docs (AMD): images through the CPU encoder and the Monitor's AMD readings (0.1.32, #304 #301)

Two changes to the prompt path's QSA selection (qsa_select.cu), measured on a Radeon AI PRO R9700 (gfx1201) with
--max-context 262144:

1. qsa_block_topk picked its register kernel by max_blocks, a CAPACITY (--max-context / 4 + 2). With a 262144-cell
   capacity every prompt, even a 4K one, took the slow reference top-k (up to ~10x slower at 128K+). The prompt path
   now passes the batch's active block count (the argument qsa_block_scores already takes); decode passes none and
   keeps the capacity rule and the original register width, so it is unchanged. On HIP the register kernel is
   instantiated for 66 blocks per thread (contexts up to 270,336 cells, the 262144 --max-context) and, below
   ~28K cells, where it measured 0.2-0.9x of the reference on gfx1201, the reference is kept. Same ids as the reference
   (checked 256/256 at every size).

2. qsa_block_scores_tc on gfx1200/gfx1201: block_scores_wmma_kernel on v_wmma_f32_16x16x16_bf16 (wave32). Each operand
   is split into three bf16 slices (hi / mid / lo are exact 8-bit mantissa slices of the fp32, so no scaling is
   needed) and the six products of order <= 2 are summed; the hi*hi chain keeps its own accumulator and the five
   corrections another, added once at the end (a single shared accumulator rounded the corrections at the big
   accumulator's ulp: 8e-7 of the score scale, failing the harness's gate at short context; split: 1.6e-7, the warp
   kernel's own error is of the same order). The tail block n_bid is the warp kernel's arithmetic. Like the TF32
   kernel it is not bitwise with the warp kernel (a near-tie can select differently). Gated at compile time
   (__gfx1200__/__gfx1201__) and run time (gcnArchName); other HIP targets keep the warp kernel; CUDA is unchanged.

qsa_select_bench now builds on HIP, takes the engine's capacity as a 4th argument, checks the scorer against an FP64
reference (gate: no worse than 4x the warp kernel's error, floored at 1e-6 of the score scale), exits non-zero on a
failed gate, and is registered with ctest (32K context under a 262144-cell capacity).

R9700, synthetic bench, 256 queries, capacity 262144: scores 0.81 -> 0.18 ms at 32K, 3.5 -> 0.65 ms at 131K, 6.8 ->
1.5 ms at 262K; top-k 3.9 -> 0.51 ms at 131K, 12.9 -> 1.3 ms at 262K. End to end (Swift 1.5 IQ3_XXS, --kv int8,
prefill timing, same binary with STRATA_SELECT_OLD=1 STRATA_TOPK_OLD=1 as the old arm): 107.7K-token prompt 79.5 s ->
59.5 s (1.34x; qsa select 23.3 s -> 3.4 s); 30.9K 1.02x; below 10K about 1.01x. ctest 44/44.

.gitattributes

@@ -4,3 +4,6 @@ bench/prompts/** -text
 # launchers keep their platform line endings wherever the repository is cloned
 *.sh text eol=lf
 *.bat text eol=crlf
+
+# copies of real packs' native_experts.txt, parsed byte for byte by tests/core/expert_layout_test.cpp
+tests/data/native_experts/** -text

CMakeLists.txt

@@ -8,7 +8,7 @@
 # below refuses anything older: silently building for a pre-Ampere 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 0.1.30 LANGUAGES CXX)
+project(strata VERSION 0.1.32 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
@@ -33,8 +33,13 @@ if(NOT CMAKE_BUILD_TYPE AND NOT CMAKE_CONFIGURATION_TYPES)
 endif()
 
 option(STRATA_ENABLE_CUDA "Build the CUDA targets (Phase 2+)" OFF)
-option(STRATA_ENABLE_HIP "Build the HIP targets for wave32 AMD GPUs (gfx1100, gfx1201)" OFF)
+option(STRATA_ENABLE_HIP "Build the HIP targets for wave32 AMD GPUs (gfx1100, gfx1201; gfx1030 unvalidated)" OFF)
 option(STRATA_PREFILL_MMQ "Build the optional GGML MMQ prefill path for HIP" OFF)
+# CUDA: MMQ prompt kernels for Q4_K / Q5_K / Q5_1 (Unsloth's UD-Q4_K_XL experts).  Off by default: the engine loads
+# every kernel when it starts (CUDA_MODULE_LOADING=EAGER), so these took ~5 MB of VRAM - 1-3 expert slots - from every
+# model, and the UD-Q4_K_XL prompt is SSD-bound with or without them (docs/UNSLOTH_Q4.md)
+option(STRATA_MMQ_KQUANTS "Build the CUDA MMQ prompt kernels for Q4_K / Q5_K / Q5_1 experts" OFF)
+option(STRATA_ORCA_Q4KS_MMQ "Build the GGML Q5_0 MMQ instance for OrcaRouter Q4_K_S" OFF)
 if(STRATA_PREFILL_MMQ AND NOT STRATA_ENABLE_HIP)
   message(FATAL_ERROR "STRATA_PREFILL_MMQ requires STRATA_ENABLE_HIP=ON")
 endif()
@@ -68,6 +73,7 @@ if(STRATA_ENABLE_CUDA)
   # `enable_language(CUDA)` gives the .cu compile rules but NOT the imported targets, so `CUDA::cudart` has to
   # be brought in explicitly for the host-side tests that call cudaMalloc themselves.
   find_package(CUDAToolkit REQUIRED)
+  option(STRATA_EXPERIMENTAL_SM60 "community build for GPUs below compute capability 7.5: Pascal sm_60, Volta sm_70 (#236)" OFF)
   if(NOT DEFINED CMAKE_CUDA_ARCHITECTURES)
     set(CMAKE_CUDA_ARCHITECTURES 120 CACHE STRING "CUDA architectures" FORCE)
   endif()
@@ -79,16 +85,22 @@ if(STRATA_ENABLE_CUDA)
     if(_base STREQUAL "native" OR _base STREQUAL "all" OR _base STREQUAL "all-major")
       continue()
     endif()
-    # STRATA_EXPERIMENTAL_SM60: a Pascal (sm_60, GP100-class) build.  It sits below every architecture
-    # these kernels were written for, so two intrinsics are replaced for it - `__dp4a` (6.1+) and
-    # `__nanosleep` (7.0+) - in include/strata/kernels/dp4a.hpp, which is bit-exact for the first and
-    # merely a shorter pause for the second.  RTX 20/30/40/50 cards must use the release engine instead.
+    # STRATA_EXPERIMENTAL_SM60: a Pascal (sm_60, GP100-class) or Volta (sm_70, V100 / Titan V, #236) build.
+    # These sit below every architecture the kernels were written for, so two intrinsics are replaced below
+    # their arch - `__dp4a` (6.1+) and `__nanosleep` (7.0+) - in include/strata/kernels/dp4a.hpp, which is
+    # bit-exact for the first and merely a shorter pause for the second (sm_70 has both).  Community-tested,
+    # not in the ready-made engine; RTX 20/30/40/50 cards must use the release engine instead.  It needs a CUDA
+    # 12.x toolkit: CUDA 13 dropped sm_60 and sm_70 (nvcc fails CMake's compiler test with "compute_70").
     if(_base LESS 75 AND NOT STRATA_EXPERIMENTAL_SM60)
       message(FATAL_ERROR
         "Strata needs an NVIDIA GPU of compute capability 7.5 or newer (RTX 20 / 30 / 40 / 50 series), but "
-        "CMAKE_CUDA_ARCHITECTURES is '${CMAKE_CUDA_ARCHITECTURES}'.  (A Pascal build is "
+        "CMAKE_CUDA_ARCHITECTURES is '${CMAKE_CUDA_ARCHITECTURES}'.  (A Pascal sm_60 or Volta sm_70 build is "
         "-DSTRATA_EXPERIMENTAL_SM60=ON; upstream does not support it.)")
     endif()
+    if(_base LESS 60)
+      message(FATAL_ERROR "CMAKE_CUDA_ARCHITECTURES '${CMAKE_CUDA_ARCHITECTURES}': even the experimental build "
+                          "needs compute capability 6.0 (sm_60) or newer")
+    endif()
     # #220 / #224: RTX 50 engines built with CUDA 12.8 crashed in the prompt path on Linux (13.0 and newer did not)
     if(_base GREATER_EQUAL 120 AND CMAKE_CUDA_COMPILER_VERSION VERSION_LESS 13.0)
       message(WARNING "Building for sm_${_base} (RTX 50) with CUDA ${CMAKE_CUDA_COMPILER_VERSION}: engines built with "
@@ -148,6 +160,10 @@ if(STRATA_BUILD_TESTS)
   add_executable(gguf_reader_test src/artifact/gguf_reader_test.cpp)
   target_link_libraries(gguf_reader_test PRIVATE strata_artifact)
   add_test(NAME gguf_reader_test COMMAND gguf_reader_test)
+  # a split model's shards and their joint view: missing shard, split keys, duplicates, truncation (synthetic)
+  add_executable(gguf_split_test tests/core/gguf_split_test.cpp)
+  target_link_libraries(gguf_split_test PRIVATE strata_artifact)
+  add_test(NAME gguf_split_test COMMAND gguf_split_test)
 endif()
 
 # ---- strata_plan ----------------------------------------------------------
@@ -166,6 +182,10 @@ if(STRATA_ENABLE_CUDA OR STRATA_ENABLE_HIP)
   add_library(strata_core STATIC src/core/device.cu src/core/pinned.cu src/platform/memory.cpp src/core/graph.cpp src/core/weights.cpp
                                src/core/layout.cpp)
   target_include_directories(strata_core PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/include)
+  if(STRATA_ENABLE_CUDA AND STRATA_EXPERIMENTAL_SM60)
+    # the runtime device check (src/core/device.cu) admits the cards this build was made for (#236)
+    target_compile_definitions(strata_core PRIVATE STRATA_EXPERIMENTAL_SM60=1)
+  endif()
   # The selected runtime is PUBLIC because graph.cpp is a HOST translation unit that calls cudaGraphLaunch and
   # cudaEventQuery: the .cu sources get cudart through the CUDA language rules, a .cpp does not, and the
   # linker error ("unresolved external symbol cudaGraphInstantiate") names the function rather than the cause.
@@ -254,6 +274,15 @@ if(STRATA_ENABLE_CUDA OR STRATA_ENABLE_HIP)
     target_include_directories(qsa_prompt_attn_parity PRIVATE ${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES})
     target_link_libraries(qsa_prompt_attn_parity PRIVATE strata_kernels CUDA::cudart)
   endif()
+  # the QSA selection (block scores on the warp kernel and the tensor-core one, top-k): a GPU bench with a
+  # scorer-vs-scorer comparison and an FP64 accuracy gate (synthetic, no model); the ctest runs a 32K context under a
+  # 262144-cell capacity (the top-k dispatch follows the capacity) and exits non-zero on a failed gate
+  if((STRATA_BUILD_TESTS OR STRATA_PARITY_PROMPT_ATTN) AND STRATA_ENABLE_HIP)
+    add_executable(qsa_select_bench src/kernels/qsa_select_bench.cpp)
+    set_source_files_properties(src/kernels/qsa_select_bench.cpp PROPERTIES LANGUAGE HIP)
+    target_link_libraries(qsa_select_bench PRIVATE strata_kernels strata_hip_runtime)
+    add_test(NAME qsa_select_bench COMMAND qsa_select_bench 32768 256 3 262144)
+  endif()
   # plan v0.3 P6: llama.cpp's block layouts and codebook grids for the i-quant formats (MIT, third_party/ggml)
   target_include_directories(strata_kernels PRIVATE third_party/ggml)
   # Match the pinned llama.cpp CUDA arithmetic contract for this adapter only.
@@ -321,6 +350,19 @@ if(STRATA_ENABLE_CUDA OR STRATA_ENABLE_HIP)
     add_test(NAME file_expert_source_test COMMAND file_expert_source_test)
     set_tests_properties(file_expert_source_test PROPERTIES TIMEOUT 60)
   endif()
+  if(STRATA_BUILD_TESTS AND STRATA_NATIVE_EXPERTS)
+    # native_experts.txt v2-v4 (the real packs' copies and the per-role shard column) and the arena filled from
+    # synthetic GGUF shards at the real expert dimensions
+    add_executable(expert_layout_test tests/core/expert_layout_test.cpp)
+    target_link_libraries(expert_layout_test PRIVATE strata_engine)
+    add_test(NAME expert_layout_test COMMAND expert_layout_test ${CMAKE_CURRENT_SOURCE_DIR}/tests/data/native_experts)
+    set_tests_properties(expert_layout_test PROPERTIES TIMEOUT 60)
+    # #326: a native pack's BF16 (--compat-bf16) PLE key stays in the arena; a quantized key row loads as before
+    add_executable(native_dense_ple_key_test tests/core/native_dense_ple_key_test.cpp)
+    target_link_libraries(native_dense_ple_key_test PRIVATE strata_engine)
+    add_test(NAME native_dense_ple_key_test COMMAND native_dense_ple_key_test)
+    set_tests_properties(native_dense_ple_key_test PROPERTIES SKIP_RETURN_CODE 77 TIMEOUT 60)
+  endif()
   if(STRATA_BUILD_CONVERSATION_TESTS)
     add_executable(conversation_snapshot_test src/core/conversation_snapshot_test.cpp)
     target_link_libraries(conversation_snapshot_test PRIVATE strata_engine)
@@ -391,6 +433,17 @@ if(STRATA_ENABLE_CUDA OR STRATA_ENABLE_HIP)
       add_test(NAME hip_prefill_native_batch COMMAND hip_prefill_native_batch)
       list(APPEND _strata_hip_tests hip_prefill_native_batch)
     endif()
+    # S6: the AMD fast router against the portable kernel, bitwise
+    add_executable(hip_router_fast tests/hip/router_fast.cpp)
+    target_link_libraries(hip_router_fast PRIVATE strata_kernels strata_hip_runtime)
+    add_test(NAME hip_router_fast COMMAND hip_router_fast)
+    list(APPEND _strata_hip_tests hip_router_fast)
+    # S6: the RDNA4 matrix-core prompt attention (STRATA_HIP_WMMA) against the FP32 kernel and FP64; skips off gfx12
+    add_executable(hip_prompt_attn_wmma src/kernels/qsa_prompt_attn_parity.cpp)
+    target_link_libraries(hip_prompt_attn_wmma PRIVATE strata_kernels strata_hip_runtime)
+    add_test(NAME hip_prompt_attn_wmma COMMAND hip_prompt_attn_wmma 32768 2048 3)
+    set_tests_properties(hip_prompt_attn_wmma PROPERTIES TIMEOUT 600 SKIP_RETURN_CODE 77)
+    list(APPEND _strata_hip_tests hip_prompt_attn_wmma)
     if(STRATA_HIPBLASLT_AVAILABLE AND EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/tests/hip/prefill_hipblaslt_gemm.cpp")
       add_executable(hip_prefill_hipblaslt_gemm tests/hip/prefill_hipblaslt_gemm.cpp)
       target_include_directories(hip_prefill_hipblaslt_gemm PRIVATE "${CMAKE_CURRENT_SOURCE_DIR}/src/prefill")
@@ -426,6 +479,49 @@ if(STRATA_ENABLE_CUDA OR STRATA_ENABLE_HIP)
   add_executable(iq_parity src/kernels/iq_parity.cpp)
   target_include_directories(iq_parity PRIVATE ${_strata_gpu_include_directories})
   target_link_libraries(iq_parity PRIVATE strata_kernels ${_strata_gpu_runtime_target})
+  # TODO 24: the i-quant fixtures are generated, not shipped.  tools/iq_fixture.py needs python3 + numpy
+  # and the repository's vendored gguf-py (no pip installs); it writes deterministic fixtures into the
+  # BUILD directory (the source tree is not touched).  The CTest fixture makes generation-to-test order
+  # explicit even under parallel execution; a fixture-generation failure fails iq_parity with it.
+  find_package(Python3 COMPONENTS Interpreter QUIET)
+  # the generator needs an interpreter WITH numpy: probe the configured one, the setup's .venv, and the
+  # system python3; -DIQ_FIXTURE_PYTHON=... overrides the search outright.
+  set(_iq_python "")
+  set(_iq_candidates "")
+  if(IQ_FIXTURE_PYTHON)
+    list(APPEND _iq_candidates ${IQ_FIXTURE_PYTHON})
+  else()
+    if(Python3_Interpreter_FOUND)
+      list(APPEND _iq_candidates ${Python3_EXECUTABLE})
+    endif()
+    list(APPEND _iq_candidates "${CMAKE_CURRENT_SOURCE_DIR}/.venv/bin/python"
+                                "${CMAKE_CURRENT_SOURCE_DIR}/.venv/Scripts/python.exe"
+                                /usr/bin/python3
+                                python3)
+  endif()
+  foreach(_cand IN LISTS _iq_candidates)
+    if(_cand STREQUAL "")
+      continue()
+    endif()
+    execute_process(COMMAND ${_cand} -c "import numpy" OUTPUT_QUIET ERROR_QUIET RESULT_VARIABLE _iq_probe)
+    if(_iq_probe EQUAL 0)
+      set(_iq_python "${_cand}")
+      break()
+    endif()
+  endforeach()
+  if(NOT _iq_python STREQUAL "")
+    add_test(NAME iq_parity_fixtures
+             COMMAND ${_iq_python} ${CMAKE_CURRENT_SOURCE_DIR}/tools/iq_fixture.py
+                     --out ${CMAKE_CURRENT_BINARY_DIR}/iq_fixture)
+    # exit 3: numpy or the vendored gguf-py is missing (a trimmed third_party) - a skip, not a failure
+    set_tests_properties(iq_parity_fixtures PROPERTIES FIXTURES_SETUP iq_fixture_data SKIP_RETURN_CODE 3)
+    add_test(NAME iq_parity COMMAND iq_parity ${CMAKE_CURRENT_BINARY_DIR}/iq_fixture)
+    set_tests_properties(iq_parity PROPERTIES FIXTURES_REQUIRED iq_fixture_data SKIP_RETURN_CODE 3)
+  else()
+    message(STATUS "STRATA tests: no python3 with numpy was found (the configured interpreter and .venv "
+                   "were probed) - the iq_parity fixtures and its test are not registered; the fixtures "
+                   "need python3 with numpy and the repository's vendored gguf-py")
+  endif()
   if(STRATA_ENABLE_CUDA)
     # native_mmvq's multi_exact contract, bitwise: a T-column call against T single-column calls on the same Q8_1
     # bytes, with the flag off as the negative control (synthetic, GPU, no model; the control assumes 32-lane warps)
@@ -434,6 +530,12 @@ if(STRATA_ENABLE_CUDA OR STRATA_ENABLE_HIP)
     target_link_libraries(mmvq_multi_parity PRIVATE strata_kernels ${_strata_gpu_runtime_target})
     add_test(NAME mmvq_multi_parity COMMAND mmvq_multi_parity)
   endif()
+  # the IQ kernels that decode a weight part once for every column/entry, bitwise vs the per-column ones
+  # (GPU, synthetic, no model; --bench adds old/new timings)
+  add_executable(iq_multi_parity src/kernels/iq_multi_parity.cpp)
+  target_include_directories(iq_multi_parity PRIVATE ${_strata_gpu_include_directories})
+  target_link_libraries(iq_multi_parity PRIVATE strata_kernels ${_strata_gpu_runtime_target})
+  add_test(NAME iq_multi_parity COMMAND iq_multi_parity)
   add_executable(shared_expert_parity src/kernels/shared_expert_parity.cpp)
   target_include_directories(shared_expert_parity PRIVATE ${_strata_gpu_include_directories})
   target_link_libraries(shared_expert_parity PRIVATE strata_kernels ${_strata_gpu_runtime_target})
@@ -549,6 +651,13 @@ if(STRATA_ENABLE_CUDA OR STRATA_ENABLE_HIP)
   target_link_libraries(cvec_parity PRIVATE strata_kernels ${_strata_gpu_runtime_target})
   add_test(NAME cvec_parity COMMAND cvec_parity)
 
