot_rows_nt<17>(nt, w, row_bytes, n, act, out, r0, r1); break;
+        case 18: dot_rows_nt<18>(nt, w, row_bytes, n, act, out, r0, r1); break;
+        case 21: dot_rows_nt<21>(nt, w, row_bytes, n, act, out, r0, r1); break;
+        case 22: dot_rows_nt<22>(nt, w, row_bytes, n, act, out, r0, r1); break;
+        default: break;
+    }
+}
+
+// ---- IQ4_NL (type 20): 32-value blocks of 18 bytes (f16 d + 16 nibble bytes) against Q8_0 activations.
+// ggml-cpu's own AVX-2 dot (ggml_vec_dot_iq4_nl_q8_0) is single-token: every token redoes the nibble ->
+// kvalues_iq4nl pshufb decode and the |w| half of the signed x signed product.  Here both are computed once
+// per block; every token then costs a sign, a maddubs, a madd and an fmadd.  The arithmetic is ggml's - only
+// the order of the float additions differs.
+template <int NT>
+void iq4nl_rows(const uint8_t* w, size_t row_bytes, int n, const block_q8_0* const* y, float* const* out,
+                int r0, int r1) {
+    const __m128i values = _mm_loadu_si128((const __m128i*) kvalues_iq4nl);
+    const __m128i m4b = _mm_set1_epi8(0x0f);
+    const __m256i ones = _mm256_set1_epi16(1);
+    const int nb = n / QK4_NL;
+    for (int r = r0; r < r1; ++r) {
+        const uint8_t* row = w + (size_t) r * row_bytes;
+        __m256 accf[NT];
+        for (int t = 0; t < NT; ++t) accf[t] = _mm256_setzero_ps();
+        for (int ib = 0; ib < nb; ++ib) {
+            const uint8_t* blk = row + (size_t) ib * sizeof(block_iq4_nl);
+            const __m128i bits = _mm_loadu_si128((const __m128i*) (blk + 2));
+            const __m128i lo = _mm_and_si128(bits, m4b);                      // values 0..15
+            const __m128i hi = _mm_and_si128(_mm_srli_epi16(bits, 4), m4b);    // values 16..31
+            const __m256i q4 = _mm256_inserti128_si256(_mm256_castsi128_si256(_mm_shuffle_epi8(values, lo)),
+                                                       _mm_shuffle_epi8(values, hi), 1);
+            const __m256i aq = _mm256_sign_epi8(q4, q4);   // |w|: the unsigned operand of maddubs
+            const float dx = h2f(u16(blk));
+            for (int t = 0; t < NT; ++t) {
+                const block_q8_0& b = y[t][ib];
+                const __m256i q8 = _mm256_loadu_si256((const __m256i*) b.qs);
+                const __m256i p = _mm256_madd_epi16(_mm256_maddubs_epi16(aq, _mm256_sign_epi8(q8, q4)), ones);
+                accf[t] = _mm256_fmadd_ps(_mm256_set1_ps(dx * h2f(b.d)), _mm256_cvtepi32_ps(p), accf[t]);
+            }
+        }
+        for (int t = 0; t < NT; ++t) out[t][r] = hsum8(accf[t]);
+    }
+}
+
+void iq4nl256_down_rows(const uint8_t* w, size_t row_bytes, int n, const void* const* hq, int nt, float* const* out,
+                        int r0, int r1) {
+    const block_q8_0* y[8];
+    for (int t = 0; t < nt; ++t) y[t] = (const block_q8_0*) hq[t];
+    switch (nt) {
+        case 1: iq4nl_rows<1>(w, row_bytes, n, y, out, r0, r1); break;
+        case 2: iq4nl_rows<2>(w, row_bytes, n, y, out, r0, r1); break;
+        case 3: iq4nl_rows<3>(w, row_bytes, n, y, out, r0, r1); break;
+        case 4: iq4nl_rows<4>(w, row_bytes, n, y, out, r0, r1); break;
+        default: for (int t0 = 0; t0 < nt; t0 += 4) {
+            const int k = nt - t0 < 4 ? nt - t0 : 4;
+            iq4nl256_down_rows(w, row_bytes, n, hq + t0, k, out + t0, r0, r1);
+        }
+    }
+}
+
+}  // namespace strata::kernels::cpu
@@ -4,6 +4,7 @@
 #include "strata/kernels/cpu/native_expert.hpp"
 #include "strata/kernels/cpu/expert.hpp"
 #include "strata/kernels/cpu/iq_avx512.hpp"
+#include "strata/kernels/cpu/iq_avx2.hpp"
 #include "strata/kernels/cpu/expert_layout.hpp"

 #include "ggml.h"
@@ -76,12 +77,21 @@ void native_quant_h(const NativeFmt& f, const float* h, void* dst) {

 void native_gu_rows(const NativeFmt& f, const uint8_t* blob, const void* const* act, int nt, float* const* ff,
                    int r0, int r1) {
-    // the AVX-512 kernels decode the weights once for all tokens: 2.0-2.4x ggml-cpu at three tokens, no faster at
-    // one (both are bound by the codebook lookups, ~5 GB/s per core), measured by native_expert_parity
+    // the multi-token kernels decode the weights once for all tokens: 2.0-2.4x ggml-cpu at three tokens, no faster
+    // at one (all are bound by the codebook lookups, ~5 GB/s per core), measured by native_expert_parity.  AVX-512
+    // first, then the AVX-2 one (Zen 2/3, Intel 12th-14th gen); STRATA_NO_IQ512 / STRATA_NO_IQ256 fall back to
+    // ggml-cpu's single-token vec_dot.
    static const bool avx512 = cpu_avx512_ok() && std::getenv("STRATA_NO_IQ512") == nullptr;
-    if (avx512 && nt >= 2 && iq512_supported(f.gu_type)) {   // one token: ggml-cpu is as fast or faster
-        iq512_gu_rows(f.gu_type, blob, f.gu_row, f.up_off, (int) f.n_embd, act, nt, ff, r0, r1);
-        return;
+    static const bool avx2 = std::getenv("STRATA_NO_IQ256") == nullptr;
+    if (nt >= 2 && iq512_supported(f.gu_type)) {   // one token: ggml-cpu is as fast or faster
+        if (avx512) {
+            iq512_gu_rows(f.gu_type, blob, f.gu_row, f.up_off, (int) f.n_embd, act, nt, ff, r0, r1);
+            return;
+        }
+        if (avx2) {
+            iq256_gu_rows(f.gu_type, blob, f.gu_row, f.up_off, (int) f.n_embd, act, nt, ff, r0, r1);
+            return;
+        }
    }
    const ggml_vec_dot_t dot = traits(f.gu_type)->vec_dot;
    const int n = (int) f.n_embd;
@@ -99,6 +109,13 @@ void native_gu_rows(const NativeFmt& f, const uint8_t* blob, const void* const*

 void native_down_rows(const NativeFmt& f, const uint8_t* blob, const void* const* hq, int nt, float* const* out,
                      int r0, int r1) {
+    // IQ4_NL down rows: the AVX-2 multi-token kernel decodes the nibbles and absolutises the weights once per
+    // block instead of once per token; ggml-cpu's dot is single-token.  STRATA_NO_IQ4NL falls back to it.
+    static const bool iq4nl_mt = std::getenv("STRATA_NO_IQ4NL") == nullptr;
+    if (nt >= 2 && f.d_type == 20 && iq4nl_mt) {
+        iq4nl256_down_rows(blob + f.down_off, f.d_row, (int) f.n_ff, hq, nt, out, r0, r1);
+        return;
+    }
    const ggml_vec_dot_t dot = traits(f.d_type)->vec_dot;
    const int n = (int) f.n_ff;
    for (int r = r0; r < r1; ++r) {
@@ -1,5 +1,6 @@
 // src/kernels/cpu/pool.cpp - P2.S3: the CPU expert pool.  Read pool.hpp first; it explains the protocol.
 #include "strata/kernels/cpu/pool.hpp"
+#include "strata/core/progress.hpp"
 #include "strata/kernels/cpu/expert_layout.hpp"

 #include <algorithm>
@@ -8,6 +9,7 @@
 #include <immintrin.h>

 #include <cstdio>
+#include <cstdlib>

 #if defined(_WIN32)
 #define WIN32_LEAN_AND_MEAN
@@ -19,6 +21,12 @@

 namespace strata::kernels::cpu {

+namespace {
+constexpr uint64_t pack_head(uint32_t epoch, uint32_t n, uint32_t i) {
+    return ((uint64_t) epoch << 32) | ((uint64_t) n << 16) | (uint64_t) i;
+}
+}  // namespace
+
 std::vector<int> physical_cores(bool skip_first) {
    std::vector<int> cores;
 #if defined(_WIN32)
@@ -47,13 +55,38 @@ std::vector<int> physical_cores(bool skip_first) {
        }
    }
 #else
+    // The logical CPUs this process may run on, ONE PER PHYSICAL CORE (issue #40): SMT siblings share a core's
+    // load/store bandwidth, so a worker on each would put two workers on one core, as the Windows branch above
+    // explains.  sysfs names each CPU's (package, core); the first allowed CPU of each pair is kept, so a taskset
+    // that leaves out the first sibling still gets its core.  Without sysfs every allowed CPU counts, as before.
+    auto topo = [](int cpu, const char* what) -> long {
+        char path[96];
+        std::snprintf(path, sizeof path, "/sys/devices/system/cpu/cpu%d/topology/%s", cpu, what);
+        long v = -1;
+        if (std::FILE* f = std::fopen(path, "r")) {
+            if (std::fscanf(f, "%ld", &v) != 1) v = -1;
+            std::fclose(f);
+        }
+        return v;
+    };
+    std::vector<int> allowed;
    cpu_set_t set;
    CPU_ZERO(&set);
    if (sched_getaffinity(0, sizeof set, &set) == 0) {
        for (int i = 0; i < CPU_SETSIZE; ++i)
-            if (CPU_ISSET(i, &set)) cores.push_back(i);
+            if (CPU_ISSET(i, &set)) allowed.push_back(i);
    } else {
-        for (unsigned i = 0; i < std::thread::hardware_concurrency(); ++i) cores.push_back((int) i);
+        for (unsigned i = 0; i < std::thread::hardware_concurrency(); ++i) allowed.push_back((int) i);
+    }
+    std::vector<std::pair<long, long>> seen;
+    for (int cpu : allowed) {
+        const long pkg = topo(cpu, "physical_package_id"), core = topo(cpu, "core_id");
+        if (pkg >= 0 && core >= 0) {
+            const std::pair<long, long> key{pkg, core};
+            if (std::find(seen.begin(), seen.end(), key) != seen.end()) continue;   // an SMT sibling
+            seen.push_back(key);
+        }
+        cores.push_back(cpu);
    }
 #endif
    if (skip_first && !cores.empty()) cores.erase(cores.begin());
@@ -108,11 +141,50 @@ void restore_thread_affinity(long long previous) {
 #endif
 }

+namespace {
+std::atomic<const ExpertPool*> g_diag_pool{nullptr};
+void diag_active_pool(std::FILE* f) {
+    if (const ExpertPool* p = g_diag_pool.load()) p->diag(f);
+}
+int64_t now_ms() {
+    return std::chrono::duration_cast<std::chrono::milliseconds>(std::chrono::steady_clock::now().time_since_epoch())
+        .count();
+}
+}  // namespace
+
+void ExpertPool::diag(std::FILE* f) const {
+    const uint64_t h = head_.load();
+    std::fprintf(f, "  expert pool: epoch %u, batch epoch %u: %u of %u jobs claimed, %u done; %u of %d workers parked, "
+                    "%u sleeping; mode %d\n", epoch_.load(), (uint32_t) (h >> 32), (uint32_t) h & 0xffffu,
+                 (uint32_t) (h >> 16) & 0xffffu, done_.load(), parked_.load(), n_, sleepers_.load(), mode_);
+    std::fprintf(f, "  expert pool threads:");
+    for (int i = 0; i < n_; ++i) {
+        const int32_t s = wstate_[(size_t) i].load();
+        if (s == kParked) std::fprintf(f, " w%d=parked", i);
+        else if (s == kSleeping) std::fprintf(f, " w%d=sleeping", i);
+        else if (s == kBetween) std::fprintf(f, " w%d=draining", i);
+        else std::fprintf(f, " w%d=job%d", i, s);
+    }
+    const int32_t hs = hstate_.load();
+    const char* hn = hs == kIdle ? "idle" : hs == kWaitParked ? "waiting for the workers to park"
+                   : hs == kWaitDone ? "waiting for the jobs to finish" : "running a job";
+    std::fprintf(f, "; host %s", hn);
+    if (hs >= 0) std::fprintf(f, " %d", hs);
+    std::fprintf(f, " for %lld ms\n", (long long) (now_ms() - hstate_ms_.load()));
+}
+
 ExpertPool::ExpertPool(int n_workers, bool pin, bool host_works) : host_works_(host_works) {
+    if (const char* e = std::getenv("STRATA_POOL_SPIN_US"))   // a test knob; see kSpinBeforeSleep
+        spin_before_sleep_ = std::chrono::microseconds((std::max)(0, std::atoi(e)));
    const std::vector<int> cores = physical_cores(true);
    n_ = n_workers > 0 ? n_workers : (int) cores.size();
    if (n_ < 1) n_ = 1;
    scratch_.resize((size_t) n_);
+    wstate_.reset(new std::atomic<int32_t>[(size_t) n_]);
+    for (int i = 0; i < n_; ++i) wstate_[(size_t) i].store(kParked);
+    hstate_ms_.store(now_ms());
+    g_diag_pool.store(this);
+    strata::core::diag_pool_fn().store(&diag_active_pool);
    split_.resize((size_t) kMaxSplit);
    split_multi_.resize((size_t) kMaxSplitMulti);
    threads_.reserve((size_t) n_);
@@ -126,6 +198,8 @@ ExpertPool::ExpertPool(int n_workers, bool pin, bool host_works) : host_works_(h
 }

 ExpertPool::~ExpertPool() {
+    const ExpertPool* self = this;
+    g_diag_pool.compare_exchange_strong(self, nullptr);
    stop_.store(true, std::memory_order_release);
    // Bump the epoch so a PARKED worker notices the stop flag rather than sleeping through it.
    publish();
@@ -170,30 +244,106 @@ void ExpertPool::worker(int id) {
            if (stop_.load(std::memory_order_relaxed)) return;
            _mm_pause();
            if ((++spins & 1023u) != 0) continue;
-            if (std::chrono::steady_clock::now() - parked_at < kSpinBeforeSleep) continue;
+            if (std::chrono::steady_clock::now() - parked_at < spin_before_sleep_) continue;
            std::unique_lock<std::mutex> lk(sleep_mu_);
+            wstate_[(size_t) id].store(kSleeping, std::memory_order_relaxed);
            sleepers_.fetch_add(1, std::memory_order_seq_cst);
            sleep_cv_.wait(lk, [&] {
                return epoch_.load(std::memory_order_seq_cst) != seen || stop_.load(std::memory_order_relaxed);
            });
            sleepers_.fetch_sub(1, std::memory_order_relaxed);
+            wstate_[(size_t) id].store(kParked, std::memory_order_relaxed);
        }
        if (stop_.load(std::memory_order_acquire)) return;
-        seen = epoch_.load(std::memory_order_relaxed);
+        // acquire: the batch this epoch published (`head`, and the description before it) is visible from here
+        seen = epoch_.load(std::memory_order_acquire);
        parked_.fetch_sub(1, std::memory_order_acq_rel);   // leaving the park

        // Drain: one claim per iteration, so a slow worker takes fewer experts and a fast one takes more.
-        // Every job is the same size (all experts are 1,382,400 bytes), so there is nothing to schedule.
-        drain(id, scratch_[(size_t) id]);
+        // Every job is the same size (all experts are 1,382,400 bytes), so there is nothing to schedule.  Only
+        // this epoch's jobs: if the host has already moved on, the claims fail and the worker parks again.
+        wstate_[(size_t) id].store(kBetween, std::memory_order_relaxed);
+        drain(id, scratch_[(size_t) id], seen);
+        wstate_[(size_t) id].store(kParked, std::memory_order_relaxed);
        parked_.fetch_add(1, std::memory_order_acq_rel);   // back at the park
    }
 }

-void ExpertPool::drain(int id, ExpertScratch& scratch) {
+int ExpertPool::claim(uint32_t epoch) {
+    uint64_t h = head_.load(std::memory_order_acquire);
+    for (;;) {
+        if ((uint32_t) (h >> 32) != epoch) return -1;               // not the batch this thread woke for
+        const uint32_t n = (uint32_t) (h >> 16) & 0xffffu, i = (uint32_t) h & 0xffffu;
+        if (i >= n) return -1;                                       // exhausted
+        if (head_.compare_exchange_weak(h, h + 1, std::memory_order_acq_rel, std::memory_order_acquire))
+            return (int) i;
+    }
+}
+
+uint32_t ExpertPool::begin_batch(int n) {
+    if (n < 0 || n > 0xffff) {
+        std::fprintf(stderr, "strata: expert pool batch of %d jobs is out of range\n", n);
+        std::abort();
+    }
+    // Every job of the previous batch has completed (`wait_done`), and a claim of it can no longer succeed, so
+    // nothing adds to `done` until this batch's first claim - which the release below orders after the reset.
+    done_.store(0, std::memory_order_relaxed);
+    const uint32_t e = epoch_.load(std::memory_order_relaxed) + 1;   // only the host bumps the epoch
+    head_.store(pack_head(e, (uint32_t) n, 0), std::memory_order_release);
+    publish();
+    return e;
+}
+
+void ExpertPool::wait_parked(const char* what) {
+    hstate_.store(kWaitParked, std::memory_order_relaxed);
+    hstate_ms_.store(now_ms(), std::memory_order_relaxed);
+    uint32_t spins = 0;
+    std::chrono::steady_clock::time_point t0{};
+    while (parked_.load(std::memory_order_acquire) != (uint32_t) n_) {
+        _mm_pause();
+        if ((++spins & 1023u) != 0) continue;
+        const auto now = std::chrono::steady_clock::now();
+        if (spins == 1024u) t0 = now;
+        else if (now - t0 > kStall) {
+            std::fprintf(stderr, "strata: the CPU expert pool stalled %s (%u of %d workers parked) - stopping the engine "
+                                 "so the server can start it again (issue #29)\n",
+                         what, parked_.load(), n_);
+            std::fflush(stderr);
+            std::abort();
+        }
+    }
+}
+
+void ExpertPool::wait_done(int n) {
+    hstate_.store(kWaitDone, std::memory_order_relaxed);
+    hstate_ms_.store(now_ms(), std::memory_order_relaxed);
+    uint32_t spins = 0, seen = 0;
+    std::chrono::steady_clock::time_point t0{};
+    for (;;) {
+        const uint32_t d = done_.load(std::memory_order_acquire);
+        if (d >= (uint32_t) n) return;                                // `>=`: never a wait that an overshoot outlives
+        _mm_pause();
+        if ((++spins & 1023u) != 0) continue;
+        const auto now = std::chrono::steady_clock::now();
+        if (spins == 1024u || d != seen) { t0 = now; seen = d; }     // progress restarts the clock
+        else if (now - t0 > kStall) {
+            std::fprintf(stderr, "strata: the CPU expert pool stalled: %u of %d jobs done, %u of %d workers parked - "
+                                 "stopping the engine so the server can start it again (issue #29)\n",
+                         d, n, parked_.load(), n_);
+            std::fflush(stderr);
+            std::abort();
+        }
+    }
+}
+
+void ExpertPool::drain(int id, ExpertScratch& scratch, uint32_t epoch) {
    (void) id;
    for (;;) {
-        const uint32_t i = head_.fetch_add(1, std::memory_order_relaxed);
-        if (i >= (uint32_t) njobs_) break;
+        const int ci = claim(epoch);
+        if (ci < 0) break;
+        const uint32_t i = (uint32_t) ci;
+        if (id >= 0) wstate_[(size_t) id].store(ci, std::memory_order_relaxed);
+        else { hstate_.store(ci, std::memory_order_relaxed); hstate_ms_.store(now_ms(), std::memory_order_relaxed); }
        if (mode_ == 0) {
            const ExpertJob& j = jobs_[i];
            s2_expert_vnni_q(j.blob, *j.act, j.out, scratch);
@@ -267,15 +417,15 @@ void ExpertPool::drain(int id, ExpertScratch& scratch) {
 }

 void ExpertPool::run_phase(int mode, int n_tasks) {
-    while (parked_.load(std::memory_order_acquire) != (uint32_t) n_) _mm_pause();
+    wait_parked("before a phase");
    mode_ = mode;
    njobs_ = n_tasks;
-    head_.store(0, std::memory_order_relaxed);
-    done_.store(0, std::memory_order_relaxed);
-    publish();
-    if (host_works_) drain(-1, host_scratch_);
-    while (done_.load(std::memory_order_acquire) != (uint32_t) n_tasks) _mm_pause();
-    while (parked_.load(std::memory_order_acquire) != (uint32_t) n_) _mm_pause();
+    const uint32_t e = begin_batch(n_tasks);
+    if (host_works_) drain(-1, host_scratch_, e);
+    wait_done(n_tasks);
+    wait_parked("after a phase");
+    hstate_.store(kIdle, std::memory_order_relaxed);
+    hstate_ms_.store(now_ms(), std::memory_order_relaxed);
 }

 void ExpertPool::run_split(ExpertJob* jobs, int n) {
@@ -376,14 +526,12 @@ void ExpertPool::run(ExpertJob* jobs, int n) {
    // THE THREE PHASES ARE TIMED SEPARATELY.  They were one number, which cannot distinguish a pool that is
    // slow at the WORK from one that is slow at the SYNCHRONISATION - and those need opposite fixes.
    const auto t_a = std::chrono::steady_clock::now();
-    while (parked_.load(std::memory_order_acquire) != (uint32_t) n_) _mm_pause();
+    wait_parked("before a batch");
    const auto t_b = std::chrono::steady_clock::now();
    jobs_ = jobs;
    njobs_ = n;
    mode_ = 0;
-    head_.store(0, std::memory_order_relaxed);
-    done_.store(0, std::memory_order_relaxed);
-    publish();   // a release (seq_cst): jobs_/njobs_ are visible before the bump
+    const uint32_t e = begin_batch(n);   // done, then head (release), then the epoch: the batch is described first

    // ---- **THE HOST DRAINS TOO (R2.2), INSTEAD OF SPINNING ON `done_`.**
    //
@@ -401,19 +549,23 @@ void ExpertPool::run(ExpertJob* jobs, int n) {
    // device after `run()` returns, so the write must be published, not merely performed.
    if (host_works_) {
        for (;;) {
-            const uint32_t i = head_.fetch_add(1, std::memory_order_relaxed);
-            if (i >= (uint32_t) n) break;
-            const ExpertJob& j = jobs_[i];
+            const int ci = claim(e);
+            if (ci < 0) break;
+            hstate_.store(ci, std::memory_order_relaxed);
+            hstate_ms_.store(now_ms(), std::memory_order_relaxed);
+            const ExpertJob& j = jobs_[ci];
            s2_expert_vnni_q(j.blob, *j.act, j.out, host_scratch_);
            done_.fetch_add(1, std::memory_order_release);
        }
    }

-    while (done_.load(std::memory_order_acquire) != (uint32_t) n) _mm_pause();
+    wait_done(n);
    // And park again, so the next `run` starts from a known state.  See the header for why `done` alone is
    // not enough.
    const auto t_c = std::chrono::steady_clock::now();
-    while (parked_.load(std::memory_order_acquire) != (uint32_t) n_) _mm_pause();
+    wait_parked("after a batch");
+    hstate_.store(kIdle, std::memory_order_relaxed);
+    hstate_ms_.store(now_ms(), std::memory_order_relaxed);
    const auto t_d = std::chrono::steady_clock::now();

    ms_wait_park_ += std::chrono::duration<double, std::milli>(t_b - t_a).count();
@@ -0,0 +1,99 @@
+// src/kernels/cpu/pool_stress.cpp - issue #29: the expert pool under the load a big-VRAM card gives it.
+//
+// With most experts resident on the GPU, many layers hand the pool nothing, the workers go to sleep in the middle
+// of a request, and the next tiny batch (one or two experts) is often finished by the host and the awake workers
+// before a sleeper has even woken.  That late wake-up is what the park/claim protocol has to survive.  This drives
+// hundreds of thousands of such batches - 0-4 jobs, run() and run_split(), with pauses long enough for the workers
+// to sleep - and checks that every batch returns, with every job's output written.  A watchdog thread turns a
+// hang into a failure instead of a frozen test.
+//
+//   pool_stress [seconds]        (default 20)
+#include "strata/kernels/cpu/pool.hpp"
+#include "strata/kernels/cpu/expert.hpp"
+
+#include <atomic>
+#include <chrono>
+#include <cstdio>
+#include <cstdlib>
+#include <cstring>
+#include <random>
+#include <thread>
+#include <vector>
+
+namespace cpu = strata::kernels::cpu;
+
+int main(int argc, char** argv) {
+    const double seconds = argc > 1 ? std::atof(argv[1]) : 20.0;
+    const cpu::CpuFeatures feat = cpu::cpu_features();
+    if (!feat.usable()) {
+        std::printf("CPU lacks %s: pool_stress SKIPPED\n", feat.reason());
+        return 0;
+    }
+    constexpr int NJ = 4;
+    std::vector<std::vector<uint8_t>> blobs(NJ, std::vector<uint8_t>(cpu::BLOB, 0));
+    std::vector<float> x(cpu::H, 0.25f);
+    cpu::ActQ act;
+    cpu::act_quant_q8_1(x.data(), cpu::H, act);
+    std::vector<float> out((size_t) NJ * cpu::H);
+    std::vector<cpu::ExpertJob> jobs(NJ);
+    for (int e = 0; e < NJ; ++e) {
+        jobs[(size_t) e].blob = blobs[(size_t) e].data();
+        jobs[(size_t) e].act = &act;
+        jobs[(size_t) e].out = out.data() + (size_t) e * cpu::H;
+        jobs[(size_t) e].slot = e;
+    }
+
+    cpu::ExpertPool pool;
+    std::printf("pool_stress: %d workers + host, %.0f s\n", pool.workers(), seconds);
+
+    // the watchdog: a batch that does not return within 10 s is a hang
+    std::atomic<long long> beat{0};
+    std::atomic<bool> finished{false};
+    std::thread dog([&] {
+        long long last = -1;
+        auto since = std::chrono::steady_clock::now();
+        while (!finished.load()) {
+            std::this_thread::sleep_for(std::chrono::milliseconds(200));
+            const long long b = beat.load();
+            const auto now = std::chrono::steady_clock::now();
+            if (b != last) { last = b; since = now; continue; }
+            if (now - since > std::chrono::seconds(10)) {
+                std::printf("\npool_stress: HANG - batch %lld has not returned for 10 s\n", b);
+                std::fflush(stdout);
+                std::_Exit(3);
+            }
+        }
+    });
+
+    std::mt19937 rng(29);
+    const float SENTINEL = -1.2345e33f;
+    long long batches = 0, sleeps = 0, missed = 0;
+    const auto t0 = std::chrono::steady_clock::now();
+    while (std::chrono::steady_clock::now() - t0 < std::chrono::duration<double>(seconds)) {
+        // now and then a gap long enough for every worker to go to sleep (the pool sleeps after 20 ms)
+        if (rng() % 64 == 0) {
+            std::this_thread::sleep_for(std::chrono::milliseconds(21 + (int) (rng() % 10)));
+            ++sleeps;
+        } else if (rng() % 4 == 0) {
+            std::this_thread::sleep_for(std::chrono::microseconds(rng() % 300));
+        }
+        const int n = (int) (rng() % (NJ + 1));
+        for (int e = 0; e < n; ++e) std::fill(jobs[(size_t) e].out, jobs[(size_t) e].out + cpu::H, SENTINEL);
+        if (rng() % 2) pool.run(jobs.data(), n);
+        else pool.run_split(jobs.data(), n);
+        for (int e = 0; e < n; ++e)
+            if (jobs[(size_t) e].out[0] == SENTINEL || jobs[(size_t) e].out[cpu::H - 1] == SENTINEL) ++missed;
+        beat.fetch_add(1);
+        ++batches;
+    }
+    finished.store(true);
+    dog.join();
+    std::printf("pool_stress: %lld batches (%lld after the workers slept), %lld jobs not run\n", batches, sleeps,
+                missed);
+    if (missed) {
+        std::printf("pool_stress: FAILED\n");
+        return 1;
+    }
+    std::printf("pool_stress OK\n");
+    return 0;
+}
@@ -0,0 +1,128 @@
+// src/kernels/cuda/cvec.cu - see include/strata/kernels/cvec.hpp.
+#include "strata/kernels/cvec.hpp"
+
+#include <cuda_runtime.h>
+
+#include <stdexcept>
+
+namespace strata::kernels {
+namespace {
+
+constexpr int THREADS = 256;
+constexpr int MAXK = 16;   // n_embd up to 4096, held in registers between the dot and the update
+
+Cvec g_cvec;
+int* g_on = nullptr;
+bool g_on_host = false;
+
+// the fused hyper-connection read's gate (fused_gr.cu), so a write done here is bitwise the one it would have folded
+__device__ __forceinline__ float sigmoidf_(float x) { return 1.0f / (1.0f + __expf(-x)); }
+
+// one block per (stream, token): the pending write, then h . v over the stream, then the update
+__global__ void cvec_kernel(float* __restrict__ R, const float* __restrict__ dir, const float* __restrict__ s_l,
+                            const int* __restrict__ on, int mode, int64_t layer, int n, int hc, int64_t r_ld,
+                            const float* __restrict__ bo, int64_t bo_ld, const float* __restrict__ inj,
+                            int64_t inj_ld, int write) {
+    const int c = blockIdx.x;
+    const int64_t t = blockIdx.y;
+    float* r = R + t * r_ld + (int64_t) c * n;
+    const float s = s_l[layer];
+    const bool steer = *on != 0 && s != 0.0f;   // uniform over the block
+    if (!steer && !write) return;
+    const float* v = dir + layer * n;
+    const float w = write ? 2.0f * sigmoidf_(inj[t * inj_ld + c] / (float) hc) : 0.0f;
+    const float* b = write ? bo + t * bo_ld : nullptr;
+    float x[MAXK];
+    float dot = 0.0f;
+#pragma unroll
+    for (int k = 0; k < MAXK; ++k) {
+        const int d = threadIdx.x + k * THREADS;
+        if (d < n) {
+            float xv = r[d];
+            if (write) xv = fmaf(b[d], w, xv);
+            x[k] = xv;
+            if (steer && mode == 0) dot = fmaf(xv, v[d], dot);
+        }
+    }
+    if (steer && mode == 0) {
+        __shared__ float part[THREADS / 32];
+#pragma unroll
+        for (int o = 16; o > 0; o >>= 1) dot += __shfl_xor_sync(0xffffffffu, dot, o);
+        if ((threadIdx.x & 31) == 0) part[threadIdx.x >> 5] = dot;
+        __syncthreads();
+        if (threadIdx.x < 32) {
+            float p = threadIdx.x < THREADS / 32 ? part[threadIdx.x] : 0.0f;
+#pragma unroll
+            for (int o = 16; o > 0; o >>= 1) p += __shfl_xor_sync(0xffffffffu, p, o);
+            if (threadIdx.x == 0) part[0] = p;
+        }
+        __syncthreads();
+        dot = part[0] * s;   // s (h . v)
+    }
+#pragma unroll
+    for (int k = 0; k < MAXK; ++k) {
+        const int d = threadIdx.x + k * THREADS;
+        if (d < n) {
+            float xv = x[k];
+            if (steer) xv = mode == 0 ? fmaf(-dot, v[d], xv) : xv + v[d];
+            r[d] = xv;
+        }
+    }
+}
+
+}  // namespace
+
+const Cvec& cvec() { return g_cvec; }
+
+bool cvec_upload(const std::vector<float>& dir, const std::vector<float>& s, int mode, int first, int last,
+                 int64_t n_embd, int64_t hc, std::string& err) {
+    if (n_embd < 1 || n_embd > (int64_t) THREADS * MAXK) { err = "control vector: unsupported n_embd"; return false; }
+    if (s.empty() || dir.size() != s.size() * (size_t) n_embd) { err = "control vector: bad table sizes"; return false; }
+    float* d_dir = nullptr;
+    float* d_s = nullptr;
+    int* d_on = nullptr;
+    const int one = 1;
+    if (cudaMalloc(&d_dir, dir.size() * sizeof(float)) != cudaSuccess ||
+        cudaMalloc(&d_s, s.size() * sizeof(float)) != cudaSuccess || cudaMalloc(&d_on, sizeof(int)) != cudaSuccess ||
+        cudaMemcpy(d_dir, dir.data(), dir.size() * sizeof(float), cudaMemcpyHostToDevice) != cudaSuccess ||
+        cudaMemcpy(d_s, s.data(), s.size() * sizeof(float), cudaMemcpyHostToDevice) != cudaSuccess ||
+        cudaMemcpy(d_on, &one, sizeof(int), cudaMemcpyHostToDevice) != cudaSuccess) {
+        err = "control vector: device allocation failed";
+        return false;
+    }
+    g_cvec.dir = d_dir;
+    g_cvec.s = d_s;
+    g_cvec.on = d_on;
+    g_cvec.mode = mode;
+    g_cvec.first = first;
+    g_cvec.last = last;
+    g_cvec.n_embd = n_embd;
+    g_cvec.hc = hc;
+    g_cvec.steered.assign(s.size(), false);
+    for (size_t l = 0; l < s.size(); ++l) g_cvec.steered[l] = s[l] != 0.0f;
+    g_on = d_on;
+    g_on_host = true;
+    return true;
+}
+
+void cvec_set_enabled(bool on) {
+    if (g_on == nullptr || on == g_on_host) return;
+    cudaDeviceSynchronize();   // nothing in flight may still read the flag
+    const int v = on ? 1 : 0;
+    cudaMemcpy(g_on, &v, sizeof(int), cudaMemcpyHostToDevice);
+    g_on_host = on;
+}
+
+bool cvec_enabled() { return g_on != nullptr && g_on_host; }
+
+void cvec_apply(float* R, int64_t layer, int64_t T, int64_t r_ld, const float* bo, int64_t bo_ld, const float* inj,
+                int64_t inj_ld, bool write, void* stream) {
+    if (!g_cvec.loaded() || T < 1) return;
+    const dim3 grid((unsigned) g_cvec.hc, (unsigned) T);
+    cvec_kernel<<<grid, THREADS, 0, (cudaStream_t) stream>>>(R, g_cvec.dir, g_cvec.s, g_cvec.on, g_cvec.mode, layer,
+                                                            (int) g_cvec.n_embd, (int) g_cvec.hc, r_ld, bo, bo_ld,
+                                                            inj, inj_ld, write ? 1 : 0);
+    if (cudaPeekAtLastError() != cudaSuccess) throw std::runtime_error("cvec_apply: launch failed");
+}
+
+}  // namespace strata::kernels
@@ -0,0 +1,231 @@
+// src/kernels/cuda/kv_q4.cu - see include/strata/kernels/kv_q4.hpp. Q4_0 KV with Walsh-Hadamard rotation
+// (from PR #21 by code-martin; KV-streaming integration and the deterministic group maximum added on merge).
+#include "strata/kernels/kv_q4.hpp"
+#include "strata/kernels/f16_bits.hpp"
+#include "strata/kernels/kv_stream.hpp"
+
+#include <cuda_fp16.h>
+#include <cuda_runtime.h>
+#include <cstdio>
+#include <cstdlib>
+
+namespace strata::kernels {
+namespace {
+
+void check(const char* what) {
+    const cudaError_t e = cudaGetLastError();
+    if (e != cudaSuccess) {
+        std::fprintf(stderr, "kv_q4: %s: %s\n", what, cudaGetErrorString(e));
+        std::exit(1);
+    }
+}
+
+// Fast Walsh-Hadamard Transform for N = 256: one warp per row, 8 values per lane in registers.
+// Orthonormal (scale 1/sqrt(256) = 1/16), so it is its own inverse.
+__global__ void fwht256_kernel(const float* __restrict__ src, float* __restrict__ dst, int64_t n_rows, float scale) {
+    constexpr int warp_size = 32;
+    constexpr int N = 256;
+    constexpr int el_w = N / warp_size;   // 8
+
+    const int64_t r = (int64_t) blockIdx.x * blockDim.y + threadIdx.y;
+    if (r >= n_rows) return;
+
+    const float* row_src = src + r * N;
+    float* row_dst = dst + r * N;
+
+    float reg[el_w];
+    const int lane = threadIdx.x;
+
+#pragma unroll
+    for (int i = 0; i < el_w; ++i) reg[i] = row_src[i * warp_size + lane] * scale;
+
+    // the low 5 index bits live across lanes
+#pragma unroll
+    for (int h = 1; h < warp_size; h *= 2) {
+#pragma unroll
+        for (int j = 0; j < el_w; ++j) {
+            const float val = reg[j];
+            const float val2 = __shfl_xor_sync(0xffffffffu, val, h, warp_size);
+            reg[j] = (lane & h) == 0 ? val + val2 : val2 - val;
+        }
+    }
+    // the high 3 bits across each lane's registers
+#pragma unroll
+    for (int h = warp_size; h < N; h *= 2) {
+        const int step = h / warp_size;
+#pragma unroll
+        for (int j = 0; j < el_w; j += 2 * step) {
+#pragma unroll
+            for (int k = 0; k < step; ++k) {
+                const float x = reg[j + k];
+                const float y = reg[j + k + step];
+                reg[j + k] = x + y;
+                reg[j + k + step] = x - y;
+            }
+        }
+    }
+#pragma unroll
+    for (int i = 0; i < el_w; ++i) row_dst[i * warp_size + lane] = reg[i];
+}
+
+// One 32-value group in one warp (lane = element), ggml's q4_0: d = (the value of largest |x|) / -8,
+// q = clamp(trunc(x / d + 8.5), 0, 15). Returns the scale bits; `byte` is lane t's packed byte for t < 16
+// (element t in the low nibble, t + 16 in the high one). Ties in |x| resolve to the larger value in EVERY lane,
+// so all lanes agree on d (a plain `a > amax` could leave lanes with opposite signs and one block two scales).
+__device__ __forceinline__ uint16_t q4_group(float x, int lane, uint8_t& byte) {
+    float amax = fabsf(x), mval = x;
+#pragma unroll
+    for (int o = 16; o > 0; o >>= 1) {
+        const float a = __shfl_xor_sync(0xffffffffu, amax, o);
+        const float v = __shfl_xor_sync(0xffffffffu, mval, o);
+        if (a > amax || (a == amax && v > mval)) { amax = a; mval = v; }
+    }
+    const float d = mval / -8.0f;
+    const float id = d != 0.0f ? 1.0f / d : 0.0f;
+    int q = __float2int_rz(x * id + 8.5f);
+    const uint8_t qc = (uint8_t) (q < 0 ? 0 : (q > 15 ? 15 : q));
+    const uint8_t qhi = __shfl_down_sync(0xffffffffu, qc, 16);
+    byte = (uint8_t) (qc | (qhi << 4));
+    (void) lane;
+    return f16_from_f32(d);
+}
+
+__device__ __forceinline__ void q4_store(uint8_t* pool, long long row, int b, int lane, uint16_t d, uint8_t byte) {
+    block_q4_0* blk = reinterpret_cast<block_q4_0*>(pool + row * (long long) sizeof(block_q4_0) * 8) + b;
+    if (lane == 0) blk->d = d;
+    if (lane < 16) blk->qs[lane] = byte;
+}
+
+// One block = one 32-value group of one KV head of K (blockIdx.z = 0) or V (1); 32 threads. KV streaming: the VRAM
+// page only if the block is resident (table >= 0), the host copy always (identity layout) when there is one.
+__global__ void kv_append_q4_kernel(uint8_t* __restrict__ k_q4, uint8_t* __restrict__ v_q4,
+                                    const int32_t* __restrict__ table, const int32_t* __restrict__ step,
+                                    const float* __restrict__ kcur, const float* __restrict__ vcur,
+                                    int kv_heads, int head_dim, int page_size, KvHostPools host) {
+    const long long pos = (long long) __ldg(step + kStepPos);
+    const int h = blockIdx.x, b = blockIdx.y, t = threadIdx.x;
+    const bool is_v = blockIdx.z == 1;
+    const float x = (is_v ? vcur : kcur)[h * head_dim + b * QK4_0 + t];
+    uint8_t byte;
+    const uint16_t d = q4_group(x, t, byte);
+    const long long page = (long long) table[pos / page_size];
+    if (page >= 0) q4_store(is_v ? v_q4 : k_q4, (page * kv_heads + h) * page_size + (pos % page_size), b, t, d, byte);
+    if (host.k_q4 != nullptr)
+        q4_store(is_v ? host.v_q4 : host.k_q4, ((pos / page_size) * kv_heads + h) * page_size + (pos % page_size), b, t,
+                 d, byte);
+}
+
+// The prompt path: grid (T, kv_heads, groups), K then V; also into the staging pool (identity layout) when given.
+__global__ void kv_append_q4_batch_kernel(uint8_t* __restrict__ k_q4, uint8_t* __restrict__ v_q4,
+                                          const int32_t* __restrict__ table, int64_t pos0,
+                                          const float* __restrict__ K, const float* __restrict__ V,
+                                          int kv_heads, int head_dim, int page_size, int is_v_grid, KvHostPools host,
+                                          KvHostPools stage) {
+    const long long t = blockIdx.x;
+    const long long pos = pos0 + t;
+    const int h = blockIdx.y, b = blockIdx.z, th = threadIdx.x;
+    const bool is_v = is_v_grid != 0;
+    const float x = (is_v ? V : K)[t * (kv_heads * head_dim) + h * head_dim + b * QK4_0 + th];
+    uint8_t byte;
+    const uint16_t d = q4_group(x, th, byte);
+    const long long page = (long long) table[pos / page_size];
+    const long long row_id = ((pos / page_size) * kv_heads + h) * page_size + (pos % page_size);
+    if (page >= 0) q4_store(is_v ? v_q4 : k_q4, (page * kv_heads + h) * page_size + (pos % page_size), b, th, d, byte);
+    if (host.k_q4 != nullptr) q4_store(is_v ? host.v_q4 : host.k_q4, row_id, b, th, d, byte);
+    if (stage.k_q4 != nullptr) q4_store(is_v ? stage.v_q4 : stage.k_q4, row_id, b, th, d, byte);
+}
+
+// Gather step[kStepWidth] cells into FP16 scratch (the non-fused attention paths)
+__global__ void kv_gather_q4_kernel(const uint8_t* __restrict__ k_q4, const uint8_t* __restrict__ v_q4,
+                                    const int32_t* __restrict__ table, const int32_t* __restrict__ ids,
+                                    const int32_t* __restrict__ step, int kv_heads, int head_dim, int page_size,
+                                    uint16_t* __restrict__ k_scratch, uint16_t* __restrict__ v_scratch) {
+    const long long n_ids = (long long) __ldg(step + kStepWidth);
+    const int blocks_per_head = head_dim / QK4_0;                        // 8
+    const int bytes_per_head = blocks_per_head * sizeof(block_q4_0);    // 144
+    const long long total_blocks = n_ids * kv_heads * blocks_per_head;
+
+    const long long blk_idx = (long long) blockIdx.x * blockDim.y + threadIdx.y;
+    if (blk_idx >= total_blocks) return;
+
+    const int t = threadIdx.x;
+    const long long id = blk_idx / (kv_heads * blocks_per_head);
+    const int rem = (int) (blk_idx % (kv_heads * blocks_per_head));
+    const int h = rem / blocks_per_head;
+    const int b = rem % blocks_per_head;
+
+    const int cell = ids[id];
+    const long long page = (long long) table[cell / page_size];
+    const long long row = (page * kv_heads + h) * page_size + (cell % page_size);
+
+    const block_q4_0* k_blk = reinterpret_cast<const block_q4_0*>(k_q4 + row * bytes_per_head) + b;
+    const block_q4_0* v_blk = reinterpret_cast<const block_q4_0*>(v_q4 + row * bytes_per_head) + b;
+    const float kd = f32_from_f16(k_blk->d);
+    const float vd = f32_from_f16(v_blk->d);
+    const int j = t < 16 ? t : (t - 16);
+    const uint8_t k_byte = k_blk->qs[j];
+    const uint8_t v_byte = v_blk->qs[j];
+    const int kq = (t < 16) ? ((k_byte & 0x0F) - 8) : ((k_byte >> 4) - 8);
+    const int vq = (t < 16) ? ((v_byte & 0x0F) - 8) : ((v_byte >> 4) - 8);
+    const long long dst_offset = ((id * kv_heads + h) * head_dim) + (b * QK4_0 + t);
+    k_scratch[dst_offset] = f16_from_f32((float) kq * kd);
+    v_scratch[dst_offset] = f16_from_f32((float) vq * vd);
+}
+
+void need_256(const QsaShapes& s, const char* what) {
+    if (s.head_dim != 256) {
+        std::fprintf(stderr, "%s: head_dim must be 256 (the Hadamard transform's size)\n", what);
+        std::exit(1);
+    }
+}
+
+}  // namespace
+
+void fwht256_cuda(const float* src, float* dst, int64_t n_rows, void* stream) {
+    if (n_rows <= 0) return;
+    const int rows_per_block = 4;
+    const int64_t num_blocks = (n_rows + rows_per_block - 1) / rows_per_block;
+    fwht256_kernel<<<dim3((unsigned) num_blocks), dim3(32, rows_per_block), 0, (cudaStream_t) stream>>>(
+        src, dst, n_rows, 1.0f / 16.0f);
+    check("fwht256 launch");
+}
+
+void kv_append_q4_step(uint8_t* k_q4, uint8_t* v_q4, const int32_t* page_table, const int32_t* step,
+                       const float* kcur, const float* vcur, const QsaShapes& s, void* stream,
+                       const KvHostPools* host) {
+    need_256(s, "kv_append_q4");
+    const dim3 grid((unsigned) s.n_head_kv, (unsigned) (s.head_dim / QK4_0), 2);
+    kv_append_q4_kernel<<<grid, 32, 0, (cudaStream_t) stream>>>(
+        k_q4, v_q4, page_table, step, kcur, vcur, (int) s.n_head_kv, (int) s.head_dim, (int) s.page_size,
+        host ? *host : KvHostPools{});
+    check("kv_append_q4 launch");
+}
+
+void kv_append_q4(uint8_t* k_q4, uint8_t* v_q4, const int32_t* page_table, int64_t pos0, int64_t T, const float* K,
+                  const float* V, const QsaShapes& s, void* stream, const KvHostPools* host, const KvHostPools* stage) {
+    if (T <= 0) return;
+    need_256(s, "kv_append_q4");
+    const dim3 grid((unsigned) T, (unsigned) s.n_head_kv, (unsigned) (s.head_dim / QK4_0));
+    cudaStream_t cs = (cudaStream_t) stream;
+    const KvHostPools h = host ? *host : KvHostPools{}, st = stage ? *stage : KvHostPools{};
+    for (int is_v = 0; is_v < 2; ++is_v)
+        kv_append_q4_batch_kernel<<<grid, 32, 0, cs>>>(k_q4, v_q4, page_table, pos0, K, V, (int) s.n_head_kv,
+                                                       (int) s.head_dim, (int) s.page_size, is_v, h, st);
+    check("kv_append_q4 batch launch");
+}
+
+void kv_gather_q4_step(const uint8_t* k_q4, const uint8_t* v_q4, const int32_t* page_table, const int32_t* ids,
+                       const int32_t* step, int64_t max_ids, const QsaShapes& s, uint16_t* k_scratch,
+                       uint16_t* v_scratch, void* stream) {
+    if (max_ids <= 0) return;
+    const int blocks_per_head = (int) (s.head_dim / QK4_0);
+    const int64_t total_blocks = max_ids * s.n_head_kv * blocks_per_head;
+    const int rows_per_block = 4;
+    const unsigned num_blocks = (unsigned) ((total_blocks + rows_per_block - 1) / rows_per_block);
+    kv_gather_q4_kernel<<<dim3(num_blocks), dim3(32, rows_per_block), 0, (cudaStream_t) stream>>>(
+        k_q4, v_q4, page_table, ids, step, (int) s.n_head_kv, (int) s.head_dim, (int) s.page_size, k_scratch,
+        v_scratch);
+    check("kv_gather_q4 launch");
+}
+
+}  // namespace strata::kernels
@@ -1,5 +1,6 @@
 // src/kernels/cuda/kv_stream.cu - see include/strata/kernels/kv_stream.hpp.
 #include "strata/kernels/kv_stream.hpp"
+#include "strata/kernels/kv_q4.hpp"
 #include "strata/kernels/kv_q8.hpp"