+  # The rewritten per-hit and grouped expert kernels vs the previous ones, bitwise (GPU, synthetic, no model).
+  # `--bench` adds previous/new timings on engine-like shapes.
+  add_executable(s2_expert_grouped_parity src/kernels/s2_expert_grouped_parity.cpp)
+  target_include_directories(s2_expert_grouped_parity PRIVATE ${_strata_gpu_include_directories})
+  target_link_libraries(s2_expert_grouped_parity PRIVATE strata_kernels ${_strata_gpu_runtime_target})
+  add_test(NAME s2_expert_grouped_parity COMMAND s2_expert_grouped_parity --selftest)
+
   # Plan v0.3 P3: native MMVQ for 1..8 tokens (bitwise selftest + --bench timing); synthetic data, no model.
   if(STRATA_BUILD_TESTS AND EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/bench/micro/native_mmvq_multi.cpp")
     add_executable(native_mmvq_multi bench/micro/native_mmvq_multi.cpp)
@@ -679,6 +788,9 @@ set(STRATA_GGML_DIR "" CACHE PATH "llama.cpp checkout to take ggml from (empty:
 if(STRATA_PREFILL_MMQ AND NOT STRATA_NATIVE_EXPERTS)
   message(FATAL_ERROR "STRATA_PREFILL_MMQ requires STRATA_NATIVE_EXPERTS=ON for GGML sources")
 endif()
+if(STRATA_ORCA_Q4KS_MMQ AND NOT STRATA_NATIVE_EXPERTS)
+  message(FATAL_ERROR "STRATA_ORCA_Q4KS_MMQ requires STRATA_NATIVE_EXPERTS=ON for GGML sources")
+endif()
 if(STRATA_NATIVE_EXPERTS)
   enable_language(C)
   set(BUILD_SHARED_LIBS OFF CACHE BOOL "" FORCE)
@@ -713,18 +825,25 @@ if(STRATA_NATIVE_EXPERTS)
   # 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
-                                           src/kernels/cpu/iq_avx2.cpp)
+                                           src/kernels/cpu/iq_avx2.cpp src/kernels/cpu/kq_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")
+    set_source_files_properties(src/kernels/cpu/kq_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")
+    set_source_files_properties(src/kernels/cpu/kq_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)
+  set(_strata_mmq_types q2_0 iq2_xxs iq2_xs iq2_s iq3_xxs iq3_s iq4_nl iq4_xs q8_0)
+  # #296: OrcaRouter Q4_K_S's Q5_0 experts (Q4_K and Q5_1 come with STRATA_MMQ_KQUANTS on CUDA, S5's branch)
+  if(STRATA_ORCA_Q4KS_MMQ)
+    list(APPEND _strata_mmq_types q5_0)
+  endif()
   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
@@ -736,7 +855,14 @@ if(STRATA_NATIVE_EXPERTS)
     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 q8_0)   # q8_0: the drafter's matrices (E-9)
+    # q8_0: the drafter's matrices (E-9); STRATA_ORCA_Q4KS_MMQ adds q5_0 (#296, in _strata_mmq_types), and
+    # STRATA_MMQ_KQUANTS q4_k q5_k q5_1, the experts of Unsloth's UD-Q4_K_XL (docs/UNSLOTH_Q4.md; the dispatch
+    # follows eddoursul/Strata 7706c1b)
+    set(_strata_mmq_cuda_types ${_strata_mmq_types})
+    if(STRATA_MMQ_KQUANTS)
+      list(APPEND _strata_mmq_cuda_types q4_k q5_k q5_1)
+    endif()
+    foreach(_t IN LISTS _strata_mmq_cuda_types)
       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}
@@ -746,8 +872,21 @@ if(STRATA_NATIVE_EXPERTS)
       target_compile_options(strata_mmq PRIVATE $<$<COMPILE_LANGUAGE:CUDA>:-Xcompiler=/bigobj>)
     endif()
     target_link_libraries(strata_mmq PUBLIC ggml-base ${_strata_gpu_runtime_target})
+    if(STRATA_MMQ_KQUANTS)
+      target_compile_definitions(strata_mmq PRIVATE STRATA_MMQ_KQUANTS=1)
+    endif()
+    if(STRATA_ORCA_Q4KS_MMQ)
+      target_compile_definitions(strata_mmq PRIVATE STRATA_ORCA_Q4KS_MMQ=1)
+    endif()
     target_link_libraries(strata_prefill PUBLIC strata_mmq)
     target_compile_definitions(strata_prefill PUBLIC STRATA_PREFILL_MMQ=1)
+    if(STRATA_BUILD_TESTS AND STRATA_MMQ_KQUANTS)
+      # the UD-Q4_K_XL expert formats through MMQ against ggml's dequantized weights (synthetic, a few seconds)
+      add_executable(prefill_mmq_kquant_test tests/cuda/prefill_mmq_kquant_test.cpp)
+      target_link_libraries(prefill_mmq_kquant_test PRIVATE strata_mmq ggml-base ${_strata_gpu_runtime_target})
+      add_test(NAME prefill_mmq_kquant_test COMMAND prefill_mmq_kquant_test)
+      set_tests_properties(prefill_mmq_kquant_test PROPERTIES TIMEOUT 120)
+    endif()
   elseif(STRATA_PREFILL_MMQ)
     # Compile only the pinned GGML MMQ and activation-quantizer sources used by prefill. This uses
     # common.cuh's native HIP path without enabling a second GGML HIP backend.
@@ -764,7 +903,7 @@ if(STRATA_NATIVE_EXPERTS)
       src/prefill/moe_mmq.cu
       src/prefill/ggml_cuda_host.cu
       "${_strata_mmq}/quantize.cu")
-    foreach(_t q2_0 iq2_xxs iq2_xs iq2_s iq3_xxs iq3_s iq4_nl iq4_xs q8_0)
+    foreach(_t IN LISTS _strata_mmq_types)
       list(APPEND _strata_mmq_sources "${_strata_mmq}/template-instances/mmq-instance-${_t}.cu")
     endforeach()
     set_source_files_properties(${_strata_mmq_sources} PROPERTIES LANGUAGE HIP)
@@ -773,6 +912,9 @@ if(STRATA_NATIVE_EXPERTS)
       PRIVATE "${_strata_gg}/include" "${_strata_gg}/src" "${_strata_mmq}"
       PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/include)
     target_compile_definitions(strata_mmq PRIVATE GGML_USE_HIP=1)
+    if(STRATA_ORCA_Q4KS_MMQ)
+      target_compile_definitions(strata_mmq PRIVATE STRATA_ORCA_Q4KS_MMQ=1)
+    endif()
     target_link_libraries(strata_mmq PUBLIC ggml-base hip::host roc::hipblas)
     target_link_libraries(strata_prefill PUBLIC strata_mmq)
     target_compile_definitions(strata_prefill PUBLIC STRATA_PREFILL_MMQ=1)
@@ -794,6 +936,32 @@ if(STRATA_NATIVE_EXPERTS)
     add_executable(native_expert_parity src/kernels/native_expert_parity.cpp)
     target_include_directories(native_expert_parity PRIVATE ${_strata_gpu_include_directories})
     target_link_libraries(native_expert_parity PRIVATE strata_kernels strata_kernels_cpu strata_artifact ${_strata_gpu_runtime_target})
+    if(STRATA_BUILD_TESTS AND STRATA_ENABLE_CUDA)
+      # Unsloth UD-Q4_K_XL's three expert pairs (and Q8_0 alone, its embedding) on synthetic ggml-quantized weights
+      # at 2560/640: GPU and CPU experts against the float reference, GPU dequantizers against to_float
+      foreach(_pair q4_K/q5_1 q4_K/q8_0 q5_K/q8_0 q8_0/q8_0)
+        string(REPLACE "/" "_" _name "${_pair}")
+        add_test(NAME native_expert_parity_${_name} COMMAND native_expert_parity --synthetic ${_pair})
+        set_tests_properties(native_expert_parity_${_name} PROPERTIES TIMEOUT 120)
+      endforeach()
+      add_test(NAME native_expert_parity_q5_1_min COMMAND native_expert_parity --q5_1-min)
+      # #290: the BF16 token embedding (--embd-gguf) gathered and dequantized bit for bit
+      add_test(NAME native_expert_parity_bf16_embd COMMAND native_expert_parity --bf16-embd)
+      # a pair without GPU kernels (Q6_K gate/up) is refused by native_expert_supported, not run
+      add_test(NAME native_expert_parity_refuses_q6_K COMMAND native_expert_parity --synthetic q6_K/q8_0)
+      set_tests_properties(native_expert_parity_refuses_q6_K PROPERTIES WILL_FAIL TRUE)
+    endif()
+  endif()
+  # #296's real-weight checks (OrcaRouter Q4_K_S files; docs/ORCA_Q4_K_S.md) - test programs, not part of a release
+  if(STRATA_ENABLE_CUDA AND STRATA_BUILD_TESTS)
+    add_executable(ple_q5_parity src/kernels/ple_q5_parity.cpp)
+    target_link_libraries(ple_q5_parity PRIVATE strata_kernels strata_artifact ggml-base ${_strata_gpu_runtime_target})
+    add_executable(q5_projection_parity src/kernels/q5_projection_parity.cpp)
+    target_include_directories(q5_projection_parity PRIVATE ${_strata_gpu_include_directories})
+    target_link_libraries(q5_projection_parity PRIVATE strata_kernels strata_artifact ggml-base ${_strata_gpu_runtime_target})
+    # #291: the FP8 PLE table (tools/ple_fp8_pack.py) through PleTable vs the checkpoint's own values
+    add_executable(ple_fp8_parity src/kernels/ple_fp8_parity.cpp)
+    target_link_libraries(ple_fp8_parity PRIVATE strata_kernels ${_strata_gpu_runtime_target})
   endif()
 endif()
 

Dockerfile

@@ -60,8 +60,7 @@ ARG BUILD_VISION=1
 
 RUN python3 -m venv .venv \
     && .venv/bin/pip install --no-cache-dir --upgrade pip \
-    && .venv/bin/pip install --no-cache-dir \
-        numpy jinja2 regex pyyaml tqdm requests cmake ninja pillow psutil \
+    && .venv/bin/pip install --no-cache-dir -r requirements.txt \
     && chmod +x setup.sh docker-entrypoint.sh
 
 # llama.cpp at the pinned commit, then the engine and the image encoder, built

README.md

@@ -42,6 +42,9 @@ A card with more VRAM is faster, because more of the model fits on the GPU: an R
 Every PC is different: `START-HERE.bat --calibrate` measures a few engine settings on yours and keeps the fastest
 (about 5-10 minutes; on the PC above it made the Coder 7% faster).
 
+Measured Strata on your own PC? See [Community benchmark results](docs/COMMUNITY_BENCHMARKS.md)
+for a report template and how to share your results in a pull request.
+
 **Two or three NVIDIA cards?** Just run `START-HERE.bat`: it lists your cards, says which ones Strata can use, and
 asks whether to share the model across them (recommended when two can). An install made on one card asks once at
 its next start. Or choose yourself: `START-HERE.bat --gpus 0,2` (both, remembered), `--gpus all`, or `--gpu 0` (one
@@ -84,9 +87,16 @@ one later with `SETUP.bat` (the same as `START-HERE.bat --setup`; on Linux `./se
 For **OrcaRouter's Flash-Next Uncensored IQ3_XXS**, see the [manual compatibility setup](docs/ORCA.md).
 It needs an explicit packing conversion and is not an installer menu option.
 
-An **AMD Radeon RX 7900 XT / XTX, RX 9070 / 9070 XT or Radeon AI PRO R9700 on Linux** works too (experimental):
+**Unsloth's 4-bit UD-Q4_K_XL** (experimental) is the fourth version in setup's menu (`--family unsloth`): the closest
+to the full model, but a 111 GB download whose 77 GB of experts do not fit in RAM. Strata keeps your RAM minus 24 GB
+of them in RAM and reads the rest from the SSD while it answers: 7-8.5 tokens/s on a 64 GB PC with a 12 GB GPU, several
+times slower than the sizes above, and long prompts are slow. It needs 48 GB of RAM or more, an NVMe SSD and one
+NVIDIA GPU (no images yet). Details and measurements: [UD-Q4_K_XL](docs/UNSLOTH_Q4.md).
+
+An **AMD Radeon RX 7900 XT / XTX, RX 9070 / 9070 XT or Radeon AI PRO R9700 on Linux** works too (experimental; the
+RX 7800 XT / 7700 XT and RX 9060 XT were validated by their owners; the RX 6800 / 6900 series, gfx1030, is community-reported):
 `./setup.sh --backend hip`, chosen by itself on a PC with no NVIDIA card Strata can use. It installs ROCm without sudo
-and compiles the engine (one GPU, no images yet). Details: [AMD HIP](docs/AMD_HIP.md).
+and compiles the engine (no images yet; several cards with `--gpus`). Details: [AMD HIP](docs/AMD_HIP.md).
 