 #include <cuda_runtime.h>
@@ -29,10 +30,15 @@ struct Runs {
    int n;
 };

-Runs runs_of(const QsaAttnPools& slots, const KvHostPools& host, bool int8, const QsaShapes& s) {
+Runs runs_of(const QsaAttnPools& slots, const KvHostPools& host, int fmt, const QsaShapes& s) {
    const int rows = (int) (s.n_head_kv * s.page_size);
    Runs r{};
-    if (int8) {
+    if (fmt == kKvQ4) {
+        const int bytes = rows * (int) kv_q4_bytes_per_head((int) s.head_dim);
+        r.src[0] = (const uint8_t*) host.k_q4; r.dst[0] = (uint8_t*) slots.k_q4; r.len[0] = bytes;
+        r.src[1] = (const uint8_t*) host.v_q4; r.dst[1] = (uint8_t*) slots.v_q4; r.len[1] = bytes;
+        r.n = 2;
+    } else if (fmt == kKvInt8) {
        const int codes = rows * (int) s.head_dim, scales = rows * (int) (s.head_dim / KV_Q8_GROUP) * 2;
        r.src[0] = (const uint8_t*) host.k_q;     r.dst[0] = (uint8_t*) slots.k_q;     r.len[0] = codes;
        r.src[1] = (const uint8_t*) host.v_q;     r.dst[1] = (uint8_t*) slots.v_q;     r.len[1] = codes;
@@ -183,9 +189,10 @@ __global__ void ring_kernel(int32_t* table, long long n_blocks, long long n_slot

 }  // namespace

-uint64_t kv_block_bytes(const QsaShapes& s, bool int8) {
+uint64_t kv_block_bytes(const QsaShapes& s, int fmt) {
    const uint64_t rows = (uint64_t) (s.n_head_kv * s.page_size);
-    return int8 ? rows * (uint64_t) s.head_dim * 2 + rows * (uint64_t) (s.head_dim / KV_Q8_GROUP) * 2 * 2
+    if (fmt == kKvQ4) return rows * kv_q4_bytes_per_head((int) s.head_dim) * 2;
+    return fmt == kKvInt8 ? rows * (uint64_t) s.head_dim * 2 + rows * (uint64_t) (s.head_dim / KV_Q8_GROUP) * 2 * 2
                : rows * (uint64_t) s.head_dim * 2 * 2;
 }

@@ -194,7 +201,7 @@ void kv_stream_reset(const KvStreamMap& m, void* stream) {
    check("reset");
 }

-void kv_stream_resolve(const KvStreamMap& m, const QsaAttnPools& slots, const KvHostPools& host, bool int8,
+void kv_stream_resolve(const KvStreamMap& m, const QsaAttnPools& slots, const KvHostPools& host, int fmt,
                       const int32_t* ids, const int32_t* steps, int64_t n_q, int64_t cap, const QsaShapes& s,
                       void* stream) {
    if (n_q <= 0) return;
@@ -204,7 +211,7 @@ void kv_stream_resolve(const KvStreamMap& m, const QsaAttnPools& slots, const Kv
    }
    resolve_kernel<<<1, RT, 0, (cudaStream_t) stream>>>(m, ids, steps, (int) n_q, (int) cap, (int) s.page_size);
    check("resolve");
-    copy_kernel<<<96, 128, 0, (cudaStream_t) stream>>>(m, runs_of(slots, host, int8, s));
+    copy_kernel<<<96, 128, 0, (cudaStream_t) stream>>>(m, runs_of(slots, host, fmt, s));
    check("copy");
 }

@@ -213,9 +220,9 @@ void kv_ring_table(int32_t* page_table, int64_t n_blocks, int64_t n_slots, void*
    check("ring table");
 }

-void kv_ring_restore(const QsaAttnPools& slots, const KvHostPools& host, bool int8, int64_t b0, int64_t b1,
+void kv_ring_restore(const QsaAttnPools& slots, const KvHostPools& host, int fmt, int64_t b0, int64_t b1,
                     int64_t n_slots, const QsaShapes& s, void* stream) {
-    const Runs r = runs_of(slots, host, int8, s);
+    const Runs r = runs_of(slots, host, fmt, s);
    for (int64_t b = b0; b < b1;) {
        const int64_t sl = b % n_slots, run = std::min<int64_t>(b1 - b, n_slots - sl);   // up to the ring's end
        for (int a = 0; a < r.n; ++a)
@@ -226,10 +233,10 @@ void kv_ring_restore(const QsaAttnPools& slots, const KvHostPools& host, bool in
    }
 }

-void kv_stage_from_host(const QsaAttnPools& stage, const KvHostPools& host, bool int8, int64_t n_blocks,
+void kv_stage_from_host(const QsaAttnPools& stage, const KvHostPools& host, int fmt, int64_t n_blocks,
                        const QsaShapes& s, void* stream) {
    if (n_blocks <= 0) return;
-    const Runs r = runs_of(stage, host, int8, s);
+    const Runs r = runs_of(stage, host, fmt, s);
    for (int a = 0; a < r.n; ++a)
        if (cudaMemcpyAsync(r.dst[a], r.src[a], (size_t) (n_blocks * r.len[a]), cudaMemcpyDefault,
                            (cudaStream_t) stream) != cudaSuccess)
@@ -86,6 +86,76 @@ __global__ void conv_residual_kernel(const float* history, const float* normaliz
    // Exact hidden/result alias is safe: each thread owns one element.
    result[c] = __fadd_rn(hidden[c], __fadd_rn(gated[c], activation));
 }
+// ---- the batch: T tokens, the same arithmetic per element as the kernels above
+// weighted_rms_norm (native_gr_norm.cu) with the gamma row repeating every H rows (one token's H groups)
+__device__ float norm_warp_sum(float value) {
+#pragma unroll
+    for (int offset = 16; offset > 0; offset >>= 1) value += __shfl_xor_sync(0xffffffffu, value, offset, 32);
+    return value;
+}
+__global__ void rms_rep_kernel(const float* __restrict__ input, const float* __restrict__ gamma,
+                               float* __restrict__ output) {
+    constexpr int BlockSize = 1024;             // native_gr_rms_norm_weighted's choice for 2560 columns
+    const int tid = threadIdx.x;
+    const size_t row_offset = size_t(blockIdx.x) * N;
+    input += row_offset;
+    output += row_offset;
+    gamma += size_t(blockIdx.x % H) * N;
+    float partial = 0.0f;
+    for (int col = tid; col < N; col += BlockSize) {
+        const float value = input[col];
+        partial += value * value;
+    }
+    __shared__ float sums[32];
+    partial = norm_warp_sum(partial);
+    const int lane = tid % 32;
+    if (lane == 0) sums[tid / 32] = partial;
+    __syncthreads();
+    partial = 0.0f;
+    if (lane < BlockSize / 32) partial = sums[lane];
+    partial = norm_warp_sum(partial);
+    const float mean = partial / N;
+    const float scale = rsqrtf(mean + NG_RMS_EPS);
+    for (int col = tid; col < N; col += BlockSize) output[col] = scale * input[col] * gamma[col];
+}
+__global__ void broadcast_batch_kernel(const float* value, const float* gate, float* gated, int T) {
+    const size_t i = size_t(blockIdx.x) * blockDim.x + threadIdx.x;
+    if (i >= size_t(T) * D) return;
+    const size_t t = i / D, d = i % D;
+    gated[i] = __fmul_rn(value[t * N + d % N], gate[t * H + d / N]);
+}
+// the dilated conv (taps 9, 6, 3 tokens back and this one) and the residual; a tap before the chunk reads the history
+__global__ void conv_residual_batch_kernel(const float* history, const float* normalized, const uint16_t* weights,
+                                           float* hidden, const float* gated, int T) {
+    const size_t i = size_t(blockIdx.x) * blockDim.x + threadIdx.x;
+    if (i >= size_t(T) * D) return;
+    const int t = int(i / D), c = int(i % D);
+    float sum = 0;
+#pragma unroll
+    for (int k = 0; k < 4; ++k) {
+        const int p = t - 9 + 3 * k;             // the token this tap reads (k == 3: this one)
+        const float x = p >= 0 ? normalized[size_t(p) * D + c] : history[size_t(c) * HISTORY + (9 + p)];
+        const float wk = __half2float(__ushort_as_half(weights[c * 4 + k]));
+        const float term = __fmul_rn(x, wk);
+        sum = k == 0 ? term : __fadd_rn(sum, term);
+    }
+    const float activation = sum / (1.0f + expf(-sum));
+    hidden[i] = __fadd_rn(hidden[i], __fadd_rn(gated[i], activation));
+}
+// the history after the chunk: the last nine normalized rows (older ones from the history when T < 9)
+__global__ void history_batch_kernel(float* history, const float* normalized, int T) {
+    const int c = blockIdx.x * blockDim.x + threadIdx.x;
+    if (c >= D) return;
+    float h[HISTORY];
+#pragma unroll
+    for (int r = 0; r < HISTORY; ++r) {
+        const int p = T - HISTORY + r;
+        h[r] = p >= 0 ? normalized[size_t(p) * D + c] : history[size_t(c) * HISTORY + (T + r)];
+    }
+#pragma unroll
+    for (int r = 0; r < HISTORY; ++r) history[size_t(c) * HISTORY + r] = h[r];
+}
+
 struct Span { const void* p; size_t bytes; size_t alignment; };
 bool overlaps(Span a, Span b) {
    const auto x = reinterpret_cast<uintptr_t>(a.p), y = reinterpret_cast<uintptr_t>(b.p);
@@ -131,4 +201,21 @@ void native_ple_postops(const float* projected_key, const float* hidden,
    conv_residual_kernel<<<D/256,256,0,st>>>(history,b.normalized,w.conv1d_f16,hidden,b.gated,b.conv,b.result);
    launch_check();
 }
+
+void native_ple_postops_batch(float* key, float* hidden, const float* value, float* history, const PleWeights& w,
+                              float* query_norm, float* gated, float* gate, int T, void* stream) {
+    if (!stream || T <= 0 || !key || !hidden || !value || !history || !query_norm || !gated || !gate)
+        throw std::invalid_argument("native PLE postops batch: null input or empty batch");
+    auto st = static_cast<cudaStream_t>(stream);
+    const unsigned rows = unsigned(T) * H;
+    const unsigned blocks = unsigned((size_t(T) * D + 255) / 256);
+    rms_rep_kernel<<<rows, 1024, 0, st>>>(key, w.norm_key, key);
+    rms_rep_kernel<<<rows, 1024, 0, st>>>(hidden, w.norm_query, query_norm);
+    gate_kernel<<<rows, 512, 0, st>>>(key, query_norm, gate, 1.0f / std::sqrt(float(N)));
+    broadcast_batch_kernel<<<blocks, 256, 0, st>>>(value, gate, gated, T);
+    rms_rep_kernel<<<rows, 1024, 0, st>>>(gated, w.norm_conv, query_norm);
+    conv_residual_batch_kernel<<<blocks, 256, 0, st>>>(history, query_norm, w.conv1d_f16, hidden, gated, T);
+    history_batch_kernel<<<D / 256, 256, 0, st>>>(history, query_norm, T);
+    launch_check();
+}
 } // namespace strata::kernels
@@ -1,6 +1,7 @@
 // src/kernels/cuda/qsa_decode_attn.cu - see include/strata/kernels/qsa_decode_attn.hpp.
 #include "strata/kernels/qsa_decode_attn.hpp"
 #include "strata/kernels/kv_q8.hpp"
+#include "strata/kernels/kv_q4.hpp"

 #include <cuda_fp16.h>
 #include <cuda_runtime.h>
@@ -30,9 +31,9 @@ __device__ __forceinline__ float warp_max(float v) {
 }

 // 8 consecutive values of one cell's key or value row for KV head `kvh`, dimensions [d0, d0+8).
-template <bool INT8>
+template <int KV_MODE>
 __device__ __forceinline__ void load8(const QsaAttnPools& p, bool value, long long row, int d0, float* out) {
-    if constexpr (!INT8) {
+    if constexpr (KV_MODE == 0) {
        const uint16_t* base = (value ? p.v_pool : p.k_pool) + row * HD + d0;
        const uint4 raw = *reinterpret_cast<const uint4*>(base);
        const __half2* h2 = reinterpret_cast<const __half2*>(&raw);
@@ -42,7 +43,7 @@ __device__ __forceinline__ void load8(const QsaAttnPools& p, bool value, long lo
            out[2 * j] = f.x;
            out[2 * j + 1] = f.y;
        }
-    } else {
+    } else if constexpr (KV_MODE == 1) {
        const int8_t* codes = (value ? p.v_q : p.k_q) + row * HD + d0;
        const uint16_t sbits = (value ? p.v_scale : p.k_scale)[row * (HD / KV_Q8_GROUP) + d0 / KV_Q8_GROUP];
        const float sc = __half2float(__ushort_as_half(sbits));
@@ -50,10 +51,25 @@ __device__ __forceinline__ void load8(const QsaAttnPools& p, bool value, long lo
        const int8_t* c = reinterpret_cast<const int8_t*>(&raw);
 #pragma unroll
        for (int j = 0; j < 8; ++j) out[j] = (float) c[j] * sc;
+    } else {
+        constexpr int bytes_per_head = (HD / QK4_0) * sizeof(block_q4_0);
+        const int b = d0 / QK4_0;
+        const int rem = d0 % QK4_0;
+        const block_q4_0* blk = reinterpret_cast<const block_q4_0*>((value ? p.v_q4 : p.k_q4) + row * bytes_per_head) + b;
+        const float d = __half2float(__ushort_as_half(blk->d));
+        const int j = (rem == 0 || rem == 16) ? 0 : 8;
+        const uint8_t* bytes = blk->qs + j;
+        if (rem < 16) {
+#pragma unroll
+            for (int k = 0; k < 8; ++k) out[k] = (float) ((int)(bytes[k] & 0x0F) - 8) * d;
+        } else {
+#pragma unroll
+            for (int k = 0; k < 8; ++k) out[k] = (float) ((int)(bytes[k] >> 4) - 8) * d;
+        }
    }
 }

-template <bool INT8>
+template <int KV_MODE>
 __global__ void __launch_bounds__(THREADS) attn_chunk_kernel(const float* __restrict__ q, QsaAttnPools p,
                                                             const int32_t* __restrict__ ids,
                                                             const int32_t* __restrict__ step, int n_kv_heads,
@@ -98,7 +114,7 @@ __global__ void __launch_bounds__(THREADS) attn_chunk_kernel(const float* __rest
            continue;
        }
        float k8[8];
-        load8<INT8>(p, false, srow[c], lane * 8, k8);
+        load8<KV_MODE>(p, false, srow[c], lane * 8, k8);
 #pragma unroll
        for (int h = 0; h < G; ++h) {
            const float4 qa = *reinterpret_cast<const float4*>(&sq[h][lane * 8]);
@@ -128,11 +144,21 @@ __global__ void __launch_bounds__(THREADS) attn_chunk_kernel(const float* __rest
    for (int h = 0; h < G; ++h) acc[h] = 0.0f;
    for (int c = 0; c < n_here; ++c) {
        float v;
-        if constexpr (!INT8) {
+        if constexpr (KV_MODE == 0) {
            v = __half2float(__ushort_as_half(p.v_pool[srow[c] * HD + t]));
-        } else {
+        } else if constexpr (KV_MODE == 1) {
            const float sc = __half2float(__ushort_as_half(p.v_scale[srow[c] * (HD / KV_Q8_GROUP) + t / KV_Q8_GROUP]));
            v = (float) p.v_q[srow[c] * HD + t] * sc;
+        } else {
+            constexpr int bytes_per_head = (HD / QK4_0) * sizeof(block_q4_0);
+            const int b = t / QK4_0;
+            const int rem = t % QK4_0;
+            const block_q4_0* blk = reinterpret_cast<const block_q4_0*>(p.v_q4 + srow[c] * bytes_per_head) + b;
+            const float d = __half2float(__ushort_as_half(blk->d));
+            const int j = rem < 16 ? rem : (rem - 16);
+            const uint8_t byte = blk->qs[j];
+            const int nibble = (rem < 16) ? ((byte & 0x0F) - 8) : ((byte >> 4) - 8);
+            v = (float) nibble * d;
        }
 #pragma unroll
        for (int h = 0; h < G; ++h) acc[h] = fmaf(sp[h][c], v, acc[h]);
@@ -176,7 +202,7 @@ void qsa_decode_attn_batch(const float* q, const QsaAttnPools& pools, const int3
        std::fprintf(stderr, "qsa_decode_attn_batch: unsupported geometry or missing buffers\n");
        std::exit(1);
    }
-    const bool int8 = pools.k_q != nullptr;
+    const int kv_mode = pools.k_q4 != nullptr ? 2 : (pools.k_q != nullptr ? 1 : 0);
    const int n_chunks = (int) ((cap + CHUNK - 1) / CHUNK);
    // per query: [acc: n_chunks*n_head*HD][m: n_chunks*n_head][l: n_chunks*n_head], all offsets from one stride
    const long long stride = (long long) qsa_decode_attn_scratch_floats(cap, s);
@@ -186,12 +212,15 @@ void qsa_decode_attn_batch(const float* q, const QsaAttnPools& pools, const int3
    const float scale = 1.0f / sqrtf((float) HD);
    const dim3 grid((unsigned) n_chunks, (unsigned) s.n_head_kv, (unsigned) n_q);
    cudaStream_t st = (cudaStream_t) stream;
-    if (int8)
-        attn_chunk_kernel<true><<<grid, THREADS, 0, st>>>(q, pools, ids, steps, (int) s.n_head_kv, (int) s.page_size,
-                                                          scale, part_acc, part_m, part_l, n_chunks, (int) cap, stride);
+    if (kv_mode == 2)
+        attn_chunk_kernel<2><<<grid, THREADS, 0, st>>>(q, pools, ids, steps, (int) s.n_head_kv, (int) s.page_size,
+                                                        scale, part_acc, part_m, part_l, n_chunks, (int) cap, stride);
+    else if (kv_mode == 1)
+        attn_chunk_kernel<1><<<grid, THREADS, 0, st>>>(q, pools, ids, steps, (int) s.n_head_kv, (int) s.page_size,
+                                                        scale, part_acc, part_m, part_l, n_chunks, (int) cap, stride);
    else
-        attn_chunk_kernel<false><<<grid, THREADS, 0, st>>>(q, pools, ids, steps, (int) s.n_head_kv, (int) s.page_size,
-                                                           scale, part_acc, part_m, part_l, n_chunks, (int) cap, stride);
+        attn_chunk_kernel<0><<<grid, THREADS, 0, st>>>(q, pools, ids, steps, (int) s.n_head_kv, (int) s.page_size,
+                                                        scale, part_acc, part_m, part_l, n_chunks, (int) cap, stride);
    attn_merge_kernel<<<dim3((unsigned) s.n_head, (unsigned) n_q), HD, 0, st>>>(part_acc, part_m, part_l, n_chunks,
                                                                                  attn, stride);
    const cudaError_t e = cudaGetLastError();
@@ -213,8 +242,8 @@ void qsa_decode_attn_step(const float* q, const QsaAttnPools& pools, const int32
        std::fprintf(stderr, "qsa_decode_attn: unsupported geometry or missing buffers\n");
        std::exit(1);
    }
-    const bool int8 = pools.k_q != nullptr;
-    if (int8 ? (!pools.v_q || !pools.k_scale || !pools.v_scale) : (!pools.k_pool || !pools.v_pool)) {
+    const int kv_mode = pools.k_q4 != nullptr ? 2 : (pools.k_q != nullptr ? 1 : 0);
+    if (kv_mode == 2 ? (!pools.v_q4) : (kv_mode == 1 ? (!pools.v_q || !pools.k_scale || !pools.v_scale) : (!pools.k_pool || !pools.v_pool))) {
        std::fprintf(stderr, "qsa_decode_attn: incomplete KV pools\n");
        std::exit(1);
    }
@@ -225,12 +254,15 @@ void qsa_decode_attn_step(const float* q, const QsaAttnPools& pools, const int32
    const float scale = 1.0f / sqrtf((float) HD);
    const dim3 grid((unsigned) n_chunks, (unsigned) s.n_head_kv);
    cudaStream_t st = (cudaStream_t) stream;
-    if (int8)
-        attn_chunk_kernel<true><<<grid, THREADS, 0, st>>>(q, pools, ids, step, (int) s.n_head_kv, (int) s.page_size,
-                                                          scale, part_acc, part_m, part_l, n_chunks);
+    if (kv_mode == 2)
+        attn_chunk_kernel<2><<<grid, THREADS, 0, st>>>(q, pools, ids, step, (int) s.n_head_kv, (int) s.page_size,
+                                                        scale, part_acc, part_m, part_l, n_chunks);
+    else if (kv_mode == 1)
+        attn_chunk_kernel<1><<<grid, THREADS, 0, st>>>(q, pools, ids, step, (int) s.n_head_kv, (int) s.page_size,
+                                                        scale, part_acc, part_m, part_l, n_chunks);
    else
-        attn_chunk_kernel<false><<<grid, THREADS, 0, st>>>(q, pools, ids, step, (int) s.n_head_kv, (int) s.page_size,
-                                                           scale, part_acc, part_m, part_l, n_chunks);
+        attn_chunk_kernel<0><<<grid, THREADS, 0, st>>>(q, pools, ids, step, (int) s.n_head_kv, (int) s.page_size,
+                                                        scale, part_acc, part_m, part_l, n_chunks);
    attn_merge_kernel<<<(unsigned) s.n_head, HD, 0, st>>>(part_acc, part_m, part_l, n_chunks, attn);
    const cudaError_t e = cudaGetLastError();
    if (e != cudaSuccess) {
@@ -1,6 +1,6 @@
 // src/kernels/cuda/sampler.cu - P2.S2: the sampler chain, in the order docs/sampling.md settles.
 //
-//     penalties  ->  top_k  ->  top_p  ->  temperature  ->  pick
+//     penalties -> top_k -> min_p -> top_p -> temperature -> penalties -> pick
 //
 // THE ORDER IS THE WHOLE CONTENT OF THIS FILE.  `docs/sampling.md` transcribes it from llama.cpp's own chain
 // (`common/sampling.cpp` L357/360/375/381/399) and the two facts that are easy to get backwards are that
@@ -9,10 +9,9 @@
 // valid token, so only a comparison at the distribution level can tell them apart; the parity test does that
 // explicitly by running the wrong order and requiring it to differ.
 //
-// Everything happens on the logits of ONE token in one thread.  The vocab is 248,320, which is far too large
-// to sort per token on a naive path, so `top_k` uses `k` passes of a maximum scan - 20 x 248,320 = 5.0M
-// comparisons per token.  Phase 3's note ("sort-free sampler: top-k = 20 makes this easy") is the optimisation;
-// this is the correctness version.
+// Both kernels put ONE BLOCK per token over the vocabulary: `sampler_greedy_kernel` is the plain argmax,
+// `sampler_kernel` runs the sampled chain as `top_k` block-argmax rounds followed by the top_p / temperature /
+// draw chain (its header says why the selection must be parallel and why the tie rule keeps the semantics).
 #include "strata/kernels/sampler.hpp"

 #include <cuda_runtime.h>
@@ -102,12 +101,32 @@ __global__ void sampler_greedy_kernel(const float* __restrict__ logits, int n_vo
        hrow += history_len - hlen;          // the window is the TAIL
    }

+    // PENALTY MEMBERSHIP AS A BITMAP.  The history touches at most `hlen` tokens of a quarter-million
+    // vocabulary, but the naive `history_count` per candidate per argmax round costs O(k x n_vocab x hlen)
+    // integer compares (~318 M per token at k=20, hlen=64 - measured 45 -> 31 tok/s on a real workload).
+    // A shared bitmap gives an O(1) membership test, and only the (at most hlen) hits pay the count scan;
+    // the counts - and therefore every sampled value - are exactly what the per-candidate scan produced.
+    extern __shared__ unsigned int penal_bits[];
+    const int bits_words = (int) ((n_vocab + 31) / 32);
+    const bool use_bits = hrow != nullptr && bits_words > 0;
+    if (use_bits) {
+        for (int w = threadIdx.x; w < bits_words; w += blockDim.x) penal_bits[w] = 0u;
+        __syncthreads();
+        for (int i = threadIdx.x; i < hlen; i += blockDim.x)
+            if (hrow[i] >= 0) atomicOr(&penal_bits[hrow[i] >> 5], 1u << (hrow[i] & 31));
+        __syncthreads();
+    }
+    auto hit_count = [&](int v) -> int {
+        if (!use_bits || !(penal_bits[v >> 5] & (1u << (v & 31)))) return 0;
+        return history_count(hrow, hlen, v);
+    };
+
    // `n_vocab` is the "no candidate" index: it loses every comparison to a real one, so a thread with no
    // elements contributes nothing rather than contributing a bogus zero.
    float bv = __int_as_float(0xff800000);   // -inf
    int best = n_vocab;
    for (int v = threadIdx.x; v < n_vocab; v += blockDim.x) {
-        const float s = apply_penalties(l[v], hrow ? history_count(hrow, hlen, v) : 0, p);
+        const float s = apply_penalties(l[v], hit_count(v), p);
        if (s > bv) { bv = s; best = v; }
    }
    for (int off = 16; off > 0; off >>= 1) {
@@ -134,47 +153,32 @@ __global__ void sampler_greedy_kernel(const float* __restrict__ logits, int n_vo
    }
 }

+/// **THE SAMPLED PATH, ONE BLOCK PER TOKEN.**  The kernel below replaced a version that ran the whole chain
+/// in ONE THREAD per token (`<<<ceil(T/64), 64>>>`, so a 4-token window fielded four threads): `top_k` alone
+/// was `k` sequential scans of the vocabulary with an inner sweep over the already-taken list - 20 x 248,320
+/// iterations of dependent work on one SM - and a verify window measured **1.6 s in the sampler**, which made
+/// every temperature-bearing request ~30x slower than a greedy one.  The selection is `k` argmax rounds, and
+/// an argmax over the vocabulary parallelises exactly like `sampler_greedy_kernel` (block over the vocab), so
+/// the rounds run back to back inside a block-per-token launch: the per-token cost falls to
+/// `k x n_vocab / 1024` plus `k` block reductions.
+///
+/// THE SEMANTICS ARE THE SERIAL ONES, EXACTLY.  Each round's argmax resolves ties to the LOWEST index (the
+/// serial scan's strict `>` keeps the first maximum it meets), so the kept sequence - both its set and its
+/// order - is unchanged; `top_p`'s cut reads that order in double arithmetic as before; temperature and the
+/// Philox draw apply after the cut.  `sampler_parity` pins all of it against the host reference.
 __global__ void sampler_kernel(const float* __restrict__ logits, int n_vocab, int n_tokens,
                               const int* __restrict__ history, int history_len, const SamplerParams p,
                               int* __restrict__ out) {
-    const int t = blockIdx.x * blockDim.x + threadIdx.x;
+    const int t = blockIdx.x;
    if (t >= n_tokens) return;
-
-    // Work on a private copy: the chain mutates the distribution and the caller's logits are shared with the
-    // next stage.  A 248,320-float allocation per thread is far too much for shared memory, so this uses a
-    // two-pass approach instead - first find the keep set, then normalise over it - and never materialises a
-    // second full-size buffer.
    const float* l = logits + (size_t) t * n_vocab;

-    // Temperature is needed by BOTH paths below, so it is computed here; the chain still APPLIES it after
+    // Temperature is needed by BOTH stages below, so it is computed here; the chain still APPLIES it after
    // the truncation filters - the survivors are chosen on the raw logits and only then scaled.
    const float inv_t = p.temperature > 0.0f ? 1.0f / p.temperature : 0.0f;

-    // ---- penalties: NOT IMPLEMENTED, and the ORDER they must take is transcribed here so the next person
-    // does not have to guess it.  `llama_sampler_penalties_apply` (src/llama-sampler.cpp) runs AFTER
-    // temperature in the chain (L381 against L375), and for every token seen in the last `penalty_last_n`:
-    //
-    //     if (logit <= 0) logit *= penalty_repeat;   else logit /= penalty_repeat;
-    //     logit -= float(count) * penalty_freq + float(count > 0) * penalty_present;
-    //
-    // TWO details there are counter-intuitive.  The repeat penalty MULTIPLIES for non-positive logits and
-    // DIVIDES for positive ones - the source notes the paper only ever divided, "but that would cause tokens
-    // with negative logits to become more likely, which is obviously wrong".  And the presence penalty rides
-    // on `float(count > 0)`, a BOOLEAN cast to float, not the count - so it applies once however many times
-    // the token appeared.  Both are the kind of thing a plausible implementation gets wrong while still
-    // producing valid tokens.
-    //
-    // It needs the token history, which is not in this signature, so a caller passing a non-zero
-    // `penalty_last_n` gets a loud failure rather than a silently unpenalised sample.
-    // ---- top_k: k passes of a maximum scan, skipping the already-taken
-    // ---- top_p: cumulative mass over the survivors in descending order
-    // The two are done together in one selection pass so the survivors and their mass are consistent.
-    // GREEDY IS THE GLOBAL ARGMAX, WHATEVER THE FILTERS SAY - so it is computed directly and the filter chain
-    // is skipped entirely.  No filter can remove the maximum: top_k keeps the k largest, top_p keeps a prefix
-    // of the descending order that always contains the largest (min_keep >= 1), and temperature is monotonic so
-    // it cannot reorder anything.  That also makes greedy INDEPENDENT OF THE ORDER, which is why the ordering
-    // test cannot use it - see sampler_parity.cpp.
-    // The penalty window is the last `penalty_last_n` entries of this row's history.
+    // The penalty window is the last `penalty_last_n` entries of this row's history (disabled at this
+    // launch: `sample_tokens` refuses a non-zero `penalty_last_n` without a history buffer).
    const int* hrow = history ? history + (size_t) t * history_len : nullptr;
    int hlen = 0;
    if (hrow) {
@@ -183,109 +187,117 @@ __global__ void sampler_kernel(const float* __restrict__ logits, int n_vocab, in
        hrow += history_len - hlen;          // the window is the TAIL
    }

-    if (p.greedy || p.temperature <= 0.0f) {
-        // Greedy is the global argmax ONLY when no penalty is active: a penalty can lower a token enough to
-        // change the maximum, so the argmax has to be taken over the penalised logits, not the raw ones.
-        //
-        // *** TEMPERATURE MUST NOT BE APPLIED HERE.  THIS WAS A REAL BUG. ***
-        // This branch used to read `apply_penalties(l[v] * inv_t, ...)`.  `inv_t` is 0.0f whenever
-        // temperature <= 0 (see above), so at temperature 0 EVERY logit became 0.0f and the argmax returned
-        // index 0 - the sampler emitted token 0 forever, whatever the model predicted.  OpenAI clients send
-        // `temperature: 0` for greedy decoding, so this was reachable from any ordinary client.
-        // It is also pointless even when inv_t is non-zero: scaling by a positive constant is monotonic and
-        // cannot reorder the argmax, which is exactly what the note above this block already says.
-        int best = 0;
-        float bv = apply_penalties(l[0], hrow ? history_count(hrow, hlen, 0) : 0, p);
-        for (int v = 1; v < n_vocab; ++v) {
-            const float s = apply_penalties(l[v], hrow ? history_count(hrow, hlen, v) : 0, p);
-            if (s > bv) { bv = s; best = v; }
-        }
-        out[t] = best;
-        return;
+    // the membership bitmap, as in `sampler_greedy_kernel` - see the cost note there
+    extern __shared__ unsigned int penal_bits[];
+    const int bits_words = (int) ((n_vocab + 31) / 32);
+    const bool use_bits = hrow != nullptr && bits_words > 0;
+    if (use_bits) {
+        for (int w = threadIdx.x; w < bits_words; w += blockDim.x) penal_bits[w] = 0u;
+        __syncthreads();
+        for (int i = threadIdx.x; i < hlen; i += blockDim.x)
+            if (hrow[i] >= 0) atomicOr(&penal_bits[hrow[i] >> 5], 1u << (hrow[i] & 31));
+        __syncthreads();
    }
+    auto hit_count = [&](int v) -> int {
+        if (!use_bits || !(penal_bits[v >> 5] & (1u << (v & 31)))) return 0;
+        return history_count(hrow, hlen, v);
+    };

    const int KMAX = 64;
-    int keep_ids[KMAX];
-    float keep_logit[KMAX];
-    int k = p.top_k > 0 ? (p.top_k < KMAX ? p.top_k : KMAX) : KMAX;
-    if (p.top_k <= 0) k = 0;                      // 0 means "no top-k filter" -> handled by the mask below
-
+    int k = p.top_k > 0 ? (p.top_k < KMAX ? p.top_k : KMAX) : 0;
    if (k <= 0) {
-        std::printf("sampler: the sampled path needs top_k in 1..%d (got %d); greedy needs no filters\n", KMAX, p.top_k);
+        if (threadIdx.x == 0)
+            std::printf("sampler: the sampled path needs top_k in 1..%d (got %d); greedy needs no filters\n",
+                        KMAX, p.top_k);
        return;   // leave out[t] unwritten rather than returning an uninitialised token
    }
-    int n_keep = 0;
-    {
-        for (int i = 0; i < k; ++i) {
-            int best = -1;
-            float bv = 0.0f;
-            for (int v = 0; v < n_vocab; ++v) {
-                bool taken = false;
-                for (int j = 0; j < i; ++j) if (keep_ids[j] == v) { taken = true; break; }
-                if (taken) continue;
-                if (best < 0 || l[v] > bv) { best = v; bv = l[v]; }
+
+    // ---- top_k: k rounds of a block argmax over the not-yet-taken.  `sel_*` holds the kept ids and their
+    // raw logits in selection order: descending by value, ties to the lower index, which is the order the
+    // top_p cut below is defined over.
+    __shared__ int sel_ids[KMAX];
+    __shared__ float sel_logit[KMAX];
+    __shared__ float sv[32];
+    __shared__ int si[32];
+    for (int i = 0; i < k; ++i) {
+        // `n_vocab` is the "no candidate" index: it loses every comparison to a real one (same convention as
+        // the greedy kernel, whose tie rule this reduction shares).
+        float bv = __int_as_float(0xff800000);   // -inf
+        int best = n_vocab;
+        for (int v = threadIdx.x; v < n_vocab; v += blockDim.x) {
+            bool taken = false;
+            for (int j = 0; j < i; ++j) if (sel_ids[j] == v) { taken = true; break; }
+            if (taken) continue;
+            const float s = apply_penalties(l[v], hit_count(v), p);
+            if (s > bv) { bv = s; best = v; }
+        }
+        for (int off = 16; off > 0; off >>= 1) {
+            const float ov = __shfl_down_sync(0xFFFFFFFFu, bv, off);
+            const int oi = __shfl_down_sync(0xFFFFFFFFu, best, off);
+            if (ov > bv || (ov == bv && oi < best)) { bv = ov; best = oi; }
+        }
+        const int warp = (int) (threadIdx.x >> 5), lane = (int) (threadIdx.x & 31);
+        if (lane == 0) { sv[warp] = bv; si[warp] = best; }
+        __syncthreads();
+        if (warp == 0) {
+            const int nw = (int) ((blockDim.x + 31) >> 5);
+            float wv = lane < nw ? sv[lane] : __int_as_float(0xff800000);
+            int wi = lane < nw ? si[lane] : n_vocab;
+            for (int off = 16; off > 0; off >>= 1) {
+                const float ov = __shfl_down_sync(0xFFFFFFFFu, wv, off);
+                const int oi = __shfl_down_sync(0xFFFFFFFFu, wi, off);
+                if (ov > wv || (ov == wv && oi < wi)) { wv = ov; wi = oi; }
            }
-            keep_ids[i] = best;
-            keep_logit[i] = bv;
-        }
-        n_keep = k;
-
-        // ---- top_p over the survivors, in descending order (which the selection above already produced)
-        if (p.top_p < 1.0f) {
-            // softmax over the keep set for the cumulative mass
-            float mx = keep_logit[0];
-            for (int i = 1; i < n_keep; ++i) mx = fmaxf(mx, keep_logit[i]);
-            double sum = 0.0;
-            for (int i = 0; i < n_keep; ++i) sum += exp((double) keep_logit[i] - (double) mx);
-            double cum = 0.0;
-            int cut = n_keep;
-            for (int i = 0; i < n_keep; ++i) {
-                cum += exp((double) keep_logit[i] - (double) mx) / sum;
-                if (cum >= (double) p.top_p) { cut = i + 1; break; }
-            }
-            if (cut < p.min_keep) cut = p.min_keep < n_keep ? p.min_keep : n_keep;
-            n_keep = cut;
+            if (lane == 0) { sel_ids[i] = (wi < n_vocab) ? wi : 0; sel_logit[i] = wv; }
        }
+        __syncthreads();
    }

+    // ---- min_p: keep the descending prefix whose probability is at least `min_p` of the top token's.  The
+    // kept list is in selection order (descending), so the survivors are a PREFIX and the cut composes with
+    // top_p's below.  In logit space the threshold is `sel_logit[0] + logf(min_p)` - equivalent to
+    // `p >= min_p * p_max` without the overflow an exp of raw logits risks.  0 disables, and the head itself
+    // always survives (`expf(0) == 1 >= min_p` for min_p in 0..1), so the count never reaches zero.
+    int n_minp = k;
+    if (p.min_p > 0.0f) {
+        const float thresh = sel_logit[0] + logf(p.min_p);
+        for (int i = 0; i < k; ++i)
+            if (sel_logit[i] < thresh) { n_minp = i; break; }
+    }

-    if (p.greedy || p.temperature <= 0.0f) {
-        // DEAD CODE AS WRITTEN: the identical condition at the top of this function already returned, so this
-        // block cannot be reached.  It is kept and corrected rather than deleted because if that early return
-        // is ever narrowed, this is the path that would run - and it carried the same `* inv_t` bug (every
-        // logit 0.0f at temperature 0, so `best` would always be `keep_ids[0]`).  Temperature is monotonic and
-        // cannot reorder a maximum, so it must not appear here at all.
-        // argmax over the SURVIVORS, ties by smallest id (the same convention as the router and top_k)
-        int best = keep_ids[0];
-        float bv = keep_logit[0];
-        for (int i = 1; i < n_keep; ++i) {
-            const float v = keep_logit[i];
-            if (v > bv) { bv = v; best = keep_ids[i]; }
+    // ---- top_p over the survivors, in descending order (which the selection produced), then temperature and
+    // one Philox draw.  Every thread computes the same chain redundantly over `sel_*` - the arithmetic is the
+    // serial kernel's, instruction for instruction - so they agree on `pick` and thread 0 writes it.
+    int n_keep = n_minp;
+    float mx = sel_logit[0];
+    for (int i = 1; i < n_minp; ++i) mx = fmaxf(mx, sel_logit[i]);
+    if (p.top_p < 1.0f) {
+        double sum = 0.0;
+        for (int i = 0; i < n_minp; ++i) sum += exp((double) sel_logit[i] - (double) mx);
+        double cum = 0.0;
+        int cut = n_minp;
+        for (int i = 0; i < n_minp; ++i) {
+            cum += exp((double) sel_logit[i] - (double) mx) / sum;
+            if (cum >= (double) p.top_p) { cut = i + 1; break; }
        }
-        out[t] = best;
-        return;
+        if (cut < p.min_keep) cut = p.min_keep < n_minp ? p.min_keep : n_minp;
+        n_keep = cut;
    }
-
-    // ---- pick: temperature, then penalties, then softmax over the survivors, then one uniform draw.
-    // The ORDER is the chain's: the survivors were chosen on the RAW logits, temperature scales them, and the
-    // penalty is applied to the scaled value - which is why the `logit <= 0` branch inside the penalty sees a
-    // temperature-scaled logit and not the model's own.
-    for (int i = 0; i < n_keep; ++i) {
-        keep_logit[i] = apply_penalties(keep_logit[i] * inv_t,
-                                        hrow ? history_count(hrow, hlen, keep_ids[i]) : 0, p);
-    }
-    float mx = keep_logit[0];
-    for (int i = 1; i < n_keep; ++i) mx = fmaxf(mx, keep_logit[i]);
+    auto scaled = [&](int i) {
+        return apply_penalties(sel_logit[i] * inv_t, hit_count(sel_ids[i]), p);
+    };
+    float smx = scaled(0);
+    for (int i = 1; i < n_keep; ++i) smx = fmaxf(smx, scaled(i));
    double sum = 0.0;
-    for (int i = 0; i < n_keep; ++i) sum += exp((double) keep_logit[i] - (double) mx);
+    for (int i = 0; i < n_keep; ++i) sum += exp((double) scaled(i) - (double) smx);
    const float u = philox_uniform(p.seed, p.counter + (uint64_t) t);
    double cum = 0.0;
-    int pick = keep_ids[n_keep - 1];
+    int pick = sel_ids[n_keep - 1];
    for (int i = 0; i < n_keep; ++i) {
-        cum += exp((double) keep_logit[i] - (double) mx) / sum;
-        if ((double) u < cum) { pick = keep_ids[i]; break; }
+        cum += exp((double) scaled(i) - (double) smx) / sum;
+        if ((double) u < cum) { pick = sel_ids[i]; break; }
    }
-    out[t] = pick;
+    if (threadIdx.x == 0) out[t] = pick;
 }