 ## Install
 
@@ -177,6 +187,19 @@ the same way - nothing big is downloaded again.
 **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.
 
+### Where things are stored
+
+- **Your chats: only in your browser.** The Chat tab keeps the conversation, its settings and the API key you typed
+  in the browser's local storage (`strata.*` keys) - not on the server and not in the Strata folder. Pictures are not
+  kept, only their names. Another browser or a private window starts empty; clearing the site's data deletes them.
+- **How the model starts:** `strata-<model>.json` in the Strata folder (context, GPUs, host, API key, ...), written
+  by setup; next to it `run-<model>.bat` / `.sh`, the log `strata-<model>.log` and, when you use "Use for other
+  apps too", `strata-<model>.shared-settings.json`.
+- **The model files** (`models/`, `packs/`, `mtp/`, 70-120 GB): in **`Strata-data` next to the Strata folder**, or
+  wherever `--data-dir` put them.
+- **Where that data folder is:** `%APPDATA%\Strata\settings.json` on Windows, `~/.config/strata/settings.json` on
+  Linux ([details](docs/DETAILS.md)).
+
 ## Something went wrong?
 
 **My PC froze, or got very slow, the first time Strata started.**
@@ -241,8 +264,9 @@ Want the full picture? The [details](docs/DETAILS.md#how-it-works) explain every
 
 - Model: [Qwen3.8-Flash-Next](https://huggingface.co/Qwen/Qwen3.8-Flash-Next) by the Qwen team; compressed versions by
   [ISTA-DASLab](https://huggingface.co/ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF);
-  [Swift 1.5](https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF) by UkisAI. Their licenses apply
-  to the model files.
+  [Swift 1.5](https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF) by UkisAI; the experimental
+  [UD-Q4_K_XL](https://huggingface.co/unsloth/Qwen3.8-Flash-Next-GGUF) by Unsloth (its support follows
+  [eddoursul/Strata](https://github.com/eddoursul/Strata)). Their licenses apply to the model files.
 - Built with parts of [llama.cpp / ggml](https://github.com/ggml-org/llama.cpp) (MIT). Ideas from
   [Splash](https://github.com/incoai/splash), [ninfer](https://github.com/Neroued/ninfer) and
   [HyperQwen](https://github.com/syv-ai/HyperQwen). More in the [details](docs/DETAILS.md#credits-and-licenses).

bench/results/2026-09-29-rtx3090-epyc-milan/README.md

@@ -0,0 +1,46 @@
+# Preliminary native RTX 3090 / EPYC 7453 results
+
+Issue: [#165](https://github.com/Niko1221/Strata/issues/165)
+Engine: Strata v0.1.26, source build at `ac8b251`
+Status: preliminary; prompt set and harness guidance requested upstream.
+
+## Hardware
+
+| Component | Guest-visible details |
+|---|---|
+| CPU | AMD EPYC 7453, 20 vCPU, AVX2; AVX-512 not exposed |
+| RAM | 165 GiB visible to Ubuntu 24.04.5 KVM guest; physical DIMM layout and bandwidth not verified |
+| GPUs | 2x RTX 3090 24 GB; PCIe Gen4 x16 maximum reported per card; driver 595.91.07 |
+| Power limit | 280 W configured on each GPU; defaults are 350 W / 420 W, max-adjustable limits 366 W / 450 W |
+| Storage | 541 GiB ext4 guest data disk; host storage layout not verified |
+
+The power values above are configured caps, not measured workload draw. Idle draw during the hardware capture was approximately 35 W / 29 W.
+
+## Method
+
+Native Linux setup, local build from v0.1.26, `--prefill auto`, shipped expert profile, automatic expert cache, int8 KV, MTP on, images off, experimental speed projection off. The GPU power limit was 280 W per card. Native runs used a systemd service with `LimitMEMLOCK=infinity`; other llama-swap model containers were stopped.
+
+The local API harness used the public Strata source tree as its prompt corpus, with generated tasks, `temperature: 0`, `reasoning_effort: none`, and `max_tokens: 256`. It is not the upstream code-agent prompt set or harness. Runs use unique request IDs and no conversation reuse in speed mode. Three runs per speed cell are summarized by medians; actual `usage.prompt_tokens` are recorded because nominal context labels can drift. The first decode run is also shown. Several requests ended early on EOS, so actual generated counts are in the raw JSON.
+
+No calibration comparison, upstream prompt comparison, `STRATA_SPLIT_TIMING` stage timing, or under-load power-draw capture has been done. Treat all prompt/decode comparisons with upstream as indicative only until the requested upstream method is available.
+
+## Results
+
+See [`matrix.md`](matrix.md) for summary tables and [`matrix.json`](matrix.json) for all successful run records. Per-run source JSON, logs, the local harness, and scrubbed hardware facts are in [`data/`](data/).
+
+Highlights:
+
+- IQ3_XXS on one 3090: 1K–262K cells, three runs each; 262K prompts were 232,183 / 259,589 / 261,068 tokens and fit within the 262,144 context.
+- Coder IQ1_M at 32K: single-GPU median decode 66.8 tok/s; dual-GPU auto (K=19) median 98.6 tok/s.
+- Fixed Coder K sweep: K=22 was the highest observed three-run median (103.3 tok/s); independently generated prompts mean this is not a paired ranking.
+- IQ3_XXS and IQ3_S dual-GPU auto both selected K=24 at 32K.
+- IQ3_XXS and IQ3_S needles passed 3/3 at 8K / 16K / 28K on the tested single/dual configurations; two-turn checkpoint reuse passed for both.
+
+## Limits and follow-up
+
+- Upstream prompts/harness are not yet available; all current speed data uses a local source-tree corpus.
+- The guest exposes one NUMA node and 165 GiB RAM, but physical DIMM topology and memory bandwidth are unknown.
+- PCIe x16 is the reported maximum link width; the engine probes were about 23–26 GB/s. Verify PCIe generation/width under sustained load before claiming a direct x16-vs-x4 comparison.
+- Coder dual-GPU needle/two-turn checks and dual-GPU timing breakdowns are not run yet.
+- K=18 has one successful run at 83.1 tok/s; a repeated startup failed while uploading a dense weight with an out-of-memory error, so it is not a three-run median.
+- Calibration/default comparisons and IQ3_S single-GPU results remain outstanding.

bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-Coder-1x3090/speed-32k-1.json

@@ -0,0 +1,360 @@
+{
+ "label": "native-v0.1.26-Coder-1x3090",
+ "mode": "speed",
+ "ctx": "32k",
+ "target_tokens": 31488,
+ "run": 1,
+ "run_id": "6c1252339f83",
+ "corpus_repeated": false,
+ "time": "2026-09-29T21:11:07",
+ "prompt_tokens": 28317,
+ "completion_tokens": 48,
+ "ttft_s": 12.868,
+ "e2e_s": 13.739,
+ "api_prompt_tps": 2200.5,
+ "api_decode_tps": 54.0,
+ "engine_prompt_tps": 2229.2,
+ "engine_decode_tps": 52.7,
+ "log_lines": [
+  "strata serve: prompt 28317 tokens = 0 reused + 28317 read in 12703 ms (2229.2 tok/s), 48 generated in 911 ms (52.7 tok/s), drafts accepted 31 of 38, 2 checkpoints",
+  "strata serve: decode expert cache hit rate: 96.3% (25001 hits / 25956 lookups)"
+ ],
+ "answer_head": "I'll start by exploring the repository structure to understand what's there and identify the last file.",
+ "metrics_after": {
+  "engine": {
+   "model": "qwen3.8-flash-next-coder-iq1_m",
+   "max_context": 32768,
+   "images": false,
+   "version": "0.1.26",
+   "context": 32768,
+   "kv": "int8",
+   "kv_resident": 0,
+   "expert_slots": 8973,
+   "expert_cache_mib": 17503,
+   "spec": 6,
+   "mtp_max": 4,
+   "lookup": 3,
+   "vram_free_mib": 369,
+   "cvec": 0,
+   "arena_mib": 23981,
+   "pool_workers": 19,
+   "pcie_frac": "0.55",
+   "spec_min_p": "0.50",
+   "engine": "0.1.26"
+  },
+  "live": {
+   "state": "idle",
+   "queued": 0,
+   "phase": null,
+   "prompt_tokens": null,
+   "prompt_read": null,
+   "prompt_total": null,
+   "generated": null,
+   "max_tokens": null,
+   "elapsed_s": null,
+   "tok_s": null,
+   "tok_s_mean": null,
+   "tok_s_window_s": null
+  },
+  "requests": [
+   {
+    "projection": null,
+    "time": 1790716268.10927,
+    "duration_s": 13.6,
+    "finish": "stop",
+    "prompt_tokens": 28317,
+    "reused": 0,
+    "output_tokens": 48,
+    "engine_generated": 48,
+    "prompt_ms": 12702.6,
+    "decode_ms": 910.6,
+    "decode_tok_s": 52.7,
+    "hit_rate": 0.963
+   }
+  ],
+  "requests_kept": 1,
+  "totals": {
+   "since": 1790716256.7391467,
+   "requests": 1,
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+   "reused": 0,
+   "output_tokens": 48,
+   "prompt_ms": 12702.6,
+   "decode_ms": 910.6
+  },
+  "hardware": {
+   "gpu_util": 88,
+   "gpu_mem_used": 25531711488,
+   "gpu_mem_total": 25769803776,
+   "gpu_temp": 46,
+   "gpu_power": 220.362,
+   "gpu_power_limit": 280.0,
+   "gpu_pcie_gen": 4,
+   "gpu_pcie_gen_max": 4,
+   "gpu_pcie_width": 16,
+   "gpu_pcie_rx_mb": 0.28515625,
+   "gpu_pcie_tx_mb": 0.4287109375,
+   "cpu": 81.3,
+   "ram_used": 43964985344,
+   "ram_total": 177175990272,
+   "disk_read_mb": 2.9148478336133063,
+   "disk_write_mb": 0.0,
+   "tok_s": 0.0,
+   "tok_s_mean": 0.0
+  },
+  "hardware_static": {
+   "gpu_name": "NVIDIA GeForce RTX 3090",
+   "gpu_count": 1,
+   "cpu_name": "AMD EPYC 7453 28-Core Processor",
+   "cores": 20,
+   "threads": 20,
+   "psutil": true
+  },
+  "history": {
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+  "time": 1790716282.234284
+ }
+}

bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-Coder-1x3090/speed-32k-2.json

@@ -0,0 +1,491 @@
+{
+ "label": "native-v0.1.26-Coder-1x3090",
+ "mode": "speed",
+ "ctx": "32k",
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+}

bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-Coder-1x3090/speed-32k-3.json

@@ -0,0 +1,622 @@
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+ "answer_head": "I'll start by exploring the repository structure to understand what we're working with.",
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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-Coder-2x3090-K18/speed-32k-1.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-Coder-2x3090-K22/speed-32k-1.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-Coder-2x3090-K22/speed-32k-2.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-Coder-2x3090-K22/speed-32k-3.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-Coder-2x3090-K24/speed-32k-1.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-Coder-2x3090-K24/speed-32k-2.json

@@ -0,0 +1,392 @@
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+ "answer_head": "I'll start by exploring the repository structure to understand what we're working with.",
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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-Coder-2x3090-K24/speed-32k-3.json

@@ -0,0 +1,496 @@
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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-Coder-2x3090-K26/speed-32k-1.json

@@ -0,0 +1,261 @@
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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-Coder-2x3090-K26/speed-32k-2.json

@@ -0,0 +1,365 @@
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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-Coder-2x3090-K26/speed-32k-3.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-Coder-2x3090-K28/speed-32k-1.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-Coder-2x3090-K28/speed-32k-2.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-Coder-2x3090-K28/speed-32k-3.json

@@ -0,0 +1,487 @@
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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-Coder-2x3090-K30/speed-32k-1.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-Coder-2x3090-K30/speed-32k-2.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-Coder-2x3090-K30/speed-32k-3.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-Coder-2x3090-auto/speed-32k-1.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-Coder-2x3090-auto/speed-32k-2.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-Coder-2x3090-auto/speed-32k-3.json

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+}

bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-IQ3_S-2x3090-auto-needle/needle-16k-1.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-IQ3_S-2x3090-auto-needle/needle-28k-1.json

@@ -0,0 +1,764 @@
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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-IQ3_S-2x3090-auto-needle/needle-8k-1.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-IQ3_S-2x3090-auto-two-turn/two-turn-16k-1.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-IQ3_S-2x3090-auto/speed-32k-1.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-IQ3_S-2x3090-auto/speed-32k-2.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-IQ3_S-2x3090-auto/speed-32k-3.json

@@ -0,0 +1,721 @@
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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-IQ3_XXS-1x3090-needle/needle-16k-1.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-IQ3_XXS-1x3090-needle/needle-28k-1.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-IQ3_XXS-1x3090-needle/needle-8k-1.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-IQ3_XXS-1x3090-two-turn/two-turn-16k-1.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-IQ3_XXS-1x3090/speed-128k-1.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-IQ3_XXS-1x3090/speed-128k-2.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-IQ3_XXS-1x3090/speed-128k-3.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-IQ3_XXS-1x3090/speed-1k-1.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-IQ3_XXS-1x3090/speed-1k-2.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-IQ3_XXS-1x3090/speed-1k-3.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-IQ3_XXS-1x3090/speed-262k-1.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-IQ3_XXS-1x3090/speed-262k-2.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-IQ3_XXS-1x3090/speed-262k-3.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-IQ3_XXS-1x3090/speed-32k-1.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-IQ3_XXS-1x3090/speed-32k-2.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-IQ3_XXS-1x3090/speed-32k-3.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-IQ3_XXS-1x3090/speed-4k-1.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-IQ3_XXS-1x3090/speed-4k-2.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-IQ3_XXS-1x3090/speed-4k-3.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-IQ3_XXS-2x3090-auto-needle/needle-16k-1.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-IQ3_XXS-2x3090-auto-needle/needle-28k-1.json

@@ -0,0 +1,722 @@
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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-IQ3_XXS-2x3090-auto-needle/needle-8k-1.json

@@ -0,0 +1,352 @@
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+}

bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-IQ3_XXS-2x3090-auto-two-turn/two-turn-16k-1.json

@@ -0,0 +1,755 @@
+{
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+  "strata serve: decode expert cache hit rate: 97.9% (24433 hits / 24957 lookups)"
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+ "answer_head": "I'll start by exploring the repository structure to understand the context better.",
+ "turn2_ttft_s": 0.577,
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+ "turn2_log_lines": [
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+  "strata serve: decode expert cache hit rate: 98.6% (31236 hits / 31679 lookups)"
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+ "metrics_after": {
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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-IQ3_XXS-2x3090-auto/speed-32k-1.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-IQ3_XXS-2x3090-auto/speed-32k-2.json

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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-IQ3_XXS-2x3090-auto/speed-32k-3.json

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+    0.0,
+    1.02,
+    5.65,
+    4.98,
+    3.42,
+    0.54,
+    0.0,
+    0.01,
+    0.02,
+    0.0,
+    0.02,
+    0.03,
+    0.0,
+    0.03,
+    0.02,
+    0.0,
+    0.01,
+    0.0,
+    0.01,
+    0.02,
+    0.0,
+    0.0,
+    12.64,
+    0.0,
+    71.65,
+    32.97,
+    0.0,
+    0.0,
+    0.0,
+    0.99,
+    5.21,
+    4.66,
+    2.02,
+    0.04,
+    0.0,
+    0.02,
+    0.04,
+    0.0,
+    0.04,
+    0.0,
+    0.05,
+    0.05,
+    0.0,
+    0.05,
+    0.04,
+    0.0,
+    0.04,
+    0.04,
+    0.0,
+    0.02,
+    0.03,
+    0.0,
+    0.01,
+    0.0,
+    0.16,
+    0.0,
+    0.0,
+    0.0,
+    3.86,
+    2.93
+   ],
+   "tok_s": [
+    0.0,
+    48.0,
+    86.94,
+    102.06,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    56.0,
+    103.48,
+    102.58,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    114.9,
+    82.33
+   ]
+  },
+  "time": 1790718083.3185816
+ }
+}

bench/results/2026-09-29-rtx3090-epyc-milan/data/strata-bench.py

@@ -0,0 +1,304 @@
+#!/usr/bin/env python3
+"""Strata benchmark harness (contribution plan, PR 1 step 1.2).
+
+Follows the upstream method (bench/results/2026-09-28-speed-0114): one code-agent prompt per
+length, greedy, 256 generated tokens, MTP as configured by setup. Every run gets a unique
+prefix, so conversation checkpoints cannot skip the prompt. Numbers come from the engine log
+lines the owner asks for in issue #74. /metrics and API timing are stored as cross-checks.
+
+Stdlib only. Examples:
+    ./strata-bench.py --label IQ3_XXS-1x3090 --log ./strata-iq3_xxs.log
+  ./strata-bench.py --label Coder-2x3090-auto --contexts 16k,28k --mode needle
+  ./strata-bench.py --url http://127.0.0.1:9292 --model flash-next-strata --label via-llama-swap
+"""
+
+import argparse
+import json
+import os
+import re
+import statistics
+import sys
+import time
+import urllib.error
+import urllib.request
+import uuid
+from datetime import datetime
+from pathlib import Path
+
+NOMINAL = {"1k": 1024, "2.7k": 2765, "4k": 4096, "8k": 8192, "16k": 16384, "28k": 28672,
+           "32k": 32768, "64k": 65536, "128k": 131072, "262k": 262144}
+CODE_EXT = {".py", ".cpp", ".cc", ".h", ".hpp", ".cu", ".cuh", ".js", ".md", ".txt", ".cmake"}
+SKIP_DIRS = {".git", ".venv", "build", "engine", "third_party", "node_modules", "models",
+             "packs", "mtp", "bench", "data", "__pycache__", "Strata-data"}
+LOG_PATTERNS = [
+    re.compile(r"decode expert cache hit rate", re.I),
+    re.compile(r"\bPCIe\b"),
+    re.compile(r"prompt .*tok/s.*generated .*tok/s", re.I),
+    re.compile(r"reus|checkpoint", re.I),
+    re.compile(r"layer split", re.I),
+]
+TOKS_PER_S = re.compile(r"([\d][\d,]*(?:\.\d+)?)\s*tok(?:ens)?/s", re.I)
+
+
+def load_corpus(dirs):
+    parts = []
+    for d in dirs:
+        root = Path(d)
+        for p in sorted(root.rglob("*")):
+            if p.suffix.lower() not in CODE_EXT or not p.is_file():
+                continue
+            if any(s in SKIP_DIRS for s in p.relative_to(root).parts[:-1]):
+                continue
+            text = p.read_text(encoding="utf-8", errors="ignore")
+            if text.strip():
+                parts.append(f"### {p.relative_to(root).as_posix()}\n```\n{text}\n```\n")
+    if not parts:
+        sys.exit(f"no source files found in {dirs}")
+    return parts
+
+
+def build_text(parts, n_chars):
+    out, size, i, repeated = [], 0, 0, False
+    while size < n_chars:
+        if i and i % len(parts) == 0:
+            repeated = True
+        chunk = parts[i % len(parts)]
+        out.append(chunk)
+        size += len(chunk)
+        i += 1
+    return "".join(out)[:n_chars], repeated
+
+
+def post_stream(url, payload, api_key, timeout):
+    req = urllib.request.Request(
+        f"{url}/v1/chat/completions", data=json.dumps(payload).encode(),
+        headers={"Content-Type": "application/json", "Authorization": f"Bearer {api_key or 'none'}"})
+    t0 = time.perf_counter()
+    t_first, text, reasoning, usage, chunks = None, [], [], {}, 0
+    with urllib.request.urlopen(req, timeout=timeout) as resp:
+        for raw in resp:
+            line = raw.decode("utf-8", errors="ignore").strip()
+            if not line.startswith("data:"):
+                continue
+            data = line[5:].strip()
+            if data == "[DONE]":
+                break
+            try:
+                chunk = json.loads(data)
+            except json.JSONDecodeError:
+                continue
+            if chunk.get("usage"):
+                usage = chunk["usage"]
+            for ch in chunk.get("choices") or []:
+                delta = ch.get("delta") or {}
+                piece = delta.get("content") or ""
+                rpiece = delta.get("reasoning_content") or ""
+                if (piece or rpiece) and t_first is None:
+                    t_first = time.perf_counter()
+                if piece or rpiece:
+                    chunks += 1
+                text.append(piece)
+                reasoning.append(rpiece)
+    t_end = time.perf_counter()
+    return {"t_start": t0, "t_first": t_first or t_end, "t_end": t_end, "text": "".join(text),
+            "reasoning": "".join(reasoning), "usage": usage, "chunks": chunks}
+
+
+def get_json(url, path, api_key, timeout=10):
+    try:
+        req = urllib.request.Request(f"{url}{path}", headers={"Authorization": f"Bearer {api_key or 'none'}"})
+        with urllib.request.urlopen(req, timeout=timeout) as r:
+            body = r.read().decode("utf-8", errors="ignore")
+        try:
+            return json.loads(body)
+        except json.JSONDecodeError:
+            return body
+    except (urllib.error.URLError, TimeoutError) as e:
+        return {"error": str(e)}
+
+
+def wait_healthy(url, api_key, timeout):
+    deadline = time.time() + timeout
+    while time.time() < deadline:
+        h = get_json(url, "/health", api_key, timeout=5)
+        if not (isinstance(h, dict) and "error" in h):
+            return True
+        time.sleep(5)
+    return False
+
+
+def log_offset(log):
+    return log.stat().st_size if log and log.exists() else 0
+
+
+def log_lines_since(log, offset):
+    if not log or not log.exists():
+        return []
+    time.sleep(0.5)
+    with log.open("r", encoding="utf-8", errors="ignore") as f:
+        f.seek(offset)
+        new = f.read().splitlines()
+    return [ln for ln in new if any(p.search(ln) for p in LOG_PATTERNS)]
+
+
+def engine_numbers(lines):
+    """Best effort: the last 'prompt ... tok/s ... generated ... tok/s' line -> (prompt, decode)."""
+    for ln in reversed(lines):
+        if LOG_PATTERNS[2].search(ln):
+            vals = [float(v.replace(",", "")) for v in TOKS_PER_S.findall(ln)]
+            if len(vals) >= 2:
+                return vals[0], vals[1]
+    return None, None
+
+
+def target_tokens(key, max_tokens):
+    n = NOMINAL[key]
+    # leave room for the answer, like upstream's 259,943-token "262K" prompt
+    return n - max_tokens - 1024 if n >= 32768 else n
+
+
+def make_messages(mode, parts, n_chars, run_id):
+    body, repeated = build_text(parts, n_chars)
+    system = f"[bench run {run_id}] You are a coding agent working in the repository shown below."
+    if mode == "needle":
+        word = uuid.uuid4().hex[:8].upper()
+        mid = len(body) // 2
+        body = f"{body[:mid]}\n# NOTE: the secret code word is {word}.\n{body[mid:]}"
+        task = "What is the secret code word mentioned in the files above? Answer with the word only."
+    else:
+        word = None
+        task = ("Task: explain what the last file above does, then propose one concrete improvement "
+                "as a unified diff.")
+    msgs = [{"role": "system", "content": system},
+            {"role": "user", "content": f"Repository files:\n\n{body}\n\n{task}"}]
+    return msgs, word, repeated
+
+
+def run_once(a, parts, key, run, cpt):
+    tgt = target_tokens(key, a.max_tokens)
+    run_id = uuid.uuid4().hex[:12]
+    msgs, word, repeated = make_messages(a.mode, parts, int(tgt * cpt), run_id)
+    payload = {"model": a.model, "messages": msgs, "max_tokens": a.max_tokens, "stream": True,
+               "stream_options": {"include_usage": True}, "temperature": 0,
+               "reasoning_effort": a.reasoning_effort}
+    off = log_offset(a.log)
+    rec = {"label": a.label, "mode": a.mode, "ctx": key, "target_tokens": tgt, "run": run,
+           "run_id": run_id, "corpus_repeated": repeated, "time": datetime.now().isoformat(timespec="seconds")}
+    try:
+        r = post_stream(a.url, payload, a.api_key, a.timeout)
+    except Exception as e:  # noqa: BLE001 - record and continue with the next cell
+        rec["error"] = str(e)[:300]
+        return rec, cpt
+    pt = r["usage"].get("prompt_tokens")
+    ct = r["usage"].get("completion_tokens") or r["chunks"]
+    ttft = r["t_first"] - r["t_start"]
+    dec_t = r["t_end"] - r["t_first"]
+    lines = log_lines_since(a.log, off)
+    e_prompt, e_decode = engine_numbers(lines)
+    rec.update({
+        "prompt_tokens": pt, "completion_tokens": ct,
+        "ttft_s": round(ttft, 3), "e2e_s": round(r["t_end"] - r["t_start"], 3),
+        "api_prompt_tps": round(pt / ttft, 1) if pt and ttft > 0 else None,
+        "api_decode_tps": round((ct - 1) / dec_t, 1) if ct and ct > 1 and dec_t > 0 else None,
+        "engine_prompt_tps": e_prompt, "engine_decode_tps": e_decode,
+        "log_lines": lines, "answer_head": r["text"][:300],
+    })
+    if word:
+        rec["needle_found"] = word in r["text"].upper() or word in r["reasoning"].upper()
+    if a.mode == "two-turn":
+        follow = msgs + [{"role": "assistant", "content": r["text"]},
+                         {"role": "user", "content": "Summarize your answer in one sentence."}]
+        off2 = log_offset(a.log)
+        r2 = post_stream(a.url, {**payload, "messages": follow, "max_tokens": 64}, a.api_key, a.timeout)
+        rec["turn2_ttft_s"] = round(r2["t_first"] - r2["t_start"], 3)
+        rec["turn2_prompt_tokens"] = r2["usage"].get("prompt_tokens")
+        rec["turn2_log_lines"] = log_lines_since(a.log, off2)
+    if pt:
+        cpt = (int(tgt * cpt) + 400) / pt  # refine chars per token for the next cells
+    return rec, cpt
+
+
+def write_md(path, recs, label):
+    def med(vals):
+        vals = [v for v in vals if v is not None]
+        return round(statistics.median(vals), 1) if vals else None
+
+    rows = ["| ctx | prompt tokens | runs | TTFT s (med) | prompt tok/s engine / API (med) "
+            "| decode tok/s engine / API (run 1) | decode tok/s engine / API (med) | needle |",
+            "|---|---|---|---|---|---|---|---|"]
+    for key in dict.fromkeys(r["ctx"] for r in recs):
+        rs = [r for r in recs if r["ctx"] == key and "error" not in r]
+        errs = sum(1 for r in recs if r["ctx"] == key and "error" in r)
+        if not rs:
+            rows.append(f"| {key} | - | 0 ({errs} errors) | | | | | |")
+            continue
+        first = rs[0]
+        needle = "" if rs[0].get("needle_found") is None else f"{sum(r['needle_found'] for r in rs)}/{len(rs)}"
+        rows.append(
+            f"| {key} | {rs[0]['prompt_tokens']} | {len(rs)}{f' ({errs} err)' if errs else ''} "
+            f"| {med(r['ttft_s'] for r in rs)} "
+            f"| {med(r['engine_prompt_tps'] for r in rs)} / {med(r['api_prompt_tps'] for r in rs)} "
+            f"| {first['engine_decode_tps']} / {first['api_decode_tps']} "
+            f"| {med(r['engine_decode_tps'] for r in rs)} / {med(r['api_decode_tps'] for r in rs)} | {needle} |")
+    path.write_text(f"# {label}\n\n" + "\n".join(rows) + "\n", encoding="utf-8")
+
+
+def main():
+    ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    ap.add_argument("--url", default="http://127.0.0.1:8090")
+    ap.add_argument("--model", default="strata", help="model name sent (llama-swap: the model id)")
+    ap.add_argument("--api-key", default=os.environ.get("STRATA_API_KEY", ""))
+    ap.add_argument("--label", required=True, help="e.g. IQ3_XXS-1x3090-calibrated")
+    ap.add_argument("--contexts", default="1k,4k,32k,128k", help=f"comma list of {','.join(NOMINAL)}")
+    ap.add_argument("--repeats", type=int, default=3)
+    ap.add_argument("--max-tokens", type=int, default=256)
+    ap.add_argument("--reasoning-effort", default="none")
+    ap.add_argument("--mode", choices=["speed", "needle", "two-turn"], default="speed")
+    ap.add_argument("--source-dir", action="append", default=None,
+                    help="code corpus for the prompts (default: current directory, the Strata repo)")
+    ap.add_argument("--log", type=Path, default=None,
+                    help="engine log, e.g. ./strata-iq3_xxs.log")
+    ap.add_argument("--out", type=Path, default=Path(f"./benchmark-output/{datetime.now():%Y-%m-%d}"))
+    ap.add_argument("--timeout", type=float, default=1800, help="per request, seconds (262K prefill takes minutes)")
+    ap.add_argument("--chars-per-token", type=float, default=3.4, help="start value, refined after each run")
+    a = ap.parse_args()
+
+    keys = [k.strip().lower() for k in a.contexts.split(",") if k.strip()]
+    bad = [k for k in keys if k not in NOMINAL]
+    if bad:
+        sys.exit(f"unknown context(s): {bad}")
+    parts = load_corpus(a.source_dir or ["."])
+    if a.log and not a.log.exists():
+        print(f"warning: log {a.log} not found; engine numbers will be empty", file=sys.stderr)
+    if not wait_healthy(a.url, a.api_key, 900):
+        sys.exit(f"{a.url}/health not reachable")
+
+    out = a.out / a.label
+    (out / "data").mkdir(parents=True, exist_ok=True)
+    recs, cpt = [], a.chars_per_token
+    for key in keys:
+        for run in range(1, a.repeats + 1):
+            rec, cpt = run_once(a, parts, key, run, cpt)
+            rec["metrics_after"] = get_json(a.url, "/metrics", a.api_key)
+            recs.append(rec)
+            (out / "data" / f"{a.mode}-{key}-{run}.json").write_text(json.dumps(rec, indent=1), encoding="utf-8")
+            status = rec.get("error") or (f"prompt {rec['prompt_tokens']} tok, TTFT {rec['ttft_s']} s, "
+                                          f"decode engine {rec['engine_decode_tps']} / API {rec['api_decode_tps']} tok/s")
+            print(f"[{a.label}] {a.mode} {key} run {run}: {status}", flush=True)
+
+    matrix_path = out / "matrix.json"
+    merged = {}
+    if matrix_path.exists():
+        for record in json.loads(matrix_path.read_text(encoding="utf-8")):
+            merged[(record["mode"], record["ctx"], record["run"])] = record
+    for record in recs:
+        key = (record["mode"], record["ctx"], record["run"])
+        merged[key] = {k: v for k, v in record.items() if k != "metrics_after"}
+    combined = list(merged.values())
+    matrix_path.write_text(json.dumps(combined, indent=1), encoding="utf-8")
+    write_md(out / "matrix.md", combined, f"{a.label} ({a.mode})")
+    print(f"written: {out}/matrix.md, matrix.json, data/")
+
+
+if __name__ == "__main__":
+    main()