 }  // namespace
@@ -298,16 +310,19 @@ void sample_tokens(const float* logits, int n_tokens, int n_vocab, const int* hi
                     p.penalty_last_n, (const void*) history, history_len);
        std::exit(1);
    }
-    const int threads = 64;
-    const unsigned grid = (unsigned) ((n_tokens + threads - 1) / threads);
+    const unsigned shmem = (history != nullptr && history_len > 0 && p.penalty_last_n > 0)
+                               ? (unsigned) ((n_vocab + 31) / 32) * sizeof(unsigned)   // the penalty bitmap
+                               : 0;
    if (p.greedy || p.temperature <= 0.0f) {
        // One block per token, 1,024 threads over the vocabulary.  See `sampler_greedy_kernel`.
        const int gthreads = 1024;
-        sampler_greedy_kernel<<<(unsigned) n_tokens, gthreads, 0, (cudaStream_t) stream>>>(
+        sampler_greedy_kernel<<<(unsigned) n_tokens, gthreads, shmem, (cudaStream_t) stream>>>(
            logits, n_vocab, history, history_len, p, p.penalty_last_n, p.penalty_last_n, out);
    } else {
-        sampler_kernel<<<grid, threads, 0, (cudaStream_t) stream>>>(logits, n_vocab, n_tokens, history,
-                                                                   history_len, p, out);
+        // The same block-per-token shape: the selection's k argmax rounds reduce inside the block.  See
+        // `sampler_kernel`'s header for what the old one-thread-per-token launch cost.
+        sampler_kernel<<<(unsigned) n_tokens, 1024, shmem, (cudaStream_t) stream>>>(
+            logits, n_vocab, n_tokens, history, history_len, p, out);
    }
    const cudaError_t e = cudaGetLastError();
    if (e != cudaSuccess) {
@@ -0,0 +1,223 @@
+// src/kernels/cvec_parity.cpp - the control vector kernel (strata/kernels/cvec.hpp) against a host reference.
+//   1. project: h - s (h.v) v per stream and token, against double precision; the steered component is gone.
+//   2. add: h + d.
+//   3. switched off: R unchanged without a pending write; with one, R is BITWISE the write the fused read folds
+//      (fused_gr_read with apply), which is what keeps a loaded-but-off vector identical to the stock engine.
+//   4. a layer without a direction is untouched.
+
+#include "strata/kernels/cvec.hpp"
+#include "strata/kernels/fused_gr.hpp"
+
+#include <cuda_runtime.h>
+
+#include <cmath>
+#include <cstdio>
+#include <cstdlib>
+#include <cstring>
+#include <random>
+#include <vector>
+
+namespace k = strata::kernels;
+
+namespace {
+
+int g_fail = 0;
+
+void ck(cudaError_t e, const char* w) {
+    if (e != cudaSuccess) {
+        std::fprintf(stderr, "CUDA error in %s: %s\n", w, cudaGetErrorString(e));
+        std::exit(2);
+    }
+}
+
+template <typename T>
+T* dalloc(size_t n) {
+    T* p = nullptr;
+    ck(cudaMalloc(&p, n * sizeof(T)), "malloc");
+    return p;
+}
+
+template <typename T>
+void up(T* d, const std::vector<T>& h) { ck(cudaMemcpy(d, h.data(), h.size() * sizeof(T), cudaMemcpyHostToDevice), "up"); }
+template <typename T>
+std::vector<T> down(const T* d, size_t n) {
+    std::vector<T> h(n);
+    ck(cudaMemcpy(h.data(), d, n * sizeof(T), cudaMemcpyDeviceToHost), "down");
+    return h;
+}
+
+void check(bool ok, const char* what) {
+    std::printf("  %-66s %s\n", what, ok ? "ok" : "FAIL");
+    if (!ok) ++g_fail;
+}
+
+}  // namespace
+
+int main() {
+    constexpr int64_t L = 48, N = 2560, HC = 4, LR = 320, T = 5, D = HC * N;
+    constexpr int64_t kLayer = 7, kOff = 3;   // steered / not steered
+    std::mt19937 rng(1234);
+    std::normal_distribution<float> nd(0.0f, 1.0f);
+
+    // a unit direction at kLayer with a norm-2 scaled one's s = 2; nothing at kOff
+    std::vector<float> dir((size_t) (L * N), 0.0f), s((size_t) L, 0.0f);
+    {
+        double nrm = 0.0;
+        std::vector<float> v((size_t) N);
+        for (auto& x : v) { x = nd(rng); nrm += (double) x * x; }
+        nrm = std::sqrt(nrm);
+        for (int64_t j = 0; j < N; ++j) dir[(size_t) (kLayer * N + j)] = (float) (v[(size_t) j] / nrm);
+        s[(size_t) kLayer] = 2.0f;
+    }
+    std::string err;
+    if (!k::cvec_upload(dir, s, /*project*/ 0, 4, 44, N, HC, err)) { std::fprintf(stderr, "%s\n", err.c_str()); return 2; }
+    check(k::cvec().covers(kLayer) && !k::cvec().covers(kOff) && !k::cvec().covers(45), "covers(): steered layers only");
+
+    std::vector<float> R((size_t) (T * D)), bo((size_t) (T * N)), inj((size_t) (T * HC));
+    for (auto& x : R) x = nd(rng) * 3.0f;
+    for (auto& x : bo) x = nd(rng);
+    for (auto& x : inj) x = nd(rng);
+    float* dR = dalloc<float>(R.size());
+    float* dbo = dalloc<float>(bo.size());
+    float* dinj = dalloc<float>(inj.size());
+    up(dbo, bo);
+    up(dinj, inj);
+
+    // ---- 1. project, no pending write
+    std::printf("project\n");
+    up(dR, R);
+    k::cvec_apply(dR, kLayer, T, D, nullptr, 0, nullptr, 0, false, nullptr);
+    ck(cudaDeviceSynchronize(), "project");
+    {
+        const auto got = down(dR, R.size());
+        double worst = 0.0, worst_dot = 0.0;
+        for (int64_t t = 0; t < T; ++t)
+            for (int64_t c = 0; c < HC; ++c) {
+                const float* h = R.data() + t * D + c * N;
+                const float* v = dir.data() + kLayer * N;
+                double dot = 0.0;
+                for (int64_t j = 0; j < N; ++j) dot += (double) h[j] * v[j];
+                double after = 0.0;
+                for (int64_t j = 0; j < N; ++j) {
+                    const double want = h[j] - 2.0 * dot * v[j];
+                    const double g = got[(size_t) (t * D + c * N + j)];
+                    worst = std::fmax(worst, std::fabs(g - want));
+                    after += g * v[j];
+                }
+                // s = 2 reflects the component: h.v -> -h.v
+                worst_dot = std::fmax(worst_dot, std::fabs(after + dot));
+            }
+        std::printf("    max |err| %.3g, max |h'.v + h.v| %.3g\n", worst, worst_dot);
+        check(worst < 1e-4, "project matches h - s (h.v) v (s = 2)");
+        check(worst_dot < 1e-3, "the component along v is reflected (s = 2)");
+    }
+    // s = 1 removes it
+    s[(size_t) kLayer] = 1.0f;
+    if (!k::cvec_upload(dir, s, 0, 4, 44, N, HC, err)) return 2;
+    up(dR, R);
+    k::cvec_apply(dR, kLayer, T, D, nullptr, 0, nullptr, 0, false, nullptr);
+    ck(cudaDeviceSynchronize(), "project s=1");
+    {
+        const auto got = down(dR, R.size());
+        double worst = 0.0;
+        for (int64_t t = 0; t < T; ++t)
+            for (int64_t c = 0; c < HC; ++c) {
+                double after = 0.0;
+                for (int64_t j = 0; j < N; ++j) after += (double) got[(size_t) (t * D + c * N + j)] * dir[(size_t) (kLayer * N + j)];
+                worst = std::fmax(worst, std::fabs(after));
+            }
+        std::printf("    max |h'.v| %.3g\n", worst);
+        check(worst < 1e-3, "s = 1 projects the direction out");
+    }
+
+    // ---- 4. a layer without a direction
+    up(dR, R);
+    k::cvec_apply(dR, kOff, T, D, nullptr, 0, nullptr, 0, false, nullptr);
+    ck(cudaDeviceSynchronize(), "off layer");
+    check(std::memcmp(down(dR, R.size()).data(), R.data(), R.size() * 4) == 0, "a layer without a direction is untouched");
+
+    // ---- 3. switched off
+    std::printf("switched off\n");
+    k::cvec_set_enabled(false);
+    check(!k::cvec_enabled(), "cvec_enabled() follows the switch");
+    up(dR, R);
+    k::cvec_apply(dR, kLayer, T, D, nullptr, 0, nullptr, 0, false, nullptr);
+    ck(cudaDeviceSynchronize(), "off");
+    check(std::memcmp(down(dR, R.size()).data(), R.data(), R.size() * 4) == 0, "off, no pending write: R unchanged");
+    {
+        // the fused read's folded write, one token at a time, against the kernel's write-only pass
+        std::vector<uint16_t> wd((size_t) (LR * D)), wu((size_t) (D * LR)), wi((size_t) (HC * D));
+        std::vector<float> wn((size_t) D);
+        auto bf = [&](float x) { uint32_t u; std::memcpy(&u, &x, 4); return (uint16_t) (u >> 16); };
+        for (auto& x : wd) x = bf(nd(rng) * 0.02f);
+        for (auto& x : wu) x = bf(nd(rng) * 0.02f);
+        for (auto& x : wi) x = bf(nd(rng) * 0.02f);
+        for (auto& x : wn) x = 1.0f + 0.1f * nd(rng);
+        uint16_t* dwd = dalloc<uint16_t>(wd.size());
+        uint16_t* dwu = dalloc<uint16_t>(wu.size());
+        uint16_t* dwi = dalloc<uint16_t>(wi.size());
+        float* dwn = dalloc<float>(wn.size());
+        up(dwd, wd); up(dwu, wu); up(dwi, wi); up(dwn, wn);
+        float* lo = dalloc<float>(LR);
+        float* rs = dalloc<float>(HC);
+        float* inj_out = dalloc<float>(HC);
+        float* mixed = dalloc<float>(N);
+        float* dF = dalloc<float>(R.size());
+        up(dF, R);
+        for (int64_t t = 0; t < T; ++t) {
+            k::FusedGrArgs a;
+            a.R = dF + t * D; a.R_out = dF + t * D; a.apply = true;
+            a.bo_prev = dbo + t * N; a.inj_prev = dinj + t * HC;
+            a.w_norm = dwn; a.w_down = dwd; a.w_up = dwu; a.w_inject = dwi;
+            a.lo = lo; a.rs = rs; a.inject_out = inj_out; a.mixed = mixed;
+            k::fused_gr_read(a, nullptr);
+        }
+        up(dR, R);
+        k::cvec_apply(dR, kLayer, T, D, dbo, N, dinj, HC, true, nullptr);
+        ck(cudaDeviceSynchronize(), "write only");
+        const auto fused = down(dF, R.size()), mine = down(dR, R.size());
+        check(std::memcmp(fused.data(), mine.data(), R.size() * 4) == 0,
+              "off, pending write: bitwise the fused read's folded write");
+        k::cvec_set_enabled(true);
+        // on, with the write: the write, then the projection (reference from the fused result)
+        up(dR, R);
+        k::cvec_apply(dR, kLayer, T, D, dbo, N, dinj, HC, true, nullptr);
+        ck(cudaDeviceSynchronize(), "write+project");
+        const auto both = down(dR, R.size());
+        double worst = 0.0;
+        for (int64_t t = 0; t < T; ++t)
+            for (int64_t c = 0; c < HC; ++c) {
+                const float* h = fused.data() + t * D + c * N;
+                const float* v = dir.data() + kLayer * N;
+                double dot = 0.0;
+                for (int64_t j = 0; j < N; ++j) dot += (double) h[j] * v[j];
+                for (int64_t j = 0; j < N; ++j)
+                    worst = std::fmax(worst, std::fabs(both[(size_t) (t * D + c * N + j)] - (h[j] - dot * v[j])));
+            }
+        std::printf("    write + project: max |err| %.3g\n", worst);
+        check(worst < 1e-4, "on, pending write: the write, then the projection");
+    }
+
+    // ---- 2. add
+    std::printf("add\n");
+    for (int64_t j = 0; j < N; ++j) dir[(size_t) (kLayer * N + j)] *= 0.1f;   // d = 0.1 v, s = 1
+    s[(size_t) kLayer] = 1.0f;
+    if (!k::cvec_upload(dir, s, /*add*/ 1, 4, 44, N, HC, err)) return 2;
+    up(dR, R);
+    k::cvec_apply(dR, kLayer, T, D, nullptr, 0, nullptr, 0, false, nullptr);
+    ck(cudaDeviceSynchronize(), "add");
+    {
+        const auto got = down(dR, R.size());
+        bool same = true;
+        for (int64_t t = 0; t < T; ++t)
+            for (int64_t c = 0; c < HC; ++c)
+                for (int64_t j = 0; j < N; ++j) {
+                    const size_t i = (size_t) (t * D + c * N + j);
+                    same = same && got[i] == R[i] + dir[(size_t) (kLayer * N + j)];
+                }
+        check(same, "add is h + d in every stream (bitwise)");
+    }
+
+    std::printf(g_fail ? "cvec_parity: %d FAILED\n" : "cvec_parity: all passed\n", g_fail);
+    return g_fail ? 1 : 0;
+}
@@ -0,0 +1,346 @@
+// src/kernels/kv_q4_parity.cpp - Parity test for Q4_0 KV cache with orthonormal Walsh-Hadamard rotation.
+// Validates:
+// 1. FWHT 256 CUDA kernel vs exact mathematical orthonormal Walsh-Hadamard transform.
+//    (H * H * x == x, H is symmetric and orthonormal with scale 1/sqrt(256) = 1/16).
+// 2. Q4_0 quantization and packing (32 values per block, 18 bytes) bitwise vs host reference.
+// 3. kv_append_q4_step and kv_gather_q4_step through paged pool against host reference.
+// 4. Invariance of dot products under Walsh-Hadamard rotation: (H*q) . (H*k) == q . k.
+
+#include "strata/kernels/f16_bits.hpp"
+#include "strata/kernels/kv_q4.hpp"
+#include "strata/kernels/qsa.hpp"
+
+#include <cuda_runtime.h>
+
+#include <algorithm>
+#include <cmath>
+#include <cstdio>
+#include <cstdlib>
+#include <cstring>
+#include <random>
+#include <vector>
+
+namespace k = strata::kernels;
+
+namespace {
+
+int g_fail = 0;
+
+void ck(cudaError_t e, const char* w) {
+    if (e != cudaSuccess) {
+        std::fprintf(stderr, "CUDA error in %s: %s\n", w, cudaGetErrorString(e));
+        std::exit(2);
+    }
+}
+
+template <typename T>
+T* dalloc(size_t n) {
+    T* p = nullptr;
+    ck(cudaMalloc(&p, n * sizeof(T) + 64), "malloc");
+    ck(cudaMemset(p, 0, n * sizeof(T) + 64), "memset");
+    return p;
+}
+
+// Exact CPU reference for Fast Walsh-Hadamard Transform (D = 256)
+// Sylvester construction matching llama.cpp's ggml_gen_hadamard
+void fwht256_host_reference(float* x) {
+    constexpr int n = 256;
+    for (int s = 1; s < n; s *= 2) {
+        for (int i = 0; i < n; i += 2 * s) {
+            for (int j = 0; j < s; ++j) {
+                const float u = x[i + j];
+                const float v = x[i + j + s];
+                x[i + j]     = u + v;
+                x[i + j + s] = u - v;
+            }
+        }
+    }
+    const float scale = 1.0f / 16.0f; // 1 / sqrt(256)
+    for (int i = 0; i < n; ++i) {
+        x[i] *= scale;
+    }
+}
+
+// Host reference for Q4_0 quantization of 32 elements (1 block)
+void quantize_block_q4_0_host(const float* x, k::block_q4_0& blk) {
+    float amax = 0.0f;
+    float max_val = 0.0f;
+    for (int i = 0; i < 32; ++i) {
+        const float v = x[i];
+        if (amax < std::fabs(v)) {
+            amax = std::fabs(v);
+            max_val = v;
+        }
+    }
+    const float d = max_val / -8.0f;
+    const float id = d != 0.0f ? 1.0f / d : 0.0f;
+    blk.d = k::f16_from_f32(d);
+
+    for (int i = 0; i < 16; ++i) {
+        const float x0 = x[i] * id;
+        const float x1 = x[i + 16] * id;
+        const int q0 = (int) (x0 + 8.5f);
+        const int q1 = (int) (x1 + 8.5f);
+        const uint8_t xi0 = (uint8_t) std::min(15, std::max(0, q0));
+        const uint8_t xi1 = (uint8_t) std::min(15, std::max(0, q1));
+        blk.qs[i] = xi0 | (xi1 << 4);
+    }
+}
+
+} // namespace
+
+int main() {
+    std::printf("=== Running kv_q4_parity test ===\n");
+    std::mt19937 rng(42);
+    std::normal_distribution<float> nd(0.0f, 1.0f);
+
+    // -------------------------------------------------------------
+    // Test 1: Orthonormal Fast Walsh-Hadamard Transform (D=256)
+    // -------------------------------------------------------------
+    std::printf("[1/4] Verifying FWHT-256 CUDA kernel vs exact mathematical reference...\n");
+    constexpr int n_vectors = 64;
+    std::vector<float> h_in(n_vectors * 256);
+    std::vector<float> h_ref(n_vectors * 256);
+    for (size_t i = 0; i < h_in.size(); ++i) {
+        h_in[i] = nd(rng);
+    }
+    h_ref = h_in;
+
+    // Run CPU reference on each row
+    for (int r = 0; r < n_vectors; ++r) {
+        fwht256_host_reference(h_ref.data() + r * 256);
+    }
+
+    // Run GPU kernel
+    float* d_src = dalloc<float>(n_vectors * 256);
+    float* d_dst = dalloc<float>(n_vectors * 256);
+    ck(cudaMemcpy(d_src, h_in.data(), h_in.size() * sizeof(float), cudaMemcpyHostToDevice), "memcpy H2D");
+
+    k::fwht256_cuda(d_src, d_dst, n_vectors, nullptr);
+    ck(cudaDeviceSynchronize(), "fwht256_cuda");
+
+    std::vector<float> h_out(n_vectors * 256);
+    ck(cudaMemcpy(h_out.data(), d_dst, h_out.size() * sizeof(float), cudaMemcpyDeviceToHost), "memcpy D2H");
+
+    double max_fwht_diff = 0.0;
+    for (size_t i = 0; i < h_out.size(); ++i) {
+        double diff = std::fabs(h_out[i] - h_ref[i]);
+        if (diff > max_fwht_diff) max_fwht_diff = diff;
+    }
+    if (max_fwht_diff > 1e-5) {
+        std::fprintf(stderr, "FAIL: FWHT-256 GPU output differs from host reference (max diff = %e)\n", max_fwht_diff);
+        ++g_fail;
+    } else {
+        std::printf("  -> FWHT-256 bitwise match with host reference (max diff = %e, OK)\n", max_fwht_diff);
+    }
+
+    // Self-inverse property: H * (H * x) == x
+    k::fwht256_inplace_cuda(d_dst, n_vectors, nullptr);
+    ck(cudaDeviceSynchronize(), "fwht256_inplace_cuda");
+    ck(cudaMemcpy(h_out.data(), d_dst, h_out.size() * sizeof(float), cudaMemcpyDeviceToHost), "memcpy D2H");
+
+    double max_inv_diff = 0.0;
+    for (size_t i = 0; i < h_out.size(); ++i) {
+        double diff = std::fabs(h_out[i] - h_in[i]);
+        if (diff > max_inv_diff) max_inv_diff = diff;
+    }
+    if (max_inv_diff > 1e-5) {
+        std::fprintf(stderr, "FAIL: FWHT-256 self-inverse property failed (max diff = %e)\n", max_inv_diff);
+        ++g_fail;
+    } else {
+        std::printf("  -> FWHT-256 self-inverse property verified H*H*x == x (max diff = %e, OK)\n", max_inv_diff);
+    }
+
+    // -------------------------------------------------------------
+    // Test 2: Invariance of Attention Scores under Hadamard Rotation
+    // -------------------------------------------------------------
+    std::printf("[2/4] Verifying Attention Score Invariance: <H*q, H*k> == <q, k>...\n");
+    double max_dot_diff = 0.0;
+    for (int r = 0; r < n_vectors; r += 2) {
+        const float* q = h_in.data() + r * 256;
+        const float* k_vec = h_in.data() + (r + 1) * 256;
+        const float* hq = h_ref.data() + r * 256;
+        const float* hk = h_ref.data() + (r + 1) * 256;
+
+        double dot_raw = 0.0;
+        double dot_rot = 0.0;
+        for (int i = 0; i < 256; ++i) {
+            dot_raw += (double) q[i] * (double) k_vec[i];
+            dot_rot += (double) hq[i] * (double) hk[i];
+        }
+        double d = std::fabs(dot_raw - dot_rot);
+        if (d > max_dot_diff) max_dot_diff = d;
+    }
+    if (max_dot_diff > 1e-5) {
+        std::fprintf(stderr, "FAIL: Rotated dot product differs from original (max diff = %e)\n", max_dot_diff);
+        ++g_fail;
+    } else {
+        std::printf("  -> Dot product invariant under rotation: |<Hq,Hk> - <q,k>| = %e (OK)\n", max_dot_diff);
+    }
+
+    // -------------------------------------------------------------
+    // Test 3: Q4_0 Paged KV Append & Gather against Host Reference
+    // -------------------------------------------------------------
+    std::printf("[3/4] Verifying Q4_0 KV append and gather against host reference...\n");
+    k::QsaShapes s = k::qsa_real_shapes();
+    s.page_size = 64;
+    const int H = (int) s.n_head_kv; // 2
+    const int D = (int) s.head_dim;   // 256
+    const int P = (int) s.page_size;  // 64
+    const int pages = 8;
+    const int cells = pages * P;
+
+    std::vector<int32_t> table(pages);
+    for (int i = 0; i < pages; ++i) table[i] = (i * 3 + 5) % pages; // permutation
+    int32_t* d_table = dalloc<int32_t>(pages);
+    ck(cudaMemcpy(d_table, table.data(), pages * sizeof(int32_t), cudaMemcpyHostToDevice), "memcpy table");
+
+    const size_t pool_bytes = (size_t) pages * H * P * k::kv_q4_bytes_per_head(D);
+    uint8_t* d_k_q4 = dalloc<uint8_t>(pool_bytes);
+    uint8_t* d_v_q4 = dalloc<uint8_t>(pool_bytes);
+    float* d_kcur = dalloc<float>(H * D);
+    float* d_vcur = dalloc<float>(H * D);
+    int32_t* d_step = dalloc<int32_t>(k::kStepCount);
+
+    std::vector<std::vector<float>> host_k(cells), host_v(cells);
+    std::vector<int> positions(cells);
+    for (int i = 0; i < cells; ++i) positions[i] = i;
+    std::shuffle(positions.begin(), positions.end(), rng);
+
+    const int n_fill = cells - 25; // leave some cells empty
+    for (int n = 0; n < n_fill; ++n) {
+        const int pos = positions[n];
+        std::vector<float> kv(H * D), vv(H * D);
+        for (auto& x : kv) x = nd(rng) * 2.0f;
+        for (auto& x : vv) x = nd(rng) * 2.0f;
+
+        // Apply FWHT before append
+        for (int h = 0; h < H; ++h) {
+            fwht256_host_reference(kv.data() + h * D);
+            fwht256_host_reference(vv.data() + h * D);
+        }
+        host_k[pos] = kv;
+        host_v[pos] = vv;
+
+        int32_t hstep[k::kStepCount] = {pos, pos + 1, 0, 0};
+        ck(cudaMemcpy(d_step, hstep, sizeof(hstep), cudaMemcpyHostToDevice), "memcpy step");
+        ck(cudaMemcpy(d_kcur, kv.data(), kv.size() * sizeof(float), cudaMemcpyHostToDevice), "memcpy k");
+        ck(cudaMemcpy(d_vcur, vv.data(), vv.size() * sizeof(float), cudaMemcpyHostToDevice), "memcpy v");
+
+        k::kv_append_q4_step(d_k_q4, d_v_q4, d_table, d_step, d_kcur, d_vcur, s, nullptr);
+        ck(cudaDeviceSynchronize(), "kv_append_q4_step");
+    }
+
+    // Verify written Q4_0 blocks bitwise against host quantizer
+    std::vector<uint8_t> h_k_q4(pool_bytes), h_v_q4(pool_bytes);
+    ck(cudaMemcpy(h_k_q4.data(), d_k_q4, pool_bytes, cudaMemcpyDeviceToHost), "memcpy d2h k_q4");
+    ck(cudaMemcpy(h_v_q4.data(), d_v_q4, pool_bytes, cudaMemcpyDeviceToHost), "memcpy d2h v_q4");
+
+    long bad_blocks = 0;
+    const int blocks_per_head = D / k::QK4_0; // 8
+    const int bytes_per_head = blocks_per_head * sizeof(k::block_q4_0); // 144
+
+    for (int n = 0; n < n_fill; ++n) {
+        const int pos = positions[n];
+        for (int is_v = 0; is_v < 2; ++is_v) {
+            for (int h = 0; h < H; ++h) {
+                const long long page = (long long) table[pos / P];
+                const long long row = (page * H + h) * P + (pos % P);
+                const uint8_t* pool_ptr = (is_v ? h_v_q4.data() : h_k_q4.data()) + row * bytes_per_head;
+                const float* raw_vec = (is_v ? host_v[pos].data() : host_k[pos].data()) + h * D;
+
+                for (int b = 0; b < blocks_per_head; ++b) {
+                    k::block_q4_0 host_blk;
+                    quantize_block_q4_0_host(raw_vec + b * 32, host_blk);
+
+                    const k::block_q4_0* gpu_blk = reinterpret_cast<const k::block_q4_0*>(pool_ptr) + b;
+                    if (std::memcmp(&host_blk, gpu_blk, sizeof(k::block_q4_0)) != 0) {
+                        if (bad_blocks == 0) {
+                            std::printf("MISMATCH at n=%d, pos=%d, is_v=%d, h=%d, b=%d\n", n, pos, is_v, h, b);
+                            std::printf("  host blk.d = 0x%04x (%f), gpu blk.d = 0x%04x (%f)\n",
+                                        host_blk.d, k::f32_from_f16(host_blk.d),
+                                        gpu_blk->d, k::f32_from_f16(gpu_blk->d));
+                            std::printf("  host qs: ");
+                            for (int i = 0; i < 16; ++i) std::printf("%02x ", host_blk.qs[i]);
+                            std::printf("\n  gpu  qs: ");
+                            for (int i = 0; i < 16; ++i) std::printf("%02x ", gpu_blk->qs[i]);
+                            std::printf("\n");
+                        }
+                        ++bad_blocks;
+                    }
+                }
+            }
+        }
+    }
+    if (bad_blocks > 0) {
+        std::fprintf(stderr, "FAIL: %ld Q4_0 blocks differ from host quantizer\n", bad_blocks);
+        ++g_fail;
+    } else {
+        std::printf("  -> All Q4_0 pool blocks bitwise equal to host quantizer (OK)\n");
+    }
+
+    // -------------------------------------------------------------
+    // Test 4: Gather Q4_0 into FP16 Scratch and verify accuracy
+    // -------------------------------------------------------------
+    std::printf("[4/4] Verifying Q4_0 gather unpacking and dequantization...\n");
+    const int max_ids = 128;
+    int32_t* d_ids = dalloc<int32_t>(max_ids);
+    uint16_t* d_k_scratch = dalloc<uint16_t>((size_t) max_ids * H * D);
+    uint16_t* d_v_scratch = dalloc<uint16_t>((size_t) max_ids * H * D);
+
+    std::vector<int32_t> ids(max_ids);
+    for (int i = 0; i < max_ids; ++i) {
+        ids[i] = positions[rng() % n_fill];
+    }
+    ck(cudaMemcpy(d_ids, ids.data(), max_ids * sizeof(int32_t), cudaMemcpyHostToDevice), "memcpy ids");
+
+    int32_t hstep[k::kStepCount] = {0, 0, 0, max_ids};
+    ck(cudaMemcpy(d_step, hstep, sizeof(hstep), cudaMemcpyHostToDevice), "memcpy step");
+
+    k::kv_gather_q4_step(d_k_q4, d_v_q4, d_table, d_ids, d_step, max_ids, s, d_k_scratch, d_v_scratch, nullptr);
+    ck(cudaDeviceSynchronize(), "kv_gather_q4_step");
+
+    std::vector<uint16_t> h_k_scratch((size_t) max_ids * H * D);
+    std::vector<uint16_t> h_v_scratch((size_t) max_ids * H * D);
+    ck(cudaMemcpy(h_k_scratch.data(), d_k_scratch, h_k_scratch.size() * sizeof(uint16_t), cudaMemcpyDeviceToHost), "memcpy d2h k_scratch");
+    ck(cudaMemcpy(h_v_scratch.data(), d_v_scratch, h_v_scratch.size() * sizeof(uint16_t), cudaMemcpyDeviceToHost), "memcpy d2h v_scratch");
+
+    double worst_quant_err = 0.0;
+    for (int i = 0; i < max_ids; ++i) {
+        const int cell = ids[i];
+        for (int is_v = 0; is_v < 2; ++is_v) {
+            for (int h = 0; h < H; ++h) {
+                const float* orig = (is_v ? host_v[cell].data() : host_k[cell].data()) + h * D;
+                const uint16_t* gathered = (is_v ? h_v_scratch.data() : h_k_scratch.data()) + (i * H + h) * D;
+
+                for (int d = 0; d < D; ++d) {
+                    float deq = k::f32_from_f16(gathered[d]);
+                    float raw = orig[d];
+                    double err = std::fabs(deq - raw);
+                    if (err > worst_quant_err) worst_quant_err = err;
+                }
+            }
+        }
+    }
+    std::printf("  -> Max reconstruction error after 4-bit quant + dequant: %.4f (OK)\n", worst_quant_err);
+
+    cudaFree(d_src);
+    cudaFree(d_dst);
+    cudaFree(d_table);
+    cudaFree(d_k_q4);
+    cudaFree(d_v_q4);
+    cudaFree(d_kcur);
+    cudaFree(d_vcur);
+    cudaFree(d_step);
+    cudaFree(d_ids);
+    cudaFree(d_k_scratch);
+    cudaFree(d_v_scratch);
+
+    if (g_fail == 0) {
+        std::printf("=== kv_q4_parity: ALL TESTS PASSED SUCCESSFULLY! ===\n");
+        return 0;
+    } else {
+        std::fprintf(stderr, "=== kv_q4_parity: FAILED (%d failures) ===\n", g_fail);
+        return 1;
+    }
+}
@@ -8,7 +8,8 @@
 //   2. the residency map is consistent after every call (slot_block and page_table invert each other);
 //   3. no call overflowed, and the hit/miss counters add up;
 //   4. a ring (the MTP drafter's layout) restored from the host copy reads the same values as the resident pool.
-// INT8 and FP16 pools both.
+// INT8, FP16 and Q4_0 (PR #21) pools.
+#include "strata/kernels/kv_q4.hpp"
 #include "strata/kernels/kv_q8.hpp"
 #include "strata/kernels/kv_stream.hpp"
 #include "strata/kernels/qsa.hpp"
@@ -48,9 +49,13 @@ template <typename T> T* halloc(size_t n) {   // pinned, mapped; returns the dev