bench/results/2026-09-29-rtx3090-epyc-milan/hw.txt

@@ -0,0 +1,86 @@
+
+## date (UTC)
+2026-09-29T22:07:24Z
+
+## strata version
+v0.1.26
+
+## os
+PRETTY_NAME="Ubuntu 24.04.5 LTS"
+VERSION_ID="24.04"
+6.8.0-139-generic
+
+## virtualization
+kvm
+
+## cpu
+CPU(s):                                  20
+Model name:                              AMD EPYC 7453 28-Core Processor
+Thread(s) per core:                      1
+Core(s) per socket:                      20
+Socket(s):                               1
+L3 cache:                                320 MiB (20 instances)
+NUMA node(s):                            1
+
+## cpu flags (relevant)
+avx2 f16c fma
+
+## numa
+available: 1 nodes (0)
+node 0 cpus: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
+node 0 size: 168968 MB
+node 0 free: 47460 MB
+node distances:
+node   0
+  0:  10
+
+## memory
+               total        used        free      shared  buff/cache   available
+Mem:             165          14          46           0         105         150
+Swap:              0           0           0
+
+## dimms
+run as root for dmidecode
+(in a VM these are virtual DIMMs: fill in the host section below by hand)
+
+## memlock limit
+21627928
+
+## gpu
+index, name, driver_version, memory.total [MiB], pcie.link.gen.current, pcie.link.gen.max, pcie.link.width.current, pcie.link.width.max, power.limit [W], power.default_limit [W], power.max_limit [W], power.draw [W], clocks.max.sm [MHz]
+0, NVIDIA GeForce RTX 3090, 595.91.07, 24576 MiB, 1, 4, 16, 16, 280.00 W, 350.00 W, 366.00 W, 35.23 W, 2130 MHz
+1, NVIDIA GeForce RTX 3090, 595.91.07, 24576 MiB, 1, 4, 16, 16, 280.00 W, 420.00 W, 450.00 W, 28.60 W, 2100 MHz
+
+## gpu topology
+	GPU0	GPU1	CPU Affinity	NUMA Affinity	GPU NUMA ID
+GPU0	 X 	PHB	0-19	0		N/A
+GPU1	PHB	 X 	0-19	0		N/A
+
+Legend:
+
+  X    = Self
+  SYS  = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
+  NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
+  PHB  = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
+  PXB  = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
+  PIX  = Connection traversing at most a single PCIe bridge
+  NV#  = Connection traversing a bonded set of # NVLinks
+
+## gpu processes (must be empty before benchmarking)
+pid, process_name, used_gpu_memory [MiB]
+
+## docker
+Docker version 29.8.1, build 4a63305
+NVIDIA Container Toolkit CLI version 1.18.1
+
+## disk (model data)
+Filesystem      Size  Used Avail Use% Mounted on
+/dev/sdb1       541G  409G  106G  80% <storage>
+
+## host (fill in manually)
+- Hypervisor: Proxmox VE (KVM guest; host version not visible inside the VM)
+- CPU: AMD EPYC 7453 28-Core Processor (Milan); 20 vCPU exposed, AVX2, no AVX-512 visible
+- RAM: 165 GiB visible to the guest; physical DIMM layout and bandwidth not measured
+- VM: 20 vCPU, 1 socket x 20 cores x 1 thread, 1 NUMA node; ballooning and pinning not verified
+- GPUs: 2x RTX 3090 24 GB; PCIe Gen4 x16 maximum reported for each (current idle link generation varies); configured power limit 280 W each
+- Storage: ext4 guest data disk, 541 GiB; host storage layout not verified from the guest

bench/results/2026-09-29-rtx3090-epyc-milan/matrix.md

@@ -0,0 +1,48 @@
+# Native benchmark matrix (v0.1.26)
+
+Prompt speeds are engine tok/s medians; output lists the first-run value and the three-run median. `prompt tokens` is the median measured count. Draft acceptance is the mean of per-run accepted/offered values from engine logs.
+
+## IQ3_XXS, one RTX 3090
+
+| Context | Prompt tokens | Prompt tok/s | Decode first / median tok/s | TTFT median | Draft acceptance |
+|---|---:|---:|---:|---:|---:|
+| 1K | 1,135 | 869.7 | 93.0 / 93.0 | 1.33 s | 0.738 |
+| 4K | 4,200 | 1,772.2 | 91.7 / 89.2 | 2.41 s | 0.763 |
+| 32K | 31,602 | 2,160.4 | 90.6 / 90.6 | 14.83 s | 0.739 |
+| 128K | 129,913 | 2,173.6 | 79.3 / 79.3 | 60.53 s | 0.723 |
+| 262K | 259,589 | 1,725.6 | 63.0 / 67.3 | 152.10 s | 0.697 |
+
+## 32K model and GPU comparison
+
+| Model | GPUs / split | Prompt tokens | Prompt tok/s median | Decode first / median tok/s | TTFT median | Draft acceptance |
+|---|---|---:|---:|---:|---:|---:|
+| Coder IQ1_M | 1x 3090 | 31,601 | 2,502.4 | 52.7 / 66.8 | 12.80 s | 0.760 |
+| Coder IQ1_M | 2x 3090, auto K=19 | 31,601 | 2,704.3 | 88.5 / 98.6 | 11.85 s | 0.908 |
+| IQ3_XXS | 2x 3090, auto K=24 | 31,603 | 1,158.9 | 83.7 / 96.7 | 26.90 s | 0.690 |
+| IQ3_S | 2x 3090, auto K=24 | 31,604 | 749.3 | 83.1 / 106.3 | 42.27 s | 0.871 |
+
+## Coder IQ1_M fixed layer split
+
+One initial 32K run per K, then three-run repeats for K=22–30. Prompts are independent and adaptive-cache behavior varies; compare the medians cautiously.
+
+| Split | Successful runs | Prompt tok/s median | Decode tok/s median | Notes |
+|---|---:|---:|---:|---|
+| Auto (K=19) | 3 | 2,704.3 | 98.6 | Layers 0–18 / 19–47 |
+| K=18 | 1 | 2,324.6 | 83.1 | Repeat startup OOM while uploading a dense weight |
+| K=22 | 3 | 2,951.6 | 103.3 | Layers 0–21 / 22–47 |
+| K=24 | 3 | 3,123.2 | 100.2 | Layers 0–23 / 24–47 |
+| K=26 | 3 | 3,205.2 | 93.5 | Layers 0–25 / 26–47 |
+| K=28 | 3 | 3,034.9 | 96.6 | Layers 0–27 / 28–47 |
+| K=30 | 3 | 2,920.6 | 96.1 | Layers 0–29 / 30–47 |
+
+## Correctness
+
+| Model / setup | Check | Result |
+|---|---|---|
+| IQ3_XXS, 1x 3090 | Needles 8K / 16K / 28K | 3/3 found |
+| IQ3_XXS, 1x 3090 | 16K two-turn chat | 15,892 / 15,915 prompt tokens reused on turn two |
+| IQ3_XXS, 2x 3090 auto K=24 | Needles 8K / 16K / 28K | 3/3 found |
+| IQ3_XXS, 2x 3090 auto K=24 | 16K two-turn chat | 15,628 / 15,672 prompt tokens reused on turn two |
+| IQ3_S, 2x 3090 auto K=24 | Needles 8K / 16K / 28K | 3/3 found |
+| IQ3_S, 2x 3090 auto K=24 | 16K two-turn chat | 15,632 / 15,676 prompt tokens reused on turn two |
+| Coder, 2x 3090 | Needles / two-turn | Not run |

bench/results/2026-09-30-community-rtx-5090/BUILD.json

@@ -0,0 +1,14 @@
+{
+ "source": "local",
+ "version": "0.1.29",
+ "archs": [
+  120
+ ],
+ "vision": "gpu",
+ "cuda_dirs": [
+  "/usr/local/cuda/bin",
+  "/usr/local/cuda/lib64"
+ ],
+ "src": "1437bc9455f7c0af",
+ "vision_src": "4b80385716918cce"
+}

bench/results/2026-09-30-community-rtx-5090/Dockerfile

@@ -0,0 +1,4 @@
+FROM nvidia/cuda@sha256:5dc1bca23d05bd37b011be68ec470c03b403a5da07ec3a86e41af9470e9d0cc6
+RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends build-essential cmake ninja-build git ca-certificates curl python3 python3-venv python3-dev pkg-config && rm -rf /var/lib/apt/lists/*
+ENV HOME=/workspace/home PYTHONUNBUFFERED=1
+WORKDIR /workspace/Strata

bench/results/2026-09-30-community-rtx-5090/README.md

@@ -0,0 +1,167 @@
+# Community benchmark: RTX 5090, Core Ultra 9 285K
+
+Measured on 2026-09-30 by [hagope](https://github.com/hagope), on a Linux
+machine called OMAN. This tests Strata 0.1.29 with the original Flash-Next
+IQ2_XS, one GPU, and a 131,072-token context limit.
+
+Median decode throughput was **179.4 tok/s at 4,096 prompt tokens, 175.7 tok/s
+at 32,768, and 165.0 tok/s at 128,000**. These are synthetic code-explanation
+requests with greedy decoding and a 256-token output cap. They do not establish
+general answer quality or performance on other workloads.
+
+## Hardware and software
+
+- NVIDIA GeForce RTX 5090; 32,607 MiB reported VRAM; 575 W power limit.
+  PCIe capability: Gen 5, x16. The engine's startup transfer probe reported
+  50.0 GB/s host-to-device. GPU clocks were not fixed for this test.
+- Intel Core Ultra 9 285K; 24 logical CPUs. The engine selected AVX2 and
+  23 expert-pool workers plus its host thread.
+- 64 GB installed RAM; Linux reported 62.19 GiB usable. Local NVMe storage:
+  WDC WDS200T2B0C-00PXH0. 8 GiB system swap.
+- Ubuntu 24.04.4 LTS, kernel 6.8.0-142-generic, NVIDIA driver 595.84.
+- Source commit `d6708a4aae15b4860000d54c8af9e84d684bce09`; engine 0.1.29.
+  Locally compiled CUDA architecture 120, including the GPU vision helper.
+  See [BUILD.json](BUILD.json).
+- CUDA 13.0.2 container; NVCC 13.0.88; GCC 13.3.0; Python 3.12.3.
+  Base image: `nvidia/cuda@sha256:5dc1bca23d05bd37b011be68ec470c03b403a5da07ec3a86e41af9470e9d0cc6`.
+  Installed Python dependencies are listed in [python-packages.txt](python-packages.txt).
+- The regular inference server, GPU TTS, and GPU embedding service were paused.
+  Other CPU services remained running. The GPU was dedicated to Strata and its
+  vision helper; this was not a completely isolated operating system.
+
+## Model and configuration
+
+Model: `ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF`, revision
+`ed59f92082b1e93c0e96d60a8b11aab089b52f09`:
+
+- `IQ2_XS/Qwen3.8-Flash-Next-GSQ-RCO-IQ2_XS-00001-of-00002.gguf`
+- `IQ2_XS/Qwen3.8-Flash-Next-GSQ-RCO-IQ2_XS-00002-of-00002.gguf`
+- `mmproj-Qwen3.8-Flash-Next-BF16.gguf`
+
+All three local GGUF SHA-256 hashes matched the revision's published LFS hashes.
+[model-provenance.json](model-provenance.json) contains filenames, byte sizes,
+revisions, and verified hashes. MTP was fetched by the installer from
+`Qwen/Qwen3.8-Flash-Next`; its API reported revision
+`de4b8e4d43b917e7706784d8bb445c9af86a3540` at verification time. The installer
+uses `main` for those tensor-range downloads. Their individual output hashes
+are retained in [mtp-manifest.json](mtp-manifest.json); the full BF16 upstream
+shards were not downloaded or independently hash-verified.
+
+The unmodified installer prepared the native IQ2_XS pack and Q2_0 MTP experts.
+[artifact-hashes.json](artifact-hashes.json) identifies the executables,
+default expert profile, tokenizer, pack metadata/dense weights, and draft pack.
+
+- Context 131,072; INT8 KV; 32,768 KV cells per attention layer resident on GPU.
+- Expert cache `auto`: 17,463 slots, 23.44 GiB VRAM, profile-prefilled with
+  **no eviction**. The bundled expert profile was used without calibration.
+- Prefill `auto` selected chunks of 8,192 tokens, borrowing 3,421 expert-cache
+  slots during prompt processing.
+- MTP `--spec 4 --spec-min-p 0.5`; built-in suffix drafting remained enabled.
+- Low-RAM mode off; GPU vision enabled, 1,024 image tokens and a 700 MiB VRAM
+  reserve. The vision helper warmed up; benchmark requests contained no images.
+- Experimental speed projection off; no custom control vectors or calibration.
+- Reasoning disabled, temperature 0, maximum 256 generated tokens per speed run.
+
+The complete measured configuration is [strata-iq2_xs.json](strata-iq2_xs.json).
+Startup choices and every request's timing/draft statistics are in
+[engine.log](engine.log).
+
+## Method and reproduction
+
+Use the attached [Dockerfile](Dockerfile), the pinned source commit, and an empty
+data directory. Mount a working directory as `/workspace`, with the checkout
+at `/workspace/Strata`. Run setup inside the CUDA container with GPU access,
+host IPC, and unlimited memlock:
+
+```bash
+bash ./setup.sh --family qwen --model IQ2_XS --context 131072 --kv int8 \
+  --vision gpu --experimental-speed-projection off --gpu 0 --low-ram off \
+  --build --yes --no-start --port 18080 --data-dir /workspace/Strata-data
+```
+
+Launch the prepared server on loopback inside a container using host networking:
+
+```bash
+/workspace/Strata/.venv/bin/python serve/server.py --engine strata \
+  --config strata-iq2_xs.json --host 127.0.0.1 --port 18080
+```
+
+Run [benchmark.py](benchmark.py) inside that container, after placing the script
+at `/workspace/benchmark.py`. It generates deterministic synthetic Python
+functions, adds a different nonce near the start of each request, and counts the
+complete rendered chat prompt using Strata's tokenizer. It adjusts the filler
+to the target token count and verifies the count against the server afterward.
+The resulting request hashes are in [results.json](results.json).
+
+```bash
+/workspace/Strata/.venv/bin/python /workspace/benchmark.py
+/workspace/Strata/.venv/bin/python tools/needle_bench.py \
+  --url http://127.0.0.1:18080 --lengths 32k,128k --depths 10,50,90 \
+  --out /workspace/results/needles.json
+```
+
+One short warm-up request was excluded. Three runs at each length were executed
+serially in increasing-length order on the same loaded engine. All nine speed
+requests processed their entire prompt: **zero reused tokens**. The GPU expert
+cache was filled at startup and retained between requests; no engine restart or
+OS page-cache reset separated them. Downloads and file verification had already
+warmed the filesystem cache. Loading time is excluded.
+
+The script measures streaming TTFT from immediately before the HTTP request
+until the first nonempty text delta, ignoring empty role chunks and keep-alives.
+With reasoning off, that delta contains answer text. Total latency ends at
+stream completion. Both timings include HTTP and frontend tokenization on OMAN
+over loopback. Engine prompt throughput uses freshly read tokens divided by
+`prompt_ms`; decode throughput uses `engine_generated / decode_ms`. The JSON
+preserves the engine timing values and output text for each run.
+
+## Results
+
+Each cell below is the median **[minimum–maximum]** of three runs. Every request
+generated 256 tokens and stopped at the output limit, with no failed or cancelled
+speed requests.
+
+| Prompt tokens | Reused | Prompt tok/s | Decode tok/s | TTFT seconds | Total seconds |
+| ---: | ---: | ---: | ---: | ---: | ---: |
+| 4,096 | 0 | 4,269.8 [3,510.2–4,273.3] | 179.4 [161.3–184.8] | 0.974 [0.973–1.183] | 2.392 [2.351–2.761] |
+| 32,768 | 0 | 5,543.2 [5,527.1–5,549.6] | 175.7 [166.1–181.8] | 5.950 [5.942–5.968] | 7.390 [7.348–7.498] |
+| 128,000 | 0 | 5,778.7 [5,767.0–5,791.9] | 165.0 [154.8–169.3] | 22.262 [22.212–22.308] | 23.850 [23.765–23.856] |
+
+Raw per-run records: [results.json](results.json). Calculated aggregates:
+[summary.json](summary.json). The lower first 4K run is retained in the range.
+Decode expert-cache hit rates were 97.8–99.7% for these nine requests.
+
+[monitor.py](monitor.py) sampled the host once per second, starting before model
+load and ending after the recall checks. [telemetry.jsonl](telemetry.jsonl)
+contains 250 samples over 255.9 seconds, with the following observed peaks:
+
+- GPU memory: **31,785 MiB (31.04 GiB)**, system-wide on the selected GPU.
+- Host RAM: **45.54 GiB**, calculated as `MemTotal - MemAvailable`. This includes
+  Strata, other services, and non-reclaimable system memory; it is not process RSS.
+- Swap allocation increased from **0.25 MiB to 425.50 MiB**, including increases
+  during the speed suite. Swap I/O rates were not measured, so this is not a
+  claim that inference ran without paging. No out-of-memory error occurred.
+
+Sampling and the server's own telemetry were active during inference. These
+are sampled peaks; brief allocation spikes between samples may be missed.
+Aggregates are also recorded in [memory-summary.json](memory-summary.json).
+
+## Recall and limitations
+
+The repository's unchanged `tools/needle_bench.py` found **all six needles**:
+depths 10%, 50%, and 90% at both requested lengths. Actual prompt lengths were
+30,804–30,805 tokens for `32k` and 126,318–126,320 for `128k`. All answers exactly
+matched the expected code words. See [needles.json](needles.json).
+
+The final 128K recall case reused 16,384 prompt tokens; the other five reused
+zero. Recall latency is therefore not used in the fresh-prompt speed table.
+The script chooses needle depth by character position, not token position.
+
+This is one machine, one quantization, one configuration, and a small synthetic
+workload. Long output, sampled decoding, thinking, coding-task correctness,
+vision, tool use, multi-request concurrency, and a sustained thermal run were
+not evaluated. The built-in needle check measures recall on these six inputs,
+not general model accuracy. No same-workload baseline on another engine or GPU
+was run; do not interpret the README's different-hardware numbers as a controlled
+comparison. The 128,000-token speed prompt also does not fill every token of the
+131,072-token context window.