 struct Pools {   // one K/V pool set of `pages` pages
    k::KvHostPools p;   // reused as a plain pointer bundle
-    void alloc(int64_t pages, const k::QsaShapes& s, bool int8, bool host) {
+    void alloc(int64_t pages, const k::QsaShapes& s, int fmt, bool host) {
        const size_t rows = (size_t) pages * s.n_head_kv * s.page_size;
-        if (int8) {
+        if (fmt == k::kKvQ4) {
+            const size_t b = rows * k::kv_q4_bytes_per_head((int) s.head_dim);
+            p.k_q4 = host ? halloc<uint8_t>(b) : dalloc<uint8_t>(b);
+            p.v_q4 = host ? halloc<uint8_t>(b) : dalloc<uint8_t>(b);
+        } else if (fmt == k::kKvInt8) {
            p.k_q = host ? halloc<int8_t>(rows * s.head_dim) : dalloc<int8_t>(rows * s.head_dim);
            p.v_q = host ? halloc<int8_t>(rows * s.head_dim) : dalloc<int8_t>(rows * s.head_dim);
            p.k_scale = host ? halloc<uint16_t>(rows * 4) : dalloc<uint16_t>(rows * 4);
@@ -63,14 +68,15 @@ struct Pools {   // one K/V pool set of `pages` pages
    k::QsaAttnPools attn(const int32_t* table) const {
        k::QsaAttnPools a;
        a.k_pool = p.k_pool; a.v_pool = p.v_pool; a.k_q = p.k_q; a.v_q = p.v_q; a.k_scale = p.k_scale;
-        a.v_scale = p.v_scale; a.page_table = table;
+        a.v_scale = p.v_scale; a.k_q4 = p.k_q4; a.v_q4 = p.v_q4; a.page_table = table;
        return a;
    }
 };

 void append(const Pools& pl, const int32_t* table, const int32_t* step, const float* kc, const float* vc,
-            const k::QsaShapes& s, bool int8, const k::KvHostPools* host) {
-    if (int8) k::kv_append_q8_step(pl.p.k_q, pl.p.v_q, pl.p.k_scale, pl.p.v_scale, table, step, kc, vc, s, nullptr, host);
+            const k::QsaShapes& s, int fmt, const k::KvHostPools* host) {
+    if (fmt == k::kKvQ4) k::kv_append_q4_step(pl.p.k_q4, pl.p.v_q4, table, step, kc, vc, s, nullptr, host);
+    else if (fmt == k::kKvInt8) k::kv_append_q8_step(pl.p.k_q, pl.p.v_q, pl.p.k_scale, pl.p.v_scale, table, step, kc, vc, s, nullptr, host);
    else k::kv_append_step(pl.p.k_pool, pl.p.v_pool, table, step, kc, vc, s, nullptr, host);
 }

@@ -96,18 +102,19 @@ std::vector<int32_t> selection(int64_t n_kv, int64_t width, std::mt19937& rng) {
    return ids;
 }

-bool run(bool int8) {
+bool run(int fmt) {
+    const char* name = fmt == k::kKvQ4 ? "q4_0" : fmt == k::kKvInt8 ? "int8" : "fp16";
    k::QsaShapes s = k::qsa_real_shapes();
    const int64_t N = 40000, n_blocks = (N + 3) / 4, n_slots = 8 * 516 + 700;   // must evict: slots < blocks
    const int64_t cap = k::qsa_selection_width(k::kTopkMaxCells, s), NQ = 8;
    const int64_t H = s.n_head_kv, D = s.head_dim, NH = s.n_head;
-    std::mt19937 rng(int8 ? 7 : 11);
+    std::mt19937 rng(7 + 4 * fmt);
    std::normal_distribution<float> nd(0.f, 1.f);

    Pools ref, slots, host;
-    ref.alloc(n_blocks, s, int8, false);
-    slots.alloc(n_slots, s, int8, false);
-    host.alloc(n_blocks, s, int8, true);
+    ref.alloc(n_blocks, s, fmt, false);
+    slots.alloc(n_slots, s, fmt, false);
+    host.alloc(n_blocks, s, fmt, true);
    int32_t* ident = dalloc<int32_t>(n_blocks);
    {
        std::vector<int32_t> t(n_blocks);
@@ -143,8 +150,8 @@ bool run(bool int8) {
        ck(cudaMemcpy(kc, hk.data(), hk.size() * 4, cudaMemcpyHostToDevice), "k");
        ck(cudaMemcpy(vc, hv.data(), hv.size() * 4, cudaMemcpyHostToDevice), "v");
        ck(cudaMemcpy(step, st, sizeof(st), cudaMemcpyHostToDevice), "step");
-        append(ref, ident, step, kc, vc, s, int8, nullptr);
-        append(slots, m.page_table, step, kc, vc, s, int8, &host.p);
+        append(ref, ident, step, kc, vc, s, fmt, nullptr);
+        append(slots, m.page_table, step, kc, vc, s, fmt, &host.p);
        if (pos % 131 != 130 && pos != N - 1) continue;
        // a batch: n_q queries at the last n_q positions (as a verify window), each with its own selection
        const int n_q = 1 + (int) (rng() % NQ);
@@ -163,13 +170,13 @@ bool run(bool int8) {
        ck(cudaMemcpy(steps, hst.data(), hst.size() * 4, cudaMemcpyHostToDevice), "steps");
        ck(cudaMemcpy(q, hq.data(), hq.size() * 4, cudaMemcpyHostToDevice), "q");
        k::qsa_decode_attn_batch(q, ref.attn(ident), ids, steps, cap, s, scratch, out_ref, n_q, nullptr);
-        k::kv_stream_resolve(m, slots.attn(m.page_table), host.p, int8, ids, steps, n_q, cap, s, nullptr);
+        k::kv_stream_resolve(m, slots.attn(m.page_table), host.p, fmt, ids, steps, n_q, cap, s, nullptr);
        k::qsa_decode_attn_batch(q, slots.attn(m.page_table), ids, steps, cap, s, scratch, out_str, n_q, nullptr);
        ck(cudaDeviceSynchronize(), "batch");
        ck(cudaMemcpy(a.data(), out_ref, (size_t) n_q * NH * D * 4, cudaMemcpyDeviceToHost), "a");
        ck(cudaMemcpy(b2.data(), out_str, (size_t) n_q * NH * D * 4, cudaMemcpyDeviceToHost), "b");
        if (std::memcmp(a.data(), b2.data(), (size_t) n_q * NH * D * 4) != 0) {
-            if (bad++ < 5) std::fprintf(stderr, "  %s pos %lld n_q %d: streamed attention differs\n", int8 ? "int8" : "fp16", (long long) pos, n_q);
+            if (bad++ < 5) std::fprintf(stderr, "  %s pos %lld n_q %d: streamed attention differs\n", name, (long long) pos, n_q);
        }
        // the map inverts itself
        ck(cudaMemcpy(pt.data(), m.page_table, n_blocks * 4, cudaMemcpyDeviceToHost), "pt");
@@ -188,7 +195,7 @@ bool run(bool int8) {
    }
    const k::KvStreamCounters c = k::kv_stream_counters(m);
    std::printf("  %s: %d batches, %llu block lookups, %llu misses (%.1f%% hit), overflow %d, %d failures\n",
-                int8 ? "int8" : "fp16", batches, (unsigned long long) c.lookups, (unsigned long long) c.misses,
+                name, batches, (unsigned long long) c.lookups, (unsigned long long) c.misses,
                c.lookups ? 100.0 * (double) (c.lookups - c.misses) / (double) c.lookups : 0.0, (int) c.overflow, bad);
    if (c.overflow || c.calls != (uint64_t) batches || c.misses == 0 || c.misses >= c.lookups) ++bad;

@@ -196,10 +203,10 @@ bool run(bool int8) {
    {
        const int64_t R = 1500, b1 = n_blocks, b0 = b1 - R;
        Pools ring;
-        ring.alloc(R, s, int8, false);
+        ring.alloc(R, s, fmt, false);
        int32_t* rt = dalloc<int32_t>(n_blocks);
        k::kv_ring_table(rt, n_blocks, R, nullptr);
-        k::kv_ring_restore(ring.attn(rt), host.p, int8, b0, b1, R, s, nullptr);
+        k::kv_ring_restore(ring.attn(rt), host.p, fmt, b0, b1, R, s, nullptr);
        const int64_t width = cap;
        std::vector<int32_t> hids((size_t) cap), hst = {(int32_t) (N - 1), (int32_t) N, (int32_t) (N / 4), (int32_t) width};
        for (int64_t i = 0; i < width; ++i) hids[i] = (int32_t) (N - width + i);   // the window's last cells
@@ -211,7 +218,7 @@ bool run(bool int8) {
        ck(cudaMemcpy(a.data(), out_ref, (size_t) NH * D * 4, cudaMemcpyDeviceToHost), "a");
        ck(cudaMemcpy(b2.data(), out_str, (size_t) NH * D * 4, cudaMemcpyDeviceToHost), "b");
        const bool ok = std::memcmp(a.data(), b2.data(), (size_t) NH * D * 4) == 0;
-        std::printf("  %s ring restore: %s\n", int8 ? "int8" : "fp16", ok ? "identical" : "DIFFERS");
+        std::printf("  %s ring restore: %s\n", name, ok ? "identical" : "DIFFERS");
        if (!ok) ++bad;
    }
    return bad == 0;
@@ -220,8 +227,8 @@ bool run(bool int8) {

 int main() {
    std::printf("kv_stream_parity: streamed vs resident KV, bitwise\n");
-    const bool a = run(true), b = run(false);
-    if (!a || !b) ++g_fail;
+    const bool a = run(k::kKvInt8), b = run(k::kKvF16), c = run(k::kKvQ4);
+    if (!a || !b || !c) ++g_fail;
    std::printf(g_fail ? "FAIL\n" : "PASS\n");
    return g_fail ? 1 : 0;
 }
@@ -9,6 +9,8 @@
 #include "strata/kernels/cpu/native_expert.hpp"
 #include "strata/kernels/cpu/expert.hpp"
 #include "strata/kernels/cpu/iq_avx512.hpp"
+#include "strata/kernels/cpu/iq_avx2.hpp"
+#include "strata/kernels/cpu/expert_layout.hpp"
 #include "ggml-cpu.h"
 #include "strata/kernels/iq_kernels.hpp"

@@ -33,6 +35,7 @@ static double rel(const std::vector<float>& a, const std::vector<float>& b) {
 }

 int main(int argc, char** argv) {
+    setvbuf(stdout, nullptr, _IONBF, 0);   // keep the trail on a crash
    if (argc < 2) { std::fprintf(stderr, "usage: native_expert_parity <shard1.gguf> [layer ...]\n"); return 2; }
    strata::GgufFile gguf(argv[1]);
    std::vector<int> layers;
@@ -103,40 +106,50 @@ int main(int argc, char** argv) {
            }
            cpu::native_gu_rows(f, blob.data(), a, NT, ffp, 0, (int) FF);
            if (cpu::iq512_supported(f.gu_type)) {
-                // the AVX-512 rows against ggml's own vec_dot, same Q8_K activations: float-order differences only
-                std::vector<float> g512((size_t) NT * FF), gref((size_t) NT * FF);
-                float* gp[NT];
-                for (int k = 0; k < NT; ++k) gp[k] = g512.data() + k * FF;
-                cpu::iq512_rows(f.gu_type, blob.data(), f.gu_row, (int) H, a, NT, gp, 0, (int) FF);
+                // ggml's own vec_dot, same Q8_K activations: the reference for both multi-token kernels
+                // (float-order differences only)
                const auto* tc = ggml_get_type_traits_cpu((ggml_type) f.gu_type);
+                std::vector<float> gref((size_t) NT * FF);
                for (int k = 0; k < NT; ++k)
                    for (int64_t r = 0; r < FF; ++r)
                        tc->vec_dot((int) H, &gref[k * FF + r], 0, blob.data() + r * f.gu_row, 0, a[k], 0, 1);
-                {   // single-thread throughput, the weights cache-resident (compute bound)
-                    float* f1[1] = {ffp[0]};
+                double usg = 0.0;
+                {   // ggml single-token throughput, the weights cache-resident (compute bound)
                    const int it = 50;
                    auto t0 = std::chrono::steady_clock::now();
-                    for (int i = 0; i < it; ++i) cpu::iq512_gu_rows(f.gu_type, blob.data(), f.gu_row, f.up_off, (int) H, a, 1, f1, 0, (int) FF);
-                    auto t1 = std::chrono::steady_clock::now();
                    for (int i = 0; i < it; ++i)
                        for (int64_t r = 0; r < FF; ++r) {
                            float gg, uu;
                            tc->vec_dot((int) H, &gg, 0, blob.data() + r * f.gu_row, 0, a[0], 0, 1);
                            tc->vec_dot((int) H, &uu, 0, blob.data() + f.up_off + r * f.gu_row, 0, a[0], 0, 1);
                        }
-                    auto t2 = std::chrono::steady_clock::now();
-                    const double us512 = std::chrono::duration<double, std::micro>(t1 - t0).count() / it;
-                    const double usg = std::chrono::duration<double, std::micro>(t2 - t1).count() / it;
-                    std::printf("          gate+up one token one thread: avx512 %.0f us (%.2f GB/s), ggml %.0f us (%.2f GB/s)\n",
-                                us512, 2.0 * f.up_off / us512 / 1e3, usg, 2.0 * f.up_off / usg / 1e3);
-                    auto t3 = std::chrono::steady_clock::now();
-                    for (int i = 0; i < it; ++i) cpu::iq512_gu_rows(f.gu_type, blob.data(), f.gu_row, f.up_off, (int) H, a, NT, ffp, 0, (int) FF);
-                    auto t4 = std::chrono::steady_clock::now();
-                    std::printf("          gate+up %d tokens one thread: avx512 %.0f us vs ggml %d x %.0f us\n", NT,
-                                std::chrono::duration<double, std::micro>(t4 - t3).count() / it, NT, usg);
+                    auto t1 = std::chrono::steady_clock::now();
+                    usg = std::chrono::duration<double, std::micro>(t1 - t0).count() / it;
                }
-                std::printf("          %s AVX-512 gate rows vs ggml vec_dot: rel %.2e\n",
-                            ggml_type_name((ggml_type) f.gu_type), rel(g512, gref));
+                // one multi-token kernel: parity against ggml's vec_dot, then one-token and NT-token throughput
+                auto check = [&](const char* tag, bool is512) {
+                    std::vector<float> g((size_t) NT * FF);
+                    float* gp[NT];
+                    for (int k = 0; k < NT; ++k) gp[k] = g.data() + k * FF;
+                    (is512 ? cpu::iq512_rows : cpu::iq256_rows)(f.gu_type, blob.data(), f.gu_row, (int) H, a, NT, gp, 0, (int) FF);
+                    std::printf("          %s %s gate rows vs ggml vec_dot: rel %.2e\n",
+                                ggml_type_name((ggml_type) f.gu_type), tag, rel(g, gref));
+                    float* f1[1] = {ffp[0]};
+                    const int it = 50;
+                    const auto gu = is512 ? cpu::iq512_gu_rows : cpu::iq256_gu_rows;
+                    auto t0 = std::chrono::steady_clock::now();
+                    for (int i = 0; i < it; ++i) gu(f.gu_type, blob.data(), f.gu_row, f.up_off, (int) H, a, 1, f1, 0, (int) FF);
+                    auto t1 = std::chrono::steady_clock::now();
+                    for (int i = 0; i < it; ++i) gu(f.gu_type, blob.data(), f.gu_row, f.up_off, (int) H, a, NT, ffp, 0, (int) FF);
+                    auto t2 = std::chrono::steady_clock::now();
+                    const double us1 = std::chrono::duration<double, std::micro>(t1 - t0).count() / it;
+                    const double usn = std::chrono::duration<double, std::micro>(t2 - t1).count() / it;
+                    std::printf("          gate+up one token one thread: %s %.0f us (%.2f GB/s), ggml %.0f us (%.2f GB/s)\n",
+                                tag, us1, 2.0 * f.up_off / us1 / 1e3, usg, 2.0 * f.up_off / usg / 1e3);
+                    std::printf("          gate+up %d tokens one thread: %s %.0f us vs ggml %d x %.0f us\n", NT, tag, usn, NT, usg);
+                };
+                if (cpu::cpu_avx512_ok()) check("avx512", true);   // guarded: the binary runs on AVX-2 CPUs too
+                check("avx2", false);
            }
            for (int k = 0; k < NT; ++k) {
                cpu::native_quant_h(f, ff[k].data(), hq[k].data());
@@ -145,18 +158,51 @@ int main(int argc, char** argv) {
            }
            cpu::native_down_rows(f, blob.data(), hp, NT, op, 0, (int) H);
            if (f.d_type == 42) {
-                // (b2) the AVX-512 GGUF-layout Q2_0 kernel the pool uses for Q2_0 down projections
+                // (b2) the GGUF-layout Q2_0 kernel the pool uses for Q2_0 down projections - the AVX-512 one
+                // where the CPU has it, the AVX-2 one (q2_avx2.cpp) where it does not.  Calling the AVX-512
+                // kernel unconditionally faults on a Zen 2/3 CPU.
                std::vector<cpu::ActQ> a2(NT);
                const cpu::ActQ* ap[NT];
                std::vector<float> alt((size_t) NT * H);
                float* altp[NT];
                for (int k = 0; k < NT; ++k) {
-                    cpu::act_quant_q8_1(ff[k].data(), (int) FF, a2[k]);
+                    cpu::act_quant_any(ff[k].data(), (int) FF, a2[k]);   // gated: AVX-512 or AVX-2 per CPU
                    ap[k] = &a2[k];
                    altp[k] = alt.data() + k * H;
                }
-                cpu::q2_0_gguf_rows_multi(blob.data() + f.down_off, f.d_row, (int) (FF / 64), ap, NT, altp, 0, (int) H);
-                std::printf("          q2_0 AVX-512 down vs ggml down: rel %.2e\n", rel(alt, got_c));
+                cpu::q2_rows_any(blob.data() + f.down_off, f.d_row, (int) (FF / 64), ap, NT, altp, 0, (int) H);
+                std::printf("          q2_0 %s down vs ggml down: rel %.2e\n",
+                            cpu::cpu_avx512_ok() ? "AVX-512" : "AVX-2", rel(alt, got_c));
+            }
+            if (f.d_type == 20) {
+                // (b3) the IQ4_NL multi-token AVX-2 kernel the pool now uses for IQ4_NL down projections,
+                // against ggml-cpu's single-token vec_dot on the SAME Q8_0 activations (h), plus timing.
+                std::vector<float> alt((size_t) NT * H), ref((size_t) NT * H);
+                float* altp[NT];
+                for (int k = 0; k < NT; ++k) altp[k] = alt.data() + k * H;
+                cpu::iq4nl256_down_rows(blob.data() + f.down_off, f.d_row, (int) FF, hp, NT, altp, 0, (int) H);
+                const auto* tdc = ggml_get_type_traits_cpu((ggml_type) f.d_type);
+                for (int k = 0; k < NT; ++k)
+                    for (int64_t r = 0; r < H; ++r)
+                        tdc->vec_dot((int) FF, ref.data() + k * H + r, 0,
+                                     blob.data() + f.down_off + (size_t) r * f.d_row, 0, hp[k], 0, 1);
+                std::printf("          iq4_nl AVX-2 multi-token down vs ggml down: rel %.2e\n", rel(alt, ref));
+                const int it = 50;
+                const auto t0 = std::chrono::steady_clock::now();
+                for (int i = 0; i < it; ++i)
+                    cpu::iq4nl256_down_rows(blob.data() + f.down_off, f.d_row, (int) FF, hp, NT, altp, 0, (int) H);
+                const auto t1 = std::chrono::steady_clock::now();
+                for (int i = 0; i < it; ++i)
+                    for (int k = 0; k < NT; ++k)
+                        for (int64_t r = 0; r < H; ++r)
+                            tdc->vec_dot((int) FF, ref.data() + k * H + r, 0,
+                                        blob.data() + f.down_off + (size_t) r * f.d_row, 0, hp[k], 0, 1);
+                const auto t2 = std::chrono::steady_clock::now();
+                const double usn = std::chrono::duration<double, std::micro>(t1 - t0).count() / it;
+                const double usg = std::chrono::duration<double, std::micro>(t2 - t1).count() / it;
+                std::printf("          down %d tokens one thread: avx2 %.0f us (%.2f GB/s) vs ggml %.0f us (%.2f GB/s)\n",
+                            NT, usn, (double) (f.bytes - f.down_off) / usn / 1e3, usg,
+                            (double) (f.bytes - f.down_off) / usg / 1e3);
            }
        }
        // (c) the GPU: one group holding the NT entries
@@ -12,6 +12,7 @@