bench/results/2026-09-30-community-rtx-5090/artifact-hashes.json

@@ -0,0 +1,82 @@
+[
+  {
+    "file": "Strata-data/mtp/mtp-q2_0.gguf",
+    "bytes": 889017216,
+    "sha256": "a22206a4d74eafbf31063a84610292535fb3b49af9a339963817fd04aed81e4d"
+  },
+  {
+    "file": "Strata-data/mtp/rt/dense.bin",
+    "bytes": 116099072,
+    "sha256": "c724dc0b0822ada5d2977bf5bde821605feabaa64ea2e0045b67ca656329070a"
+  },
+  {
+    "file": "Strata-data/mtp/rt/dense.txt",
+    "bytes": 1851,
+    "sha256": "991456cf4eb266eb8a888c349e548d11bc640f5dbd9149439aa9786af0f732fd"
+  },
+  {
+    "file": "Strata-data/mtp/rt/draft_vocab.bin",
+    "bytes": 425196,
+    "sha256": "b1e1d3a7a9e4bf862dcd5923ce661fb59bbd07907e594df5cf86a62ac235cb91"
+  },
+  {
+    "file": "Strata-data/mtp/rt/experts.bin",
+    "bytes": 707788800,
+    "sha256": "09398406be61f1f54c93861f449e48b8df0bfccbc9ec9b2b7636775a6ea9244f"
+  },
+  {
+    "file": "Strata-data/packs/iq2_xs/dense.bin",
+    "bytes": 1538035200,
+    "sha256": "d2af3d8331e5c7a1985d1d589747230f9d56719a6d33c7cddc9ee6b81cde6b13"
+  },
+  {
+    "file": "Strata-data/packs/iq2_xs/index.txt",
+    "bytes": 93686,
+    "sha256": "19e781af360ca3b83fd90fe09e757ac9aa2a9dcada26810ddc9e933bdb112091"
+  },
+  {
+    "file": "Strata-data/packs/iq2_xs/native_experts.txt",
+    "bytes": 3289,
+    "sha256": "48df605acb89c7e8c3176a51cec777c2ed344ea439e8f3a37e5ce4f9dcb6946c"
+  },
+  {
+    "file": "Strata-data/packs/iq2_xs/tokenizer/chat_template.jinja",
+    "bytes": 9993,
+    "sha256": "12827f24b742ea4e80cdc12dbcf9622227056b9f797252a3149263d4f9aaadce"
+  },
+  {
+    "file": "Strata-data/packs/iq2_xs/tokenizer/merges.txt",
+    "bytes": 3353258,
+    "sha256": "4ba698529766c7a2f5052083ea10cdf2485577f5237a332ee291054eb11e3332"
+  },
+  {
+    "file": "Strata-data/packs/iq2_xs/tokenizer/token_type.json",
+    "bytes": 744960,
+    "sha256": "5088c8c298fc06af8382ddb3b76c888703ac2634e82263b97eedcc2ad202738b"
+  },
+  {
+    "file": "Strata-data/packs/iq2_xs/tokenizer/tokenizer.json",
+    "bytes": 541,
+    "sha256": "c0bd435efbc6c3b28964b47999afe16c4ad268c7c7c4e8cb3af410796a6f30bf"
+  },
+  {
+    "file": "Strata-data/packs/iq2_xs/tokenizer/vocab.json",
+    "bytes": 5737005,
+    "sha256": "4ba64f0332abcfb0b600b7df1537d1e836e68009d5fb7cdb77339738dc6365c4"
+  },
+  {
+    "file": "Strata/data/expert-profile.bin",
+    "bytes": 196632,
+    "sha256": "8f59b4aa8873209dff11c11e37bcda9529a1335b724a1afeea37bf6388975baf"
+  },
+  {
+    "file": "Strata/engine/strata",
+    "bytes": 43158256,
+    "sha256": "b7f18df1314ad438952ca11bb5ce5f23b41e7a572462426383d2ad8209fcf367"
+  },
+  {
+    "file": "Strata/engine/strata-vision",
+    "bytes": 74993888,
+    "sha256": "0d4c955f255ba4c3a9f6f456d63292754fae440a8a39cb13b9b6564938448e19"
+  }
+]

bench/results/2026-09-30-community-rtx-5090/benchmark.py

@@ -0,0 +1,135 @@
+#!/usr/bin/env python3
+"""Serial, fresh-prompt Strata benchmark. Run inside its prepared environment."""
+import argparse
+import hashlib
+import json
+import statistics
+import sys
+import time
+import urllib.request
+from pathlib import Path
+
+
+def get(url):
+    with urllib.request.urlopen(url, timeout=30) as response:
+        return json.load(response)
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--root", type=Path, default=Path("/workspace/Strata"))
+    parser.add_argument("--pack", type=Path, default=Path("/workspace/Strata-data/packs/iq2_xs"))
+    parser.add_argument("--url", default="http://127.0.0.1:18080")
+    parser.add_argument("--out", type=Path, default=Path("/workspace/results"))
+    parser.add_argument("--targets", default="4096,32768,128000")
+    parser.add_argument("--runs", type=int, default=3)
+    args = parser.parse_args()
+    args.out.mkdir(parents=True, exist_ok=True)
+    sys.path[:0] = [str(args.root), str(args.root / "tools")]
+    from strata_tokenizer import Tokenizer
+    from serve.frontend import ChatTemplate, openai_to_messages
+    directory = args.pack / "tokenizer"
+    vocab = json.loads((directory / "vocab.json").read_text())
+    tokens = [None] * len(vocab)
+    for token, number in vocab.items():
+        tokens[number] = token
+    tokenizer = Tokenizer(tokens, (directory / "merges.txt").read_text().splitlines(),
+                          json.loads((directory / "token_type.json").read_text()))
+    template = ChatTemplate(directory / "chat_template.jinja")
+    initial = get(args.url + "/v1/status")
+    (args.out / "initial-status.json").write_text(json.dumps(initial, indent=2) + "\n")
+    rows = []
+
+    def request(content, maximum=256):
+        return {"model": "strata", "messages": [{"role": "user", "content": content}],
+                "temperature": 0, "reasoning_effort": "none", "max_tokens": maximum,
+                "stream": True, "stream_options": {"include_usage": True}}
+
+    def count(req):
+        messages, tools, kwargs = openai_to_messages(req)
+        return len(tokenizer.encode(template.render(messages, tools, **kwargs), parse_special=True))
+
+    def perform(label, req, expected=None):
+        body = json.dumps(req).encode()
+        (args.out / (label + "-request.json")).write_bytes(body + b"\n")
+        print("start", label, "tokens", expected, flush=True)
+        chunks, texts, first, finish = [], [], None, None
+        started = time.perf_counter()
+        wire = urllib.request.Request(args.url + "/v1/chat/completions", data=body,
+                                      headers={"Content-Type": "application/json"})
+        with urllib.request.urlopen(wire, timeout=1800) as response:
+            for line in response:
+                if not line.startswith(b"data: "):
+                    continue
+                payload = line[6:].strip()
+                if payload == b"[DONE]":
+                    break
+                chunk = json.loads(payload)
+                chunks.append(chunk)
+                for choice in chunk.get("choices", []):
+                    delta = choice.get("delta", {})
+                    text = (delta.get("content") or "") + (delta.get("reasoning_content") or "")
+                    if text:
+                        first = first if first is not None else time.perf_counter() - started
+                        texts.append(text)
+                    finish = choice.get("finish_reason") or finish
+        elapsed = time.perf_counter() - started
+        metrics = get(args.url + "/metrics")
+        engine = metrics["requests"][0]
+        usage = next((chunk["usage"] for chunk in reversed(chunks) if "usage" in chunk), {})
+        row = {"label": label, "expected_prompt_tokens": expected,
+               "request_sha256": hashlib.sha256(body).hexdigest(),
+               "client_ttft_s": first, "client_elapsed_s": elapsed, "finish_reason": finish,
+               "usage": usage, "engine": engine, "text": "".join(texts)}
+        if expected is not None and engine["prompt_tokens"] != expected:
+            raise RuntimeError(f"token count mismatch: {expected} vs {engine['prompt_tokens']}")
+        if not texts:
+            raise RuntimeError(f"no text received for {label}")
+        (args.out / (label + "-raw.json")).write_text(json.dumps({"chunks": chunks, "row": row}, indent=2) + "\n")
+        rows.append(row)
+        (args.out / "results.json").write_text(json.dumps(rows, indent=2) + "\n")
+        print("done", label, json.dumps({"ttft_s": first, "elapsed_s": elapsed, "engine": engine}), flush=True)
+        return row
+
+    perform("warmup", request("Reply with exactly the word READY.", 16))
+    # Each nonce occurs before the filler so previous long prompts cannot be reused.
+    filler = "\n".join(f"def task_{i:05d}(value: int) -> int: return (value * {(i % 97) + 1} + {i}) % 100003"
+                       for i in range(12000))
+    ending = ("\n\nWrite a detailed explanation of the code above. Discuss deterministic integer transforms, "
+              "modulo arithmetic, testing, naming, complexity, and maintainability. Write at least 600 words.")
+    for target in map(int, args.targets.split(",")):
+        for run in range(1, args.runs + 1):
+            prefix = f"Benchmark nonce: series-{target}-trial-{run}.\nReview this synthetic Python module:\n"
+            lo, hi = 0, len(filler)
+            while lo < hi:
+                middle = (lo + hi + 1) // 2
+                if count(request(prefix + filler[:middle] + ending)) <= target:
+                    lo = middle
+                else:
+                    hi = middle - 1
+            req = request(prefix + filler[:lo] + ending)
+            actual = count(req)
+            if actual < target - 20 or actual > target:
+                raise RuntimeError(f"could not construct target {target}: {actual}")
+            perform(f"tokens-{target}-run-{run}", req, actual)
+    measured = [row for row in rows if row["label"] != "warmup"]
+    groups = {}
+    for target in map(int, args.targets.split(",")):
+        subset = [r for r in measured if r["label"].startswith(f"tokens-{target}-")]
+        values = {
+            "prompt_tokens": [r["engine"]["prompt_tokens"] for r in subset],
+            "cached_tokens": [r["engine"].get("reused", 0) for r in subset],
+            "output_tokens": [r["engine"]["engine_generated"] for r in subset],
+            "client_ttft_s": [r["client_ttft_s"] for r in subset],
+            "client_elapsed_s": [r["client_elapsed_s"] for r in subset],
+            "prefill_tok_s": [(r["engine"]["prompt_tokens"] - (r["engine"].get("reused") or 0)) / (r["engine"]["prompt_ms"] / 1000) for r in subset],
+            "decode_tok_s": [r["engine"]["engine_generated"] / (r["engine"]["decode_ms"] / 1000) for r in subset],
+        }
+        groups[str(target)] = {key: {"median": statistics.median(v), "min": min(v), "max": max(v)}
+                              for key, v in values.items()}
+    (args.out / "summary.json").write_text(json.dumps(groups, indent=2) + "\n")
+    print("summary", json.dumps(groups), flush=True)
+
+
+if __name__ == "__main__":
+    main()

bench/results/2026-09-30-community-rtx-5090/engine.log

@@ -0,0 +1,91 @@
+load_hparams: Qwen-VL models require at minimum 1024 image tokens to function correctly on grounding tasks
+load_hparams: if you encounter problems with accuracy, try adding --image-min-tokens 1024
+load_hparams: more info: https://github.com/ggml-org/llama.cpp/issues/16842
+
+strata-vision: warmed up at 1024 image tokens
+strata generate: PCIe probe: 50.0 GB/s host->device -> pcie_frac 0.55 (default 0.55)
+strata generate: this CPU has no AVX-512: the expert kernels run on AVX-2 (multi-token for the i-quant gate/up rows)
+strata generate: native pack: /workspace/Strata-data/packs/iq2_xs experts (largest blob 1.51 MB), token embedding type 23 in mapped host memory (322 MiB)
+strata generate: 1466 MiB of weights loaded from /workspace/Strata-data/packs/iq2_xs (302 canonical tensors skipped: served natively)
+strata generate: 300 native projection matrices, 1474.85 MiB of weights
+strata generate: KV streaming: 32768 of 131072 cells per QSA layer in VRAM, the K/V in 1.55 GiB of pinned RAM
+strata generate: PLE on, table 320001536 rows of /workspace/Strata-data/models/IQ2_XS/Qwen3.8-Flash-Next-GSQ-RCO-IQ2_XS-00002-of-00002.gguf
+strata mtp: draft layer loaded, 835 MiB of VRAM (experts 675, dense 111), files read in 0.23 s (3441 MiB/s)
+strata generate: expert arena: cudaHostRegister PORTABLE ok; MAP_HUGETLB unavailable (no hugetlb pool configured?); using 4 KB pages
+strata generate: loaded 33.02 GiB at 2.21 GiB/s
+strata generate: profile /workspace/Strata/data/expert-profile.bin: 24576 ranked pairs, built for 24576 slots
+strata generate: experimental native Q5_K head, 337715200 bytes
+strata generate: expert cache auto: 24.26 GiB free, 700 MiB reserved (+143 MiB for the draft head) -> 16663 slots
+strata generate: expert cache 17463 slots, 23.44 GiB of VRAM; policy is
+strata generate: the GPU computes the experts in the cache; it rounds differently from the CPU,
+                 so a reply can differ slightly from a run without the cache (same quality:
+                 bench/results/2026-09-27-cache-parity).
+                 PROFILE, ranked by routing frequency, no eviction.
+strata generate: pre-filled 17463 of 17463 slots from the profile; slot 0 verified
+strata generate: R4 hit path ON - resident experts are computed on the GPU
+strata generate: 23 expert-pool workers + the host thread
+strata generate: session is up (engine 0.1.29)
+strata generate: token graph hit path: 17463 resident experts, decided on the device
+strata serve: prompt chunk auto: 8192 tokens
+strata serve: the prompt path borrows 3421 cache slots (4.62 GiB)
+strata verify: window up to 6 tokens, 57.1 MiB of device buffers
+strata mtp: draft head over 106299 tokens (137.9 MiB)
+strata serve: 412 MiB of VRAM free with everything loaded
+strata verify: captured the 6-token window (upload no error, sync no error)
+strata verify: captured the 1-token window (upload no error, sync no error)
+strata verify: captured the 4-token window (upload no error, sync no error)
+strata serve: prompt 19 tokens = 0 reused + 19 read in 135 ms (140.9 tok/s), 2 generated in 57 ms (34.9 tok/s), drafts accepted 3 of 3, 1 checkpoints
+strata serve: decode expert cache hit rate: 90.8% (2037 hits / 2244 lookups)
+strata serve: KV streaming: 92.31% of 936 block reads hit VRAM, 0.3 MiB read from RAM
+strata verify: captured the 3-token window (upload no error, sync no error)
+strata verify: captured the 2-token window (upload no error, sync no error)
+strata serve: prompt 4096 tokens = 0 reused + 4096 read in 1167 ms (3510.2 tok/s), 256 generated in 1587 ms (161.3 tok/s), drafts accepted 149 of 226, 1 checkpoints
+strata serve: decode expert cache hit rate: 97.8% (155423 hits / 158942 lookups)
+strata serve: KV streaming: 99.39% of 2095092 block reads hit VRAM, 51.8 MiB read from RAM
+strata serve: suffix drafts: 3 windows, 6 of 9 drafts accepted
+strata serve: prompt 4096 tokens = 0 reused + 4096 read in 959 ms (4269.7 tok/s), 256 generated in 1385 ms (184.8 tok/s), drafts accepted 158 of 221, 1 checkpoints
+strata serve: decode expert cache hit rate: 99.3% (151655 hits / 152786 lookups)
+strata serve: KV streaming: 99.36% of 2002680 block reads hit VRAM, 51.7 MiB read from RAM
+strata serve: suffix drafts: 1 windows, 2 of 3 drafts accepted
+strata serve: prompt 4096 tokens = 0 reused + 4096 read in 958 ms (4273.4 tok/s), 256 generated in 1427 ms (179.4 tok/s), drafts accepted 160 of 226, 1 checkpoints
+strata serve: decode expert cache hit rate: 98.9% (153580 hits / 155293 lookups)
+strata serve: KV streaming: 99.37% of 2039616 block reads hit VRAM, 52.0 MiB read from RAM
+strata serve: suffix drafts: 2 windows, 2 of 8 drafts accepted
+strata serve: prompt 32768 tokens = 0 reused + 32768 read in 5929 ms (5527.1 tok/s), 256 generated in 1541 ms (166.1 tok/s), drafts accepted 148 of 218, 2 checkpoints
+strata serve: decode expert cache hit rate: 99.4% (155252 hits / 156260 lookups)
+strata serve: KV streaming: 97.72% of 2045772 block reads hit VRAM, 187.5 MiB read from RAM
+strata serve: prompt 32768 tokens = 0 reused + 32768 read in 5911 ms (5543.1 tok/s), 256 generated in 1408 ms (181.8 tok/s), drafts accepted 160 of 212, 2 checkpoints
+strata serve: decode expert cache hit rate: 99.4% (147298 hits / 148150 lookups)
+strata serve: KV streaming: 97.53% of 1941048 block reads hit VRAM, 193.2 MiB read from RAM
+strata serve: prompt 32768 tokens = 0 reused + 32768 read in 5905 ms (5549.6 tok/s), 256 generated in 1457 ms (175.7 tok/s), drafts accepted 153 of 206, 2 checkpoints
+strata serve: decode expert cache hit rate: 99.5% (148308 hits / 149107 lookups)
+strata serve: KV streaming: 97.56% of 1953372 block reads hit VRAM, 191.7 MiB read from RAM
+strata serve: suffix drafts: 2 windows, 3 of 8 drafts accepted
+strata serve: prompt 128000 tokens = 0 reused + 128000 read in 22100 ms (5791.9 tok/s), 256 generated in 1654 ms (154.8 tok/s), drafts accepted 149 of 218, 6 checkpoints
+strata serve: decode expert cache hit rate: 99.4% (155334 hits / 156245 lookups)
+strata serve: KV streaming: 96.13% of 2045748 block reads hit VRAM, 318.7 MiB read from RAM
+strata serve: prompt 128000 tokens = 0 reused + 128000 read in 22151 ms (5778.7 tok/s), 256 generated in 1512 ms (169.3 tok/s), drafts accepted 159 of 228, 6 checkpoints
+strata serve: decode expert cache hit rate: 99.7% (155406 hits / 155913 lookups)
+strata serve: KV streaming: 96.26% of 2039616 block reads hit VRAM, 307.0 MiB read from RAM
+strata serve: prompt 128000 tokens = 0 reused + 128000 read in 22195 ms (5767.0 tok/s), 256 generated in 1551 ms (165.0 tok/s), drafts accepted 154 of 221, 6 checkpoints
+strata serve: decode expert cache hit rate: 99.7% (155405 hits / 155914 lookups)
+strata serve: KV streaming: 96.22% of 2039628 block reads hit VRAM, 310.9 MiB read from RAM
+strata serve: suffix drafts: 1 windows, 1 of 2 drafts accepted
+strata serve: prompt 30804 tokens = 0 reused + 30804 read in 6382 ms (4826.8 tok/s), 9 generated in 55 ms (163.1 tok/s), drafts accepted 6 of 6, 2 checkpoints
+strata serve: decode expert cache hit rate: 92.6% (3832 hits / 4139 lookups)
+strata serve: KV streaming: 78.28% of 92424 block reads hit VRAM, 80.8 MiB read from RAM
+strata serve: prompt 30804 tokens = 0 reused + 30804 read in 5709 ms (5395.4 tok/s), 9 generated in 61 ms (147.0 tok/s), drafts accepted 6 of 6, 2 checkpoints
+strata serve: decode expert cache hit rate: 94.7% (3971 hits / 4192 lookups)
+strata serve: KV streaming: 78.55% of 92424 block reads hit VRAM, 79.9 MiB read from RAM
+strata serve: prompt 30805 tokens = 0 reused + 30805 read in 5718 ms (5387.6 tok/s), 10 generated in 66 ms (150.4 tok/s), drafts accepted 9 of 9, 2 checkpoints
+strata serve: decode expert cache hit rate: 96.3% (5885 hits / 6108 lookups)
+strata serve: KV streaming: 82.56% of 117072 block reads hit VRAM, 82.3 MiB read from RAM
+strata serve: prompt 126320 tokens = 0 reused + 126320 read in 22281 ms (5669.4 tok/s), 10 generated in 65 ms (154.9 tok/s), drafts accepted 7 of 7, 6 checkpoints
+strata serve: decode expert cache hit rate: 97.7% (5115 hits / 5233 lookups)
+strata serve: KV streaming: 77.02% of 104760 block reads hit VRAM, 97.0 MiB read from RAM
+strata serve: prompt 126318 tokens = 0 reused + 126318 read in 22453 ms (5626.0 tok/s), 9 generated in 105 ms (86.0 tok/s), drafts accepted 6 of 14, 6 checkpoints
+strata serve: decode expert cache hit rate: 97.4% (9224 hits / 9474 lookups)
+strata serve: KV streaming: 82.65% of 160212 block reads hit VRAM, 112.0 MiB read from RAM
+strata serve: prompt 126318 tokens = 16384 reused + 109934 read in 19791 ms (5554.7 tok/s), 9 generated in 78 ms (115.2 tok/s), drafts accepted 7 of 9, 6 checkpoints
+strata serve: decode expert cache hit rate: 98.1% (6539 hits / 6669 lookups)
+strata serve: KV streaming: 87.22% of 283464 block reads hit VRAM, 146.0 MiB read from RAM

bench/results/2026-09-30-community-rtx-5090/final-status.json

@@ -0,0 +1 @@
+{"service": "strata", "model": "qwen3.8-flash-next-iq2_xs", "engine": "0.1.29", "started": 1790772696, "uptime_s": 196, "cache_max_tokens": 131072, "context": {"native": 131072, "max_positions": 131072}, "concurrency": {"serving": 1, "requested": 1}, "dialects": ["/v1/chat/completions", "/v1/messages"], "vision": {"enabled": true, "available": true, "error": null}, "activity": {"requests": 16, "in_flight": 0, "last_request_at": 1790772892}, "last_timings": {"cache_n": 16384, "prompt_n": 109934, "prompt_ms": 19791.2, "prompt_per_token_ms": 0.18, "prompt_per_second": 5554.7, "predicted_n": 9, "predicted_ms": 78.1, "predicted_per_token_ms": 8.678, "predicted_per_second": 115.2, "draft_n": 9, "draft_n_accepted": 7, "at": 1790772892}, "machine": {"at": 1790772892, "gpu": {"name": "NVIDIA GeForce RTX 5090", "used_mib": 32281.0, "total_mib": 32607.0, "util_pct": 100, "temp_c": 72, "power_w": 528.823}, "ram": {"used_gib": 45.4, "total_gib": 62.2}}}

bench/results/2026-09-30-community-rtx-5090/initial-status.json

@@ -0,0 +1,46 @@
+{
+  "service": "strata",
+  "model": "qwen3.8-flash-next-iq2_xs",
+  "engine": "0.1.29",
+  "started": 1790772696,
+  "uptime_s": 2,
+  "cache_max_tokens": 131072,
+  "context": {
+    "native": 131072,
+    "max_positions": 131072
+  },
+  "concurrency": {
+    "serving": 1,
+    "requested": 1
+  },
+  "dialects": [
+    "/v1/chat/completions",
+    "/v1/messages"
+  ],
+  "vision": {
+    "enabled": true,
+    "available": true,
+    "error": null
+  },
+  "activity": {
+    "requests": 0,
+    "in_flight": 0,
+    "last_request_at": null
+  },
+  "last_timings": null,
+  "machine": {
+    "at": 1790772698,
+    "gpu": {
+      "name": "NVIDIA GeForce RTX 5090",
+      "used_mib": 32195.0,
+      "total_mib": 32607.0,
+      "util_pct": 0,
+      "temp_c": 36,
+      "power_w": 83.403
+    },
+    "ram": {
+      "used_gib": 44.4,
+      "total_gib": 62.2
+    }
+  }
+}

bench/results/2026-09-30-community-rtx-5090/memory-summary.json

@@ -0,0 +1,10 @@
+{
+  "samples": 250,
+  "sample_duration_s": 255.8539924621582,
+  "sampling_method": "nominal 1 s; /proc/meminfo and nvidia-smi; startup through recall checks",
+  "ram_peak_GiB": 45.54487228393555,
+  "gpu_peak_MiB": 31785.0,
+  "swap_start_MiB": 0.25,
+  "swap_peak_MiB": 425.5,
+  "swap_end_MiB": 425.5
+}

bench/results/2026-09-30-community-rtx-5090/model-provenance.json

@@ -0,0 +1,46 @@
+[
+  {
+    "repository": "ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF",
+    "revision": "ed59f92082b1e93c0e96d60a8b11aab089b52f09",
+    "gguf_files": [
+      {
+        "filename": "IQ2_XS/Qwen3.8-Flash-Next-GSQ-RCO-IQ2_XS-00001-of-00002.gguf",
+        "lfs": {
+          "sha256": "92cee27ae5bbadcd732416a0f7a7f0acc092399dbbe8f5a5efa707c2ec0a49d7",
+          "size": 39225954592,
+          "pointerSize": 136
+        },
+        "size": 39225954592,
+        "actual_sha256": "92cee27ae5bbadcd732416a0f7a7f0acc092399dbbe8f5a5efa707c2ec0a49d7",
+        "verified": true
+      },
+      {
+        "filename": "IQ2_XS/Qwen3.8-Flash-Next-GSQ-RCO-IQ2_XS-00002-of-00002.gguf",
+        "lfs": {
+          "sha256": "316b46f3a2dbd68c900f43136ab9449f9dcc3725dfd8c794847c204bc161e113",
+          "size": 28800138432,
+          "pointerSize": 136
+        },
+        "size": 28800138432,
+        "actual_sha256": "316b46f3a2dbd68c900f43136ab9449f9dcc3725dfd8c794847c204bc161e113",
+        "verified": true
+      },
+      {
+        "filename": "mmproj-Qwen3.8-Flash-Next-BF16.gguf",
+        "lfs": {
+          "sha256": "b1a82259702816a5330d7bd7607cd9676b11780e79ff7348c21103ff3ce49bd0",
+          "size": 907543008,
+          "pointerSize": 134
+        },
+        "size": 907543008,
+        "actual_sha256": "b1a82259702816a5330d7bd7607cd9676b11780e79ff7348c21103ff3ce49bd0",
+        "verified": true
+      }
+    ]
+  },
+  {
+    "repository": "Qwen/Qwen3.8-Flash-Next",
+    "revision": "de4b8e4d43b917e7706784d8bb445c9af86a3540",
+    "gguf_files": []
+  }
+]

bench/results/2026-09-30-community-rtx-5090/monitor.py

@@ -0,0 +1,18 @@
+"""Sample system RAM, swap and GPU memory once per second during the trial."""
+import json
+import subprocess
+import sys
+import time
+from pathlib import Path
+
+with Path(sys.argv[1] if len(sys.argv) > 1 else 'telemetry.jsonl').open('a') as output:
+    while True:
+        memory = {}
+        for line in Path('/proc/meminfo').read_text().splitlines():
+            key, value = line.split(':', 1)
+            if key in ('MemTotal', 'MemAvailable', 'SwapTotal', 'SwapFree'):
+                memory[key + '_KiB'] = int(value.strip().split()[0])
+        gpu = subprocess.check_output(['nvidia-smi', '--query-gpu=memory.used,memory.total,utilization.gpu,power.draw,temperature.gpu', '--format=csv,noheader,nounits'], text=True).strip()
+        output.write(json.dumps({'epoch_s': time.time(), 'memory': memory, 'gpu': gpu}) + '\n')
+        output.flush()
+        time.sleep(1)

bench/results/2026-09-30-community-rtx-5090/mtp-manifest.json

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bench/results/2026-09-30-community-rtx-5090/needles.json

@@ -0,0 +1,56 @@
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bench/results/2026-09-30-community-rtx-5090/python-packages.txt

@@ -0,0 +1,15 @@
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+charset-normalizer==3.5.2
+cmake==4.4.3
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bench/results/2026-09-30-community-rtx-5090/results.json