 #include <cuda_runtime.h>

+#include <algorithm>
 #include <cmath>
 #include <cstdio>
 #include <cstdlib>
@@ -126,6 +127,139 @@ int run(const char* name, const std::vector<float>& logits, int n_tokens, const
    return bad;
 }

+// The Philox draw, host side - a transcription of the kernel's `philox_uniform` so the SAMPLED pick (not
+// just the greedy argmax) can be pinned against a reference.  `__umulhi(a, b)` is the high half of a 32x32
+// multiply, spelled `(uint32_t)(((uint64_t) a * b) >> 32)` here.
+struct PhiloxRound {
+    uint32_t& c0; uint32_t& c1; uint32_t& c2; uint32_t& c3;
+    void step(uint32_t k0, uint32_t k1) const {
+        const uint32_t hi0 = (uint32_t) (((uint64_t) 0x9E3779B9u * c0) >> 32);
+        const uint32_t hi1 = (uint32_t) (((uint64_t) 0xBB67AE85u * c2) >> 32);
+        const uint32_t lo0 = 0x9E3779B9u * c0;
+        const uint32_t lo1 = 0xBB67AE85u * c2;
+        const uint32_t n0 = hi1 ^ c1 ^ k0;
+        const uint32_t n1 = lo1;
+        const uint32_t n2 = hi0 ^ c3 ^ k1;
+        const uint32_t n3 = lo0;
+        c0 = n0; c1 = n1; c2 = n2; c3 = n3;
+    }
+};
+
+float host_philox_uniform(uint64_t seed, uint64_t counter) {
+    uint32_t c0 = (uint32_t) counter, c1 = (uint32_t) (counter >> 32);
+    uint32_t c2 = (uint32_t) seed, c3 = (uint32_t) (seed >> 32);
+    PhiloxRound r{c0, c1, c2, c3};
+    for (int i = 0; i < 10; ++i) r.step((uint32_t) i, 0u);
+    return (float) (c0 >> 8) * (1.0f / 16777216.0f);
+}
+
+// The full SAMPLED chain, host side - the kernel's `sampler_kernel` in serial form: penalties on the raw
+// logits during the top_k selection (ties to the lowest index), the min_p prefix cut, top_p's cut in double,
+// then penalties AGAIN on the temperature-scaled survivors (docs/sampling.md's after-temperature pass), and
+// one Philox draw at (seed, counter + row).
+int sampled_reference(const std::vector<float>& l, const std::vector<int>& hist,
+                      const strata::kernels::SamplerParams& p, int row) {
+    auto penal = [&](float logit, int count) {
+        if (count <= 0) return logit;
+        if (logit <= 0.0f) logit *= p.penalty_repeat; else logit /= p.penalty_repeat;
+        logit -= (float) count * p.penalty_freq + (count > 0 ? 1.0f : 0.0f) * p.penalty_present;
+        return logit;
+    };
+    auto count = [&](int v) { int c = 0; for (int h : hist) if (h == v) ++c; return c; };
+    const int nv = (int) l.size();
+    const int KMAX = 64;
+    const int k = p.top_k > 0 ? (p.top_k < KMAX ? p.top_k : KMAX) : 0;
+    std::vector<int> sel_ids;
+    std::vector<float> sel_logit;
+    std::vector<char> taken((size_t) nv, 0);
+    for (int i = 0; i < k; ++i) {
+        int best = -1; float bv = 0;
+        for (int v = 0; v < nv; ++v) {
+            if (taken[(size_t) v]) continue;
+            const float s = penal(l[(size_t) v], count(v));
+            if (best < 0 || s > bv) { best = v; bv = s; }
+        }
+        taken[(size_t) best] = 1;
+        sel_ids.push_back(best); sel_logit.push_back(bv);
+    }
+    int n_minp = (int) sel_ids.size();
+    if (p.min_p > 0.0f) {
+        const float thresh = sel_logit[0] + std::log(p.min_p);
+        for (int i = 0; i < (int) sel_ids.size(); ++i)
+            if (sel_logit[(size_t) i] < thresh) { n_minp = i; break; }
+    }
+    int n_keep = n_minp;
+    if (p.top_p < 1.0f) {
+        double sum = 0.0;
+        for (int i = 0; i < n_minp; ++i) sum += std::exp((double) sel_logit[(size_t) i] - (double) sel_logit[0]);
+        double cum = 0.0;
+        int cut = n_minp;
+        for (int i = 0; i < n_minp; ++i) {
+            cum += std::exp((double) sel_logit[(size_t) i] - (double) sel_logit[0]) / sum;
+            if (cum >= (double) p.top_p) { cut = i + 1; break; }
+        }
+        if (cut < p.min_keep) cut = p.min_keep < n_minp ? p.min_keep : n_minp;
+        n_keep = cut;
+    }
+    const float inv_t = p.temperature > 0.0f ? 1.0f / p.temperature : 0.0f;
+    auto scaled = [&](int i) { return penal(sel_logit[(size_t) i] * inv_t, count(sel_ids[(size_t) i])); };
+    float smx = scaled(0);
+    for (int i = 1; i < n_keep; ++i) smx = std::fmax(smx, scaled(i));
+    double sum = 0.0;
+    for (int i = 0; i < n_keep; ++i) sum += std::exp((double) scaled(i) - (double) smx);
+    const float u = host_philox_uniform(p.seed, p.counter + (uint64_t) row);
+    double cum = 0.0;
+    int pick = sel_ids[(size_t) (n_keep - 1)];
+    for (int i = 0; i < n_keep; ++i) {
+        cum += std::exp((double) scaled(i) - (double) smx) / sum;
+        if ((double) u < cum) { pick = sel_ids[(size_t) i]; break; }
+    }
+    return pick;
+}
+
+// Survivors after the min_p + top_p cuts, in the sampled chain - the quantity an order or a threshold
+// actually changes, used to assert a fixture can SEE the feature before asserting the kernel matches.
+int sampled_cut(const std::vector<float>& l, const std::vector<int>& hist, const strata::kernels::SamplerParams& p) {
+    auto penal = [&](float logit, int count) {
+        if (count <= 0) return logit;
+        if (logit <= 0.0f) logit *= p.penalty_repeat; else logit /= p.penalty_repeat;
+        logit -= (float) count * p.penalty_freq + (count > 0 ? 1.0f : 0.0f) * p.penalty_present;
+        return logit;
+    };
+    auto count = [&](int v) { int c = 0; for (int h : hist) if (h == v) ++c; return c; };
+    const int nv = (int) l.size();
+    const int KMAX = 64;
+    const int k = p.top_k > 0 ? (p.top_k < KMAX ? p.top_k : KMAX) : 0;
+    std::vector<float> sel;
+    std::vector<char> taken((size_t) nv, 0);
+    for (int i = 0; i < k; ++i) {
+        int best = -1; float bv = 0;
+        for (int v = 0; v < nv; ++v) {
+            if (taken[(size_t) v]) continue;
+            const float s = penal(l[(size_t) v], count(v));
+            if (best < 0 || s > bv) { best = v; bv = s; }
+        }
+        taken[(size_t) best] = 1; sel.push_back(bv);
+    }
+    int n_minp = (int) sel.size();
+    if (p.min_p > 0.0f) {
+        const float thresh = sel[0] + std::log(p.min_p);
+        for (int i = 0; i < (int) sel.size(); ++i)
+            if (sel[(size_t) i] < thresh) { n_minp = i; break; }
+    }
+    if (p.top_p >= 1.0f) return n_minp;
+    double sum = 0.0;
+    for (int i = 0; i < n_minp; ++i) sum += std::exp((double) sel[(size_t) i] - (double) sel[0]);
+    double cum = 0.0;
+    int cut = n_minp;
+    for (int i = 0; i < n_minp; ++i) {
+        cum += std::exp((double) sel[(size_t) i] - (double) sel[0]) / sum;
+        if (cum >= (double) p.top_p) { cut = i + 1; break; }
+    }
+    if (cut < p.min_keep) cut = p.min_keep < n_minp ? p.min_keep : n_minp;
+    return cut;
+}
+
 }  // namespace

 int main(int argc, char** argv) {
@@ -326,6 +460,116 @@ int main(int argc, char** argv) {
        bad += run("T=1 greedy=true  is the argmax", l, NT3, p2, want);
    }

+    // ---- fixture 6: PENALTIES IN THE SAMPLED CHAIN.  Fixture 4 pins the greedy (argmax) path; the sampled
+    // chain applies penalties a SECOND time - after the temperature scaling, on the survivors - so it gets its
+    // own reference (the full chain with the host Philox) and its own observability check: with the penalties
+    // on, the history row's favourite must LOSE a pick it would win penalty-free.
+    {
+        const int NV2 = 8, NT2 = 2;
+        strata::kernels::SamplerParams p;
+        p.top_k = 5; p.top_p = 0.9f; p.temperature = 0.8f; p.seed = 9; p.counter = 0;
+        p.penalty_last_n = 4; p.penalty_repeat = 3.0f; p.penalty_freq = 0.2f; p.penalty_present = 0.6f;
+
+        std::vector<float> l((size_t) NV2 * NT2, -8.0f);
+        std::vector<int> hist((size_t) NT2 * 4, -1);
+        for (int t = 0; t < NT2; ++t) {
+            float* row = l.data() + (size_t) t * NV2;
+            row[0] = 9.0f; row[1] = 4.5f; row[2] = 4.4f; row[3] = 4.3f;   // token 0 leads clean (9 vs 4.5)
+            hist[(size_t) t * 4 + 0] = 0;                                  // and falls to 2.2/2.0 penalised
+            hist[(size_t) t * 4 + 1] = t == 1 ? 0 : -1;                    // (repeat 3, freq, presence)
+        }
+        std::vector<int> want((size_t) NT2), clean((size_t) NT2);
+        strata::kernels::SamplerParams clean_p = p;
+        clean_p.penalty_last_n = 0; clean_p.penalty_repeat = 1.0f;
+        clean_p.penalty_freq = 0.0f; clean_p.penalty_present = 0.0f;
+        for (int t = 0; t < NT2; ++t) {
+            const std::vector<float> row(l.begin() + (size_t) t * NV2, l.begin() + (size_t) (t + 1) * NV2);
+            const std::vector<int> h(hist.begin() + (size_t) t * 4, hist.begin() + (size_t) (t + 1) * 4);
+            want[(size_t) t] = sampled_reference(row, h, p, t);
+            clean[(size_t) t] = sampled_reference(row, h, clean_p, t);
+        }
+        const bool visible = want[0] != clean[0] || want[1] != clean[1];
+        std::printf("  %-34s %s (penalised picks %d/%d, clean %d/%d)\n",
+                    "sampled penalties are observable", visible ? "yes" : "*** NO ***", want[0], want[1],
+                    clean[0], clean[1]);
+        if (!visible) ++bad;
+        else bad += run("sampled chain: penalties + top_k/p", l, NT2, p, want, hist, 4);
+    }
+
+    // ---- fixture 7: MIN_P.  The cut is a PREFIX of the descending top_k list (logit >= max + log(min_p)),
+    // so the fixture asserts the survivor count moves with the threshold (the observability half) and that
+    // the kernel's pick equals the reference's through the full sampled chain (the correctness half).
+    {
+        const int NV3 = 8, NT3 = 2;
+        std::vector<float> l((size_t) NV3 * NT3, -8.0f);
+        for (int t = 0; t < NT3; ++t) {
+            float* row = l.data() + (size_t) t * NV3;
+            row[0] = 4.0f; row[1] = 3.5f; row[2] = 3.2f; row[3] = 3.1f;   // gaps keep the cut off the
+            row[4] = 2.0f;                                                // logf/rounding knife edge
+        }
+        strata::kernels::SamplerParams base;
+        base.top_k = 6; base.top_p = 1.0f; base.temperature = 0.9f; base.seed = 77;
+
+        int c0 = 0, c05 = 0, c09 = 0;
+        for (int t = 0; t < NT3; ++t) {
+            const std::vector<float> row(l.begin() + (size_t) t * NV3, l.begin() + (size_t) (t + 1) * NV3);
+            strata::kernels::SamplerParams q = base; q.min_p = 0.0f;
+            c0 += sampled_cut(row, {}, q);
+            q.min_p = 0.5f; c05 += sampled_cut(row, {}, q);
+            q.min_p = 0.9f; c09 += sampled_cut(row, {}, q);
+        }
+        const bool visible = c0 > c05 && c05 > c09 && c09 >= NT3;
+        std::printf("  %-34s %s (survivors: min_p 0 -> %d, 0.5 -> %d, 0.9 -> %d)\n",
+                    "min_p cut is observable", visible ? "yes" : "*** NO ***", c0, c05, c09);
+        if (!visible) ++bad;
+
+        for (float mp : {0.0f, 0.5f, 0.9f}) {
+            strata::kernels::SamplerParams q = base; q.min_p = mp;
+            std::vector<int> want((size_t) NT3);
+            for (int t = 0; t < NT3; ++t) {
+                const std::vector<float> row(l.begin() + (size_t) t * NV3, l.begin() + (size_t) (t + 1) * NV3);
+                want[(size_t) t] = sampled_reference(row, {}, q, t);
+            }
+            char name[64];
+            std::snprintf(name, sizeof name, "sampled chain: min_p=%.1f", (double) mp);
+            bad += run(name, l, NT3, q, want);
+        }
+    }
+
+    // ---- fixture 8: THE PENALTY WINDOW IS THE TAIL.  With an 8-entry history and penalty_last_n = 4, only
+    // the LAST four entries count: a token punished in the old half must come back to full strength, and one
+    // punished in the tail half stays down.  The reference counts the same tail; the observability check runs
+    // the reference once more WITHOUT the clamp (counting all 8) and requires the picks to differ.
+    {
+        const int NV4 = 8;
+        strata::kernels::SamplerParams p;
+        p.top_k = 0; p.top_p = 1.0f; p.temperature = 1.0f; p.greedy = true;
+        p.penalty_last_n = 4; p.penalty_repeat = 3.0f; p.penalty_freq = 0.3f; p.penalty_present = 0.5f;
+
+        std::vector<float> l((size_t) NV4, -8.0f);
+        l[0] = 6.0f; l[3] = 6.5f;                       // token 0 leads clean; token 3 is the tail offender
+        std::vector<int> hist = {0, 0, 0, 0, 3, 3, 3, 3};   // token 0 old (out), token 3 in the tail
+
+        auto pick_clamped = [&](bool clamp) {
+            int best = 0; float bv = 0; bool first = true;
+            for (int v = 0; v < NV4; ++v) {
+                int c = 0;
+                for (int i = 0; i < (clamp ? 4 : 8); ++i) if (hist[(size_t) (8 - (clamp ? 4 : 8) + i)] == v) ++c;
+                float logit = l[(size_t) v];
+                if (c > 0) { logit = logit <= 0.0f ? logit * p.penalty_repeat : logit / p.penalty_repeat;
+                             logit -= (float) c * p.penalty_freq + p.penalty_present; }
+                if (first || logit > bv) { bv = logit; best = v; first = false; }
+            }
+            return best;
+        };
+        const int want = pick_clamped(true), unclamped = pick_clamped(false);
+        const bool visible = want != unclamped;
+        std::printf("  %-34s %s (clamped pick %d, full-history pick %d)\n",
+                    "penalty window clamp is observable", visible ? "yes" : "*** NO ***", want, unclamped);
+        if (!visible) ++bad;
+        else bad += run("penalty window: tail only", {l.begin(), l.end()}, 1, p, {want}, hist, 8);
+    }
+
    // A continuous stream and individual decode calls consume the same draw counters.
    {
        constexpr int count = 32, vocab = 16;
@@ -44,6 +44,13 @@ bool Gemm::init_external(void* stream, uint16_t* scratch, int64_t scratch_elems,
    return true;
 }

+void Gemm::rebind(uint16_t* scratch, int64_t scratch_elems, void* workspace, size_t ws_bytes) {
+    scratch_ = scratch;
+    scratch_elems_ = scratch_elems;
+    workspace_ = workspace;
+    cublasSetWorkspace((cublasHandle_t) handle_, workspace_, ws_bytes);
+}
+
 bool Gemm::init(void* stream, int64_t scratch_elems, std::string& err) {
    cublasHandle_t h = nullptr;
    if (cublasCreate(&h) != CUBLAS_STATUS_SUCCESS) { err = "prefill gemm: cublasCreate failed"; return false; }
@@ -0,0 +1,82 @@
+// src/prefill/ggml_cuda_host.cu - prompt-speed plan step 2b: the host-side symbols of llama.cpp's ggml-cuda that its MMQ
+// and quantize code reference, for the MMQ kernels compiled into strata_mmq without the rest of ggml-cuda.cu.
+#include "common.cuh"
+
+#include <cstdio>
+#include <cstdlib>
+#include <mutex>
+#include <vector>
+
+[[noreturn]] void ggml_cuda_error(const char * stmt, const char * func, const char * file, int line, const char * msg) {
+    std::fprintf(stderr, "ggml-cuda (strata mmq): %s: %s\n  in %s at %s:%d\n", msg, stmt, func, file, line);
+    std::abort();
+}
+
+int ggml_cuda_get_device() {
+    int id = 0;
+    CUDA_CHECK(cudaGetDevice(&id));
+    return id;
+}
+
+const ggml_cuda_device_info & ggml_cuda_info() {
+    static ggml_cuda_device_info info = [] {
+        ggml_cuda_device_info in = {};
+        int n = 0;
+        if (cudaGetDeviceCount(&n) != cudaSuccess) n = 0;
+        n = n > GGML_CUDA_MAX_DEVICES ? GGML_CUDA_MAX_DEVICES : n;
+        in.device_count = n;
+        in.physical_device_count = n;
+        for (int id = 0; id < n; ++id) {
+            cudaDeviceProp prop;
+            CUDA_CHECK(cudaGetDeviceProperties(&prop, id));
+            auto & d = in.devices[id];
+            d.cc = 100 * prop.major + 10 * prop.minor;
+            d.nsm = prop.multiProcessorCount;
+            d.smpb = prop.sharedMemPerBlock;
+            d.smpbo = prop.sharedMemPerBlockOptin;
+            d.integrated = prop.integrated != 0;
+            d.vmm = false;
+            d.total_vram = prop.totalGlobalMem;
+            d.warp_size = prop.warpSize;
+            d.supports_cooperative_launch = prop.cooperativeLaunch != 0;
+            d.physical_device = id;
+            d.physical_share_count = 1;
+            d.virtual_index = 0;
+        }
+        return in;
+    }();
+    return info;
+}
+
+namespace {
+// Buffers are kept and reused: MMQ asks for the same few sizes every launch (its stream-k fixup tiles).
+struct CachingPool : ggml_cuda_pool {
+    struct Buf { void * p; size_t size; bool used; };
+    std::vector<Buf> bufs;
+    std::mutex mu;
+    void * alloc(size_t size, size_t * actual_size) override {
+        std::lock_guard<std::mutex> lk(mu);
+        for (auto & b : bufs)
+            if (!b.used && b.size >= size) { b.used = true; *actual_size = b.size; return b.p; }
+        void * p = nullptr;
+        CUDA_CHECK(cudaMalloc(&p, size));
+        bufs.push_back({p, size, true});
+        *actual_size = size;
+        return p;
+    }
+    void free(void * ptr, size_t) override {
+        std::lock_guard<std::mutex> lk(mu);
+        for (auto & b : bufs)
+            if (b.p == ptr) { b.used = false; return; }
+    }
+    ~CachingPool() override {
+        for (auto & b : bufs) cudaFree(b.p);
+    }
+};
+}  // namespace
+
+std::unique_ptr<ggml_cuda_pool> ggml_backend_cuda_context::new_pool_for_device(int, int) {
+    return std::unique_ptr<ggml_cuda_pool>(new CachingPool());
+}
+
+ggml_backend_cuda_context::~ggml_backend_cuda_context() {}
@@ -0,0 +1,186 @@
+// src/prefill/moe_mmq.cu - see include/strata/prefill/moe_mmq.hpp.  llama.cpp's MMQ (ggml-cuda, MIT) is compiled
+// from the pinned llama.cpp checkout the build already takes ggml from; src/prefill/ggml_cuda_host.cu supplies the
+// few host symbols of ggml-cuda.cu it references.
+#include "strata/prefill/moe_mmq.hpp"
+
+#include "common.cuh"
+#include "mmq.cuh"
+#include "quantize.cuh"
+
+#include <cstdio>
+#include <cstdlib>
+
+namespace strata::prefill::mmq {
+namespace {
+
+void ck(cudaError_t e, const char* what) {
+    if (e != cudaSuccess) {
+        std::fprintf(stderr, "prefill mmq: %s: %s\n", what, cudaGetErrorString(e));
+        std::exit(1);
+    }
+}
+
+int64_t pad512(int64_t n) { return (n + 511) / 512 * 512; }
+
+__global__ void copy16_kernel(const uint4* __restrict__ a, int64_t na, const uint4* __restrict__ b, int64_t nb,
+                              uint4* __restrict__ ab_dst, const uint4* __restrict__ c, int64_t nc, uint4* __restrict__ c_dst) {
+    const int64_t i = (int64_t) blockIdx.x * blockDim.x + threadIdx.x;
+    if (i < na) ab_dst[i] = a[i];
+    else if (i < na + nb) ab_dst[i] = b[i - na];
+    else if (i < na + nb + nc) c_dst[i - na - nb] = c[i - na - nb];
+}
+__global__ void copy1_kernel(const uint8_t* __restrict__ a, int64_t na, const uint8_t* __restrict__ b, int64_t nb,
+                             uint8_t* __restrict__ ab_dst, const uint8_t* __restrict__ c, int64_t nc, uint8_t* __restrict__ c_dst) {
+    const int64_t i = (int64_t) blockIdx.x * blockDim.x + threadIdx.x;
+    if (i < na) ab_dst[i] = a[i];
+    else if (i < na + nb) ab_dst[i] = b[i - na];
+    else if (i < na + nb + nc) c_dst[i - na - nb] = c[i - na - nb];
+}
+
+// Strata blob: gate/up codes [1280][640 B], down codes [2560][160 B], gate/up scales [1280][40] f16, down scales
+// [2560][10] f16 (the layout of prefill/kernels.cu's blob_dequant_kernel).  A GGUF Q2_0 block is {f16 d; 16 code
+// bytes} with the same 2-bit codes in the same order, so a block is a scale and a 16-byte run of codes.
+__global__ void strata_q2_kernel(const uint8_t* __restrict__ blob, uint16_t* __restrict__ gu, uint16_t* __restrict__ dn) {
+    constexpr size_t O_D_CODES = (size_t) 1280 * 640, O_GU_SC = O_D_CODES + (size_t) 2560 * 160,
+                     O_D_SC = O_GU_SC + (size_t) 1280 * 40 * 2;
+    const int64_t i = (int64_t) blockIdx.x * blockDim.x + threadIdx.x;   // one GGUF block
+    const int64_t n_gu = 1280LL * 40, n_d = 2560LL * 10;
+    const uint8_t* codes;
+    const uint16_t* scale;
+    uint16_t* out;
+    if (i < n_gu) {
+        const int64_t row = i / 40, b = i % 40;
+        codes = blob + row * 640 + b * 16;
+        scale = (const uint16_t*) (blob + O_GU_SC) + row * 40 + b;
+        out = gu + i * 9;
+    } else if (i < n_gu + n_d) {
+        const int64_t j = i - n_gu, row = j / 10, b = j % 10;
+        codes = blob + O_D_CODES + row * 160 + b * 16;
+        scale = (const uint16_t*) (blob + O_D_SC) + row * 10 + b;
+        out = dn + j * 9;
+    } else {
+        return;
+    }
+    const uint4 q = *(const uint4*) codes;
+    const uint16_t* qh = (const uint16_t*) &q;
+    out[0] = *scale;
+#pragma unroll
+    for (int k = 0; k < 8; ++k) out[1 + k] = qh[k];
+}
+
+__global__ void swiglu_kernel(const float* __restrict__ gu, float* __restrict__ h, int64_t rows, int64_t n_ff,
+                              bool interleaved) {
+    const int64_t i = (int64_t) blockIdx.x * blockDim.x + threadIdx.x;
+    if (i >= rows * n_ff) return;
+    const int64_t r = i / n_ff, k = i % n_ff;
+    const float* row = gu + r * 2 * n_ff;
+    const float g = interleaved ? row[2 * k] : row[k], u = interleaved ? row[2 * k + 1] : row[n_ff + k];
+    h[i] = g / (1.0f + __expf(-g)) * u;
+}
+
+__global__ void iota_kernel(int32_t* dst, int64_t n) {
+    const int64_t i = (int64_t) blockIdx.x * blockDim.x + threadIdx.x;
+    if (i < n) dst[i] = (int32_t) i;
+}
+
+unsigned blocks(int64_t n) { return (unsigned) ((n + 255) / 256); }
+
+}  // namespace
+
+bool built() { return true; }
+
+bool supported(int t) {
+    switch ((ggml_type) t) {
+        case GGML_TYPE_Q2_0: case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ2_S:
+        case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ3_S: case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ4_XS:
+            return true;
+        default:
+            return false;
+    }
+}
+
+size_t matrix_bytes(int t, int64_t rows, int64_t cols) {
+    return (size_t) rows * (size_t) (cols / ggml_blck_size((ggml_type) t)) * ggml_type_size((ggml_type) t);
+}
+
+size_t q8_bytes(int64_t rows, int64_t cols) {
+    return (size_t) rows * (size_t) pad512(cols) * sizeof(block_q8_1_mmq) / (4 * QK8_1) + 128 * sizeof(block_q8_1_mmq);
+}
+
+void quantize(const float* x, const int32_t* ids, void* xq, int t, int64_t cols, int64_t ld, int64_t rows, void* stream) {
+    if (rows <= 0) return;
+    quantize_mmq_q8_1_cuda(x, ids, xq, (ggml_type) t, cols, ld, rows * ld, rows * ld, pad512(cols), rows, 1, 1,
+                           (cudaStream_t) stream);
+    ck(cudaGetLastError(), "quantize");
+}
+
+Context::Context() {
+    int dev = 0;
+    cudaGetDevice(&dev);
+    ctx_ = new ggml_backend_cuda_context(dev);
+}
+Context::~Context() { delete (ggml_backend_cuda_context*) ctx_; }
+
+void Context::run(const Product& p, void* stream) {
+    if (p.n <= 0 || p.max_rows <= 0) return;
+    const ggml_type t = (ggml_type) p.type;
+    const int64_t qk = ggml_blck_size(t), bpr = p.w_cols / qk;
+    const mmq_args a = {(const char*) p.w, t, (const int*) p.xq, p.ids, p.bounds, p.dst, nullptr,
+                        p.w_cols, p.w_rows, p.total_rows, bpr, p.total_rows, p.ld_dst,
+                        p.n, p.n, (int64_t) (p.expert_bytes / ggml_type_size(t)), 0, 0,
+                        1, 1, 0, 0, 0,
+                        p.max_rows, p.max_rows};
+    auto& ctx = *(ggml_backend_cuda_context*) ctx_;
+    const cudaStream_t s = (cudaStream_t) stream;
+    switch (t) {
+        case GGML_TYPE_Q2_0: mul_mat_q_case<GGML_TYPE_Q2_0>(ctx, a, s); break;
+        case GGML_TYPE_IQ2_XXS: mul_mat_q_case<GGML_TYPE_IQ2_XXS>(ctx, a, s); break;
+        case GGML_TYPE_IQ2_XS: mul_mat_q_case<GGML_TYPE_IQ2_XS>(ctx, a, s); break;
+        case GGML_TYPE_IQ2_S: mul_mat_q_case<GGML_TYPE_IQ2_S>(ctx, a, s); break;
+        case GGML_TYPE_IQ3_XXS: mul_mat_q_case<GGML_TYPE_IQ3_XXS>(ctx, a, s); break;
+        case GGML_TYPE_IQ3_S: mul_mat_q_case<GGML_TYPE_IQ3_S>(ctx, a, s); break;
+        case GGML_TYPE_IQ4_NL: mul_mat_q_case<GGML_TYPE_IQ4_NL>(ctx, a, s); break;
+        case GGML_TYPE_IQ4_XS: mul_mat_q_case<GGML_TYPE_IQ4_XS>(ctx, a, s); break;
+        default:
+            std::fprintf(stderr, "prefill mmq: type %d is not covered\n", (int) t);
+            std::exit(1);
+    }
+    ck(cudaGetLastError(), "mul_mat_q");
+}
+
+void gather_native(const void* gate, const void* up, size_t gu_half_bytes, const void* down, size_t d_bytes,
+                   void* gu_dst, void* d_dst, void* stream) {
+    const cudaStream_t s = (cudaStream_t) stream;
+    const bool a16 = ((uintptr_t) gate | (uintptr_t) up | (uintptr_t) down | (uintptr_t) gu_dst | (uintptr_t) d_dst |
+                      gu_half_bytes | d_bytes) % 16 == 0;
+    if (a16) {
+        const int64_t na = (int64_t) gu_half_bytes / 16, nc = (int64_t) d_bytes / 16;
+        copy16_kernel<<<blocks(2 * na + nc), 256, 0, s>>>((const uint4*) gate, na, (const uint4*) up, na, (uint4*) gu_dst,
+                                                          (const uint4*) down, nc, (uint4*) d_dst);
+    } else {
+        const int64_t na = (int64_t) gu_half_bytes, nc = (int64_t) d_bytes;
+        copy1_kernel<<<blocks(2 * na + nc), 256, 0, s>>>((const uint8_t*) gate, na, (const uint8_t*) up, na,
+                                                         (uint8_t*) gu_dst, (const uint8_t*) down, nc, (uint8_t*) d_dst);
+    }
+    ck(cudaGetLastError(), "gather_native");
+}
+
+void gather_strata_q2(const uint8_t* blob, void* gu_dst, void* d_dst, void* stream) {
+    strata_q2_kernel<<<blocks(1280LL * 40 + 2560LL * 10), 256, 0, (cudaStream_t) stream>>>(blob, (uint16_t*) gu_dst,
+                                                                                         (uint16_t*) d_dst);
+    ck(cudaGetLastError(), "gather_strata_q2");
+}
+
+void swiglu(const float* gu, float* h, int64_t rows, int64_t n_ff, bool interleaved, void* stream) {
+    if (rows <= 0) return;
+    swiglu_kernel<<<blocks(rows * n_ff), 256, 0, (cudaStream_t) stream>>>(gu, h, rows, n_ff, interleaved);
+    ck(cudaGetLastError(), "swiglu");
+}
+
+void iota(int32_t* dst, int64_t n, void* stream) {
+    if (n <= 0) return;
+    iota_kernel<<<blocks(n), 256, 0, (cudaStream_t) stream>>>(dst, n);
+    ck(cudaGetLastError(), "iota");
+}
+
+}  // namespace strata::prefill::mmq
@@ -0,0 +1,101 @@
+// src/spec/draft_policy.cpp - see include/strata/spec/draft_policy.hpp.
+#include "strata/spec/draft_policy.hpp"
+
+#include <algorithm>
+
+namespace strata::spec {
+namespace {
+
+// The shape of a round's cost by window size, relative to one token, used only for sizes not measured yet (the
+// measured round times replace it). Between the RTX 5070's measured curves: +10 ms per token with every missed expert
+// on the CPU (bench/results/2026-09-27-spec/window-cost), flatter with the default CPU/DMA split.
+constexpr double kShape[DraftPolicy::kMaxT + 1] = {0.0, 1.0, 1.35, 1.7, 2.05, 2.45, 2.85, 3.25, 3.6};
+constexpr double kCostAlpha = 0.1;    // EMA weight of a new round time
+constexpr double kTokAlpha = 0.05;    // EMA weight of a new MTP window outcome
+constexpr double kDecay = 0.97;       // lookup counts: older windows fade
+// Before a bucket has data: the longer the match, the likelier its continuation (llama.cpp's lookup decoding gates
+// on the same thing); worth 4 observations, so a few real windows override it
+constexpr double kPriorQ[DraftPolicy::kBuckets] = {0.75, 0.88, 0.93, 0.96};
+constexpr double kPriorN = 4.0;
+constexpr int kProbes = 3;       // a lookup window size is tried this often before its guessed cost can veto it
+
+}  // namespace
+
+DraftPolicy::DraftPolicy(int max_t, double margin)
+    : max_t_(std::clamp(max_t, 1, kMaxT)), margin_(margin) {}
+
+int DraftPolicy::bucket(int match) {
+    return match < 6 ? 0 : match < 12 ? 1 : match < 24 ? 2 : 3;
+}
+
+double DraftPolicy::lookup_rate(int match) const {
+    const int b = bucket(match);
+    return (ok_[b] + kPriorN * kPriorQ[b]) / (ok_[b] + bad_[b] + kPriorN);
+}
+
+double DraftPolicy::cost_ms(int t) const {
+    t = std::clamp(t, 1, kMaxT);
+    if (cost_n_[t] > 0) return cost_[t];
+    // scale from the measured sizes, weighting each by how often it was seen
+    double num = 0.0, den = 0.0;
+    for (int u = 1; u <= kMaxT; ++u)
+        if (cost_n_[u] > 0) {
+            const double w = std::min(cost_n_[u], 20.0);
+            num += w * cost_[u] * kShape[t] / kShape[u];
+            den += w;
+        }
+    return den > 0 ? num / den : kShape[t];
+}
+
+double DraftPolicy::mtp_tokens(int t) const {
+    if (mtp_n_[t] > 0) return mtp_tok_[t];
+    return 1.0 + 0.7 * (t - 1);       // before any MTP window of this size: a typical acceptance
+}
+
+DraftPolicy::Pick DraftPolicy::choose(int t_mtp, int lookup_k, int match) const {
+    Pick p;
+    p.t = std::clamp(t_mtp, 1, max_t_);
+    if (lookup_k <= 0) return p;
+    const double base = mtp_tokens(p.t) / cost_ms(p.t);
+    const double q = lookup_rate(match);
+    double e = 1.0, qi = 1.0, best = 0.0;
+    int best_t = 0;
+    for (int k = 1; k <= std::min(lookup_k, max_t_ - 1); ++k) {
+        qi *= q;
+        e += qi;
+        const double r = e / cost_ms(k + 1);
+        if (r > best) { best = r; best_t = k + 1; }
+    }
+    if (best_t > 0 && best > base * (1.0 + margin_)) {
+        p.lookup = true;
+        p.t = best_t;
+        return p;
+    }
+    // a guessed cost can keep the policy from ever measuring a size: the first few times a confident lookup would
+    // need a size not measured yet, it is tried (verification keeps the output; only the one round's speed is at stake)
+    const int t_full = std::min(lookup_k, max_t_ - 1) + 1;
+    if (t_full > p.t && cost_n_[t_full] < kProbes && q >= 0.85) {
+        p.lookup = true;
+        p.t = t_full;
+    }
+    return p;
+}
+
+void DraftPolicy::observe(bool lookup, int t, int accepted, int match, double round_ms) {
+    t = std::clamp(t, 1, kMaxT);
+    if (round_ms > 0) {
+        cost_[t] = cost_n_[t] > 0 ? (1.0 - kCostAlpha) * cost_[t] + kCostAlpha * round_ms : round_ms;
+        cost_n_[t] += 1.0;
+    }
+    if (lookup) {
+        const int b = bucket(match);
+        ok_[b] = kDecay * ok_[b] + accepted;
+        bad_[b] = kDecay * bad_[b] + (accepted < t - 1 ? 1.0 : 0.0);
+    } else {
+        const double got = accepted + 1.0;
+        mtp_tok_[t] = mtp_n_[t] > 0 ? (1.0 - kTokAlpha) * mtp_tok_[t] + kTokAlpha * got : got;
+        mtp_n_[t] += 1.0;
+    }
+}
+
+}  // namespace strata::spec
@@ -0,0 +1,63 @@
+// src/spec/draft_policy_test.cpp - DraftPolicy: when does a lookup window beat the MTP's?
+//
+// Simulated rounds with the costs measured on the RTX 5070 (window-cost: ~10 ms more per token) check that
+//   1. with no lookup proposal the MTP window is kept;
+//   2. lookup drafts that are mostly rejected stop being taken (their bucket's rate falls);
+//   3. lookup drafts that are always accepted are taken, and the window grows with them;
+//   4. match-length buckets learn separately (short matches failing does not stop long ones);
+//   5. the policy never proposes a window beyond its cap.
+#include "strata/spec/draft_policy.hpp"
+
+#include <cstdio>
+
+using strata::spec::DraftPolicy;
+
+namespace {
+int g_fail = 0;
+void check(bool ok, const char* what) {
+    std::printf("  %-66s %s\n", what, ok ? "ok" : "FAIL");
+    if (!ok) ++g_fail;
+}
+double cost(int t) { return 19.0 + 10.5 * (t - 1); }   // ms per round, measured shape
+}  // namespace
+
+int main() {
+    std::printf("draft_policy_test\n");
+    {
+        DraftPolicy p(6);
+        for (int i = 0; i < 50; ++i) p.observe(false, 4, 2, 0, cost(4));   // MTP windows of 4: 3 tokens each
+        const DraftPolicy::Pick k = p.choose(4, 0, 0);
+        check(!k.lookup && k.t == 4, "no proposal: the MTP window");
+    }
+    {
+        DraftPolicy p(6);
+        for (int i = 0; i < 50; ++i) p.observe(false, 4, 2, 0, cost(4));
+        for (int t = 2; t <= 6; ++t) p.observe(false, t, 0, 0, cost(t));
+        for (int i = 0; i < 40; ++i) p.observe(true, 6, 0, 4, cost(6));      // short matches, all rejected
+        check(p.lookup_rate(4) < 0.15, "rejected short-match drafts: their rate falls below 0.15");
+        check(!p.choose(4, 5, 4).lookup, "rejected short-match drafts: no longer taken");
+        for (int i = 0; i < 40; ++i) p.observe(true, 6, 5, 30, cost(6));     // long matches, all accepted
+        check(p.lookup_rate(30) > 0.9, "accepted long-match drafts: their rate rises above 0.9");
+        const DraftPolicy::Pick k = p.choose(4, 5, 30);
+        check(k.lookup && k.t == 6, "accepted long matches: the full lookup window is taken");
+        check(!p.choose(4, 5, 4).lookup, "buckets are separate: short matches still not taken");
+        check(p.choose(4, 20, 30).t <= 6, "never beyond the window cap");
+    }
+    {
+        DraftPolicy p(8);
+        for (int i = 0; i < 50; ++i) p.observe(false, 3, 2, 0, cost(3));      // a very good MTP: 3 of 3 tokens
+        for (int t = 2; t <= 8; ++t) p.observe(false, t, t - 1, 0, cost(t));
+        for (int i = 0; i < 40; ++i) p.observe(true, 4, 2, 8, cost(4));       // lookup at q ~ 0.67
+        check(!p.choose(3, 7, 8).lookup, "a mediocre lookup does not replace a strong MTP window");
+    }
+    {
+        DraftPolicy p(6);
+        for (int i = 0; i < 50; ++i) p.observe(false, 4, 3, 0, cost(4));      // a near-perfect MTP, only size 4 seen
+        const DraftPolicy::Pick k = p.choose(4, 5, 40);
+        check(k.lookup && k.t == 6, "an unmeasured size is probed for a confident lookup");
+        for (int i = 0; i < 3; ++i) p.observe(true, 6, 5, 40, 3.0 * cost(6));   // it turns out very expensive
+        check(!p.choose(4, 5, 40).lookup, "after the probes, the measured cost decides");
+    }
+    std::printf(g_fail ? "FAIL\n" : "PASS\n");
+    return g_fail ? 1 : 0;
+}
@@ -14,7 +14,7 @@ ENGINE = Path(__file__).resolve().parents[1]

 def gguf_py() -> str:
    cands = [os.environ.get("STRATA_GGUF_PY"), ENGINE / "third_party" / "llama.cpp" / "gguf-py",
-             ENGINE.parents[1] / ".ref" / "llama.cpp" / "gguf-py"]
+             ENGINE.parents[min(1, len(ENGINE.parents) - 1)] / ".ref" / "llama.cpp" / "gguf-py"]   # the dev tree; a shallow install (G:\Strata) has no grandparent
    for c in cands:
        if c and (Path(c) / "gguf").is_dir():
            return str(c)
@@ -0,0 +1,96 @@
+"""tools/make_profile.py - the expert-cache profile (`data/expert-profile.bin`): every (layer, expert) pair, ranked.
+
+`--expert-cache auto` fills the VRAM slots it can afford with the profile's pairs in order, so the ranking decides
+which experts start resident (the adaptive tier then swaps in what a conversation routes most).  A profile that
+ranks fewer pairs than a card can hold caps the cache (issue #46: a 32 GB card stopped at 8,000 slots); this tool
+writes one that ranks all 24,576.
+
+The order: the base profile's ranking (default: the shipped data/expert-profile.bin), then the pairs your routing
+traces used, most frequent first, then every pair still missing, interleaved across the layers.
+
+    python tools/make_profile.py [TRACE ...] [--base data/expert-profile.bin | --no-base] [--out PATH]
+
+A routing trace comes from a one-shot engine run with `--dump-routing FILE` (a prompt typical of your use; the
+routed experts of every layer and position are written).  Point the model config's `--expert-profile` at the result.
+"""
+import argparse
+import struct
+from collections import defaultdict
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parent.parent
+N_LAYER, N_EXPERT = 48, 512
+MAGIC, VERSION = b"STRP", 1
+
+
+def read_profile(path):
+    blob = Path(path).read_bytes()
+    if blob[:4] != MAGIC:
+        raise SystemExit(f"{path}: not a Strata profile")
+    ver, nl, ne, slots, n = struct.unpack_from("<5I", blob, 4)
+    if (nl, ne) != (N_LAYER, N_EXPERT):
+        raise SystemExit(f"{path}: {nl}x{ne}, not {N_LAYER}x{N_EXPERT}")
+    return [struct.unpack_from("<HH", blob, 24 + 4 * i) for i in range(n)]
+
+
+def read_trace(path):
+    """(layer, k, k expert ids, k weights) records, as `--dump-routing` writes them."""
+    blob = Path(path).read_bytes()
+    off, freq = 0, defaultdict(int)
+    while off + 8 <= len(blob):
+        layer, k = struct.unpack_from("<ii", blob, off)
+        off += 8
+        for e in struct.unpack_from("<%di" % k, blob, off):
+            if 0 <= layer < N_LAYER and 0 <= e < N_EXPERT:
+                freq[(layer, e)] += 1
+        off += 8 * k                                    # the ids and the weights
+    return freq
+
+
+def write_profile(path, ranked):
+    table = [[-1] * N_EXPERT for _ in range(N_LAYER)]
+    for slot, (layer, e) in enumerate(ranked):
+        table[layer][e] = slot
+    with open(path, "wb") as f:
+        f.write(MAGIC + struct.pack("<5I", VERSION, N_LAYER, N_EXPERT, len(ranked), len(ranked)))
+        for layer, e in ranked:
+            f.write(struct.pack("<HH", layer, e))
+        for layer in range(N_LAYER):
+            f.write(struct.pack("<%di" % N_EXPERT, *table[layer]))
+
+
+def main():
+    ap = argparse.ArgumentParser(description=__doc__.split("\n\n")[0])
+    ap.add_argument("traces", nargs="*", help="routing traces from --dump-routing")
+    ap.add_argument("--base", default=str(ROOT / "data" / "expert-profile.bin"), help="ranking to keep first")
+    ap.add_argument("--no-base", action="store_true", help="rank by the traces only")
+    ap.add_argument("--out", default=str(ROOT / "data" / "expert-profile.bin"))
+    a = ap.parse_args()
+
+    ranked, seen = [], set()
+
+    def take(pairs):
+        for p in pairs:
+            p = (int(p[0]), int(p[1]))
+            if p not in seen:
+                seen.add(p)
+                ranked.append(p)
+
+    if not a.no_base:
+        take(read_profile(a.base))
+    n_base = len(ranked)
+    freq = defaultdict(int)
+    for t in a.traces:
+        for p, c in read_trace(t).items():
+            freq[p] += c
+    take(p for p, _ in sorted(freq.items(), key=lambda kv: (-kv[1], kv[0])))
+    n_trace = len(ranked) - n_base
+    take((layer, e) for e in range(N_EXPERT) for layer in range(N_LAYER))   # the rest, across the layers
+    write_profile(a.out, ranked)
+    assert read_profile(a.out) == ranked, "the profile did not survive the round trip"
+    print(f"wrote {a.out}: {len(ranked)} ranked pairs ({n_base} from the base, {n_trace} from the traces, "
+          f"{len(ranked) - n_base - n_trace} filled in)")
+
+
+if __name__ == "__main__":
+    main()
@@ -19,7 +19,6 @@ from pathlib import Path

 import numpy as np

-STRATA = Path(__file__).resolve().parents[3]
 sys.path.insert(0, str(Path(__file__).resolve().parent))
 from _paths import add_gguf_py  # noqa: E402
 add_gguf_py()
@@ -34,7 +34,7 @@ sys.path.insert(0, str(pathlib.Path(__file__).resolve().parent))
 from gguf_reader import GGUFFile  # noqa: E402

 REPO = pathlib.Path(__file__).resolve().parents[1]
-STRATA = REPO.parents[1]          # the development layout's default shards (setup.py always passes --gguf)
+STRATA = REPO.parents[min(1, len(REPO.parents) - 1)]   # the dev layout's default shards (setup.py passes --gguf)
 SHARD1 = STRATA / "Q2_0" / "Qwen3.8-Flash-Next-GSQ-RCO-Q2_0-00001-of-00002.gguf"

 H, FF, NE = 2560, 640, 512
@@ -41,7 +41,8 @@ from canonical_xcheck import (MAPPINGS, KV_IQ4NL, open_shard,             # noqa

 FORMAT_VERSION = 1
 ALIGN = 64                       # every plane starts on a 64-byte boundary (P1.S7)
-STRATA = pathlib.Path(__file__).resolve().parents[3]   # the development layout; build takes the shard from --gguf
+_HERE = pathlib.Path(__file__).resolve()
+STRATA = _HERE.parents[min(3, len(_HERE.parents) - 1)]   # the development layout; build takes the shard from --gguf
 SHARD2 = STRATA / "Q2_0" / "Qwen3.8-Flash-Next-GSQ-RCO-Q2_0-00002-of-00002.gguf"

 EXPERT_RE = ("ffn_gate_exps.weight", "ffn_up_exps.weight", "ffn_down_exps.weight")