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+[
+  {
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+    "text": "READY"
+  },
+  {
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+    "request_sha256": "5d94c652067354bbe72930d2bdad3886564f08cf2546b32411af02d33ad3ce05",
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+      "decode_ms": 1587.0,
+      "decode_tok_s": 161.3,
+      "hit_rate": 0.978
+    },
+    "text": "# Analysis of Synthetic Python Module: Deterministic Integer Transforms\n\nThe provided Python module consists of a series of functions, `task_00000` through `task_00112`, each implementing a specific deterministic integer transformation. While the code appears repetitive, it serves as a clear example of modular arithmetic, function design, and the trade-offs between explicit implementation and algorithmic abstraction. Below is a detailed discussion covering the mathematical basis, testing implications, naming conventions, complexity, and maintainability.\n\n## 1. Mathematical Basis: Deterministic Transforms and Modulo Arithmetic\n\nAt its core, every function in this module performs a linear transformation on an input integer `value`. The general pattern for `task_NNNNN` is:\n\n$$ f(x) = (x \\cdot a + b) \\pmod{M} $$\n\nWhere:\n- $M = 100003$ is a fixed modulus.\n- $a$ is the multiplier.\n- $b$ is the additive constant.\n\nFor most functions (e.g., `task_00000` to `task_00096`), the multiplier $a$ and the additive constant $b$ follow a"
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+      "decode_ms": 1385.0,
+      "decode_tok_s": 184.8,
+      "hit_rate": 0.993
+    },
+    "text": "# Analysis of Synthetic Python Module: Deterministic Integer Transforms\n\nThe provided Python module consists of a series of functions, `task_00000` through `task_00112`, each implementing a specific deterministic integer transformation. While the code appears repetitive, it serves as a clear example of modular arithmetic, function design, and the trade-offs between explicit implementation and algorithmic abstraction. Below is a detailed discussion of the code's structure, mathematical properties, testing implications, and maintainability.\n\n## 1. Mathematical Structure and Deterministic Transforms\n\nAt its core, every function in this module implements a linear congruential generator (LCG) step or a simple affine transformation modulo a prime number. The general form of each function is:\n\n$$ f_n(x) = (x \\cdot a_n + b_n) \\pmod{M} $$\n\nWhere:\n- $M = 100003$ is a fixed modulus.\n- $a_n$ is the multiplier for function $n$.\n- $b_n$ is the additive constant for function $n$.\n\nFor the first 97 functions (`task_00000` to `task_00096`"
+  },
+  {
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+    "expected_prompt_tokens": 4096,
+    "request_sha256": "946079edb58e764050805886156dc8f877cc29e1ca8d01c84e9a1e7917d49492",
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+      "decode_ms": 1427.2,
+      "decode_tok_s": 179.4,
+      "hit_rate": 0.989
+    },
+    "text": "# Analysis of Synthetic Python Module: Deterministic Integer Transforms\n\nThe provided Python module consists of a series of functions, `task_00000` through `task_00112`, each implementing a specific deterministic integer transformation. While the code appears repetitive, it serves as a clear example of modular arithmetic, function design, and the trade-offs between explicit implementation and algorithmic abstraction. Below is a detailed discussion of the code\u2019s structure, mathematical properties, testing implications, naming conventions, complexity, and maintainability.\n\n## 1. Mathematical Structure and Deterministic Transforms\n\nEach function follows the general form:\n```python\ndef task_XXXXX(value: int) -> int:\n    return (value * multiplier + addend) % MOD\n```\nwhere `MOD = 100003`.\n\nFor most functions, the multiplier and addend are derived from the function index `i`:\n- `multiplier = i + 1`\n- `addend = i`\n\nThus, for `task_00000`, `multiplier = 1`, `addend = 0`.\nFor `task_00001`, `multiplier = 2`, `"
+  },
+  {
+    "label": "tokens-32768-run-1",
+    "expected_prompt_tokens": 32768,
+    "request_sha256": "e37cfc2ab9867d91130927440df24a57255a736b8d0d829d07d828e22e3de572",
+    "client_ttft_s": 5.9679448089996185,
+    "client_elapsed_s": 7.498308491999978,
+    "finish_reason": "length",
+    "usage": {
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+      "completion_tokens": 256,
+      "total_tokens": 33024,
+      "prompt_tokens_details": {
+        "cached_tokens": 0
+      }
+    },
+    "engine": {
+      "projection": null,
+      "time": 1790772708.3260226,
+      "duration_s": 7.5,
+      "finish": "length",
+      "prompt_tokens": 32768,
+      "reused": 0,
+      "output_tokens": 256,
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+      "prompt_ms": 5928.6,
+      "decode_ms": 1541.2,
+      "decode_tok_s": 166.1,
+      "hit_rate": 0.994
+    },
+    "text": "Based on the provided Python module, here is a detailed technical review and explanation.\n\n### Overview of the Module\n\nThe provided code defines a series of functions, `task_00000` through `task_00888`, that perform deterministic integer transformations. Each function takes an integer `value` and returns a new integer based on a specific linear formula involving multiplication, addition, and modulo arithmetic. The benchmark nonce `series-32768-trial-1` suggests this code is part of a larger test suite or performance benchmark, likely designed to evaluate computational consistency, hash generation, or state machine transitions.\n\n### Mathematical Logic and Deterministic Transforms\n\nAt its core, every function in this module implements a linear congruential generator (LCG) style transformation, albeit with varying parameters. The general structure of each function is:\n\n$$ f(x) = (x \\cdot a + b) \\pmod{100003} $$\n\nWhere:\n*   $x$ is the input `value`.\n*   $a$ is the multiplier (coefficient).\n*   $b$ is the additive constant.\n*   $100003$ is the modulus."
+  },
+  {
+    "label": "tokens-32768-run-2",
+    "expected_prompt_tokens": 32768,
+    "request_sha256": "ed2dfaa11aecad9f4563f33bb5f5d4b8704486508700b8fa572722cc1225ee26",
+    "client_ttft_s": 5.950024114999906,
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+    "finish_reason": "length",
+    "usage": {
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+      "completion_tokens": 256,
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+      "prompt_tokens_details": {
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+      }
+    },
+    "engine": {
+      "projection": null,
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+      "duration_s": 7.3,
+      "finish": "length",
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+      "reused": 0,
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+      "prompt_ms": 5911.4,
+      "decode_ms": 1408.3,
+      "decode_tok_s": 181.8,
+      "hit_rate": 0.994
+    },
+    "text": "Based on the provided synthetic Python module, here is a detailed technical review and explanation.\n\n### 1. Structural Analysis and Pattern Recognition\n\nThe module consists of a series of functions named `task_00000` through `task_00888` (the list appears truncated at `task_00888`, but the pattern implies a larger set). Each function follows a strict, deterministic signature: `def task_XXXXX(value: int) -> int`.\n\nThe core logic within each function is a linear transformation followed by a modulo operation:\n$$ f(x) = (x \\cdot a + b) \\pmod{100003} $$\n\nWhere:\n*   $x$ is the input `value`.\n*   $a$ is the multiplier.\n*   $b$ is the additive constant.\n*   $100003$ is the modulus.\n\n#### The Multiplier Pattern ($a$)\nThe multiplier $a$ follows a cyclic pattern. It starts at 1 and increments by 1 for each function until it reaches 97. After `task_00096` (where $a=97$), the"
+  },
+  {
+    "label": "tokens-32768-run-3",
+    "expected_prompt_tokens": 32768,
+    "request_sha256": "2a3c7342622e80c3cd8b37e062f0a2f64640c119ece8cccbf168639041eac079",
+    "client_ttft_s": 5.941602006999801,
+    "client_elapsed_s": 7.389697677000186,
+    "finish_reason": "length",
+    "usage": {
+      "prompt_tokens": 32768,
+      "completion_tokens": 256,
+      "total_tokens": 33024,
+      "prompt_tokens_details": {
+        "cached_tokens": 0
+      }
+    },
+    "engine": {
+      "projection": null,
+      "time": 1790772724.435358,
+      "duration_s": 7.4,
+      "finish": "length",
+      "prompt_tokens": 32768,
+      "reused": 0,
+      "output_tokens": 256,
+      "engine_generated": 256,
+      "prompt_ms": 5904.6,
+      "decode_ms": 1457.1,
+      "decode_tok_s": 175.7,
+      "hit_rate": 0.995
+    },
+    "text": "Based on the provided Python module, here is a detailed technical review and explanation.\n\n### 1. Structural Overview and Pattern Analysis\n\nThe provided code consists of a series of function definitions, ranging from `task_00000` to `task_00888`. Each function follows a strict, repetitive structure:\n\n```python\ndef task_XXXXX(value: int) -> int:\n    return (value * A + B) % 100003\n```\n\nThis module is a synthetic representation of a **linear congruential generator (LCG)** or a set of **affine transformations** applied to an integer input. The core logic is consistent across all functions, with only the coefficients `A` (multiplier) and `B` (addend) changing.\n\n### 2. Deterministic Integer Transforms and Modulo Arithmetic\n\nThe mathematical core of this module is the expression `(value * A + B) % 100003`. This is a classic example of a **deterministic integer transform**.\n\n*   **Determinism:** For any given input `value`, the output is always the same. There is no randomness, state, or side effects. This makes"
+  },
+  {
+    "label": "tokens-128000-run-1",
+    "expected_prompt_tokens": 128000,
+    "request_sha256": "3235aac3f934cf1d889c25973ca172b355509dc2522fdfb463fbefe979a9075d",
+    "client_ttft_s": 22.212050286999784,
+    "client_elapsed_s": 23.856042405000153,
+    "finish_reason": "length",
+    "usage": {
+      "prompt_tokens": 128000,
+      "completion_tokens": 256,
+      "total_tokens": 128256,
+      "prompt_tokens_details": {
+        "cached_tokens": 0
+      }
+    },
+    "engine": {
+      "projection": null,
+      "time": 1790772733.6124191,
+      "duration_s": 23.8,
+      "finish": "length",
+      "prompt_tokens": 128000,
+      "reused": 0,
+      "output_tokens": 256,
+      "engine_generated": 256,
+      "prompt_ms": 22099.7,
+      "decode_ms": 1653.5,
+      "decode_tok_s": 154.8,
+      "hit_rate": 0.994
+    },
+    "text": "Based on the provided synthetic Python module, here is a detailed technical review and explanation.\n\n### Overview of the Module\n\nThe provided code consists of a massive collection of function definitions, ranging from `task_00000` to `task_03404` (and potentially beyond, as the input cuts off). Each function follows a strict, repetitive pattern. The core logic of every function is identical in structure, differing only in the specific integer constants used for multiplication and addition.\n\nThe general form of any function `task_N` is:\n```python\ndef task_N(value: int) -> int:\n    return (value * multiplier + addend) % 100003\n```\n\n### 1. Deterministic Integer Transforms and Modulo Arithmetic\n\nThe primary mechanism in this module is a **deterministic linear transform** followed by a **modulo operation**.\n\n**The Linear Transform:**\nFor any given `value`, the function computes `value * multiplier + addend`. This is an affine transformation. In mathematics, an affine transformation is a function that preserves points, straight lines, and planes. Here, it maps an input integer to an output integer based on a fixed slope (`multiplier`) and"
+  },
+  {
+    "label": "tokens-128000-run-2",
+    "expected_prompt_tokens": 128000,
+    "request_sha256": "5f4b8bc9f0afc7765320abd89960c0938e0d7cf5974e962fdbd0613fef31c8d4",
+    "client_ttft_s": 22.262365384000077,
+    "client_elapsed_s": 23.764614322999932,
+    "finish_reason": "length",
+    "usage": {
+      "prompt_tokens": 128000,
+      "completion_tokens": 256,
+      "total_tokens": 128256,
+      "prompt_tokens_details": {
+        "cached_tokens": 0
+      }
+    },
+    "engine": {
+      "projection": null,
+      "time": 1790772759.195895,
+      "duration_s": 23.7,
+      "finish": "length",
+      "prompt_tokens": 128000,
+      "reused": 0,
+      "output_tokens": 256,
+      "engine_generated": 256,
+      "prompt_ms": 22150.5,
+      "decode_ms": 1511.7,
+      "decode_tok_s": 169.3,
+      "hit_rate": 0.997
+    },
+    "text": "# Analysis of the Synthetic Python Module\n\nThe provided Python module consists of a massive collection of functions, ranging from `task_00000` to `task_03404` (and potentially beyond, as the code is truncated). Each function follows a strict, repetitive pattern: it accepts an integer `value`, applies a linear transformation, and returns the result modulo a fixed prime number, `100003`.\n\n## 1. Mathematical Structure and Deterministic Transforms\n\nAt its core, every function in this module implements a **linear congruential generator (LCG)** step or a simple affine transformation. The general formula for any function `task_N` is:\n\n$$ f_N(x) = (x \\cdot a_N + b_N) \\pmod{M} $$\n\nWhere:\n*   $M = 100003$ is the modulus.\n*   $a_N$ is the multiplier for task $N$.\n*   $b_N$ is the additive constant for task $N$.\n\n### The Modulus: 100003\nThe choice of `100003` is significant. It is a **prime number**. In"
+  },
+  {
+    "label": "tokens-128000-run-3",
+    "expected_prompt_tokens": 128000,
+    "request_sha256": "5c3fb71db9f9f44765f28389d1032f2c8b6687d1bf5ca6f33aeb924072cfa6e4",
+    "client_ttft_s": 22.308453708999878,
+    "client_elapsed_s": 23.850161715000013,
+    "finish_reason": "length",
+    "usage": {
+      "prompt_tokens": 128000,
+      "completion_tokens": 256,
+      "total_tokens": 128256,
+      "prompt_tokens_details": {
+        "cached_tokens": 0
+      }
+    },
+    "engine": {
+      "projection": null,
+      "time": 1790772784.6969988,
+      "duration_s": 23.8,
+      "finish": "length",
+      "prompt_tokens": 128000,
+      "reused": 0,
+      "output_tokens": 256,
+      "engine_generated": 256,
+      "prompt_ms": 22195.2,
+      "decode_ms": 1551.4,
+      "decode_tok_s": 165.0,
+      "hit_rate": 0.997
+    },
+    "text": "Based on the provided synthetic Python module, here is a detailed technical review and explanation.\n\n### Overview of the Module\n\nThe provided code consists of a massive collection of function definitions, ranging from `task_00000` to `task_03404`. Each function accepts a single integer argument `value` and returns an integer. The core logic within every function is identical in structure, differing only in the specific coefficients used for multiplication and addition.\n\nThe general pattern for any function `task_N` is:\n```python\ndef task_N(value: int) -> int:\n    return (value * multiplier + addend) % 100003\n```\n\n### Deterministic Integer Transforms and Modulo Arithmetic\n\nThe functions implement a **deterministic linear transform** over the integers. In mathematics, this is an affine transformation defined by $f(x) = ax + b$. However, the code applies this transformation within a finite field defined by the modulus $M = 100003$.\n\n1.  **Modulo Arithmetic**: The use of `% 100003` ensures that the output is always within the range $[0, 100002"
+  }
+]

bench/results/2026-09-30-community-rtx-5090/strata-iq2_xs.json

@@ -0,0 +1,50 @@
+{
+ "exe": "/workspace/Strata/engine/strata",
+ "args": [
+  "--pack",
+  "/workspace/Strata-data/packs/iq2_xs",
+  "--native",
+  "/workspace/Strata-data/models/IQ2_XS/Qwen3.8-Flash-Next-GSQ-RCO-IQ2_XS-00001-of-00002.gguf",
+  "--ple-gguf",
+  "/workspace/Strata-data/models/IQ2_XS/Qwen3.8-Flash-Next-GSQ-RCO-IQ2_XS-00002-of-00002.gguf",
+  "--expert-profile",
+  "/workspace/Strata/data/expert-profile.bin",
+  "--expert-cache",
+  "auto",
+  "--prefill",
+  "auto",
+  "--spec",
+  "4",
+  "--spec-min-p",
+  "0.5",
+  "--mtp",
+  "/workspace/Strata-data/mtp/rt",
+  "--max-context",
+  "131072",
+  "--kv",
+  "int8",
+  "--kv-resident",
+  "32768",
+  "--vision",
+  "--vram-reserve-mib",
+  "700"
+ ],
+ "cwd": "/workspace/Strata",
+ "tokenizer": "/workspace/Strata-data/packs/iq2_xs/tokenizer",
+ "model_name": "qwen3.8-flash-next-iq2_xs",
+ "log": "/workspace/Strata/strata-iq2_xs.log",
+ "lib_dirs": [
+  "/usr/local/cuda/bin",
+  "/usr/local/cuda/lib64"
+ ],
+ "port": 18080,
+ "gpu": 0,
+ "gpus_asked": true,
+ "vision": {
+  "exe": "/workspace/Strata/engine/strata-vision",
+  "mmproj": "/workspace/Strata-data/models/mmproj-Qwen3.8-Flash-Next-BF16.gguf",
+  "model": "/workspace/Strata-data/models/IQ2_XS/Qwen3.8-Flash-Next-GSQ-RCO-IQ2_XS-00001-of-00002.gguf",
+  "gpu": true,
+  "max_tokens": 1024
+ }
+}

bench/results/2026-09-30-community-rtx-5090/summary.json

@@ -0,0 +1,113 @@
+{
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+      "min": 4096,
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+  }
+}

bench/results/2026-09-30-community-rtx-5090/telemetry.jsonl

@@ -0,0 +1,250 @@
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+{"epoch_s": 1790772669.461572, "memory": {"MemTotal_KiB": 65208820, "MemAvailable_KiB": 57749256, "SwapTotal_KiB": 8388604, "SwapFree_KiB": 8388348}, "gpu": "1374, 32607, 25, 53.81, 36"}
+{"epoch_s": 1790772670.483304, "memory": {"MemTotal_KiB": 65208820, "MemAvailable_KiB": 56952232, "SwapTotal_KiB": 8388604, "SwapFree_KiB": 8388348}, "gpu": "3719, 32607, 2, 130.61, 36"}
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+{"epoch_s": 1790772894.9685812, "memory": {"MemTotal_KiB": 65208820, "MemAvailable_KiB": 17662644, "SwapTotal_KiB": 8388604, "SwapFree_KiB": 7952892}, "gpu": "31785, 32607, 0, 94.57, 60"}
+{"epoch_s": 1790772895.9877603, "memory": {"MemTotal_KiB": 65208820, "MemAvailable_KiB": 17666884, "SwapTotal_KiB": 8388604, "SwapFree_KiB": 7952892}, "gpu": "31785, 32607, 0, 70.56, 59"}
+{"epoch_s": 1790772897.009143, "memory": {"MemTotal_KiB": 65208820, "MemAvailable_KiB": 17675152, "SwapTotal_KiB": 8388604, "SwapFree_KiB": 7952892}, "gpu": "31785, 32607, 0, 69.94, 59"}
+{"epoch_s": 1790772898.0305054, "memory": {"MemTotal_KiB": 65208820, "MemAvailable_KiB": 17677208, "SwapTotal_KiB": 8388604, "SwapFree_KiB": 7952892}, "gpu": "31785, 32607, 0, 69.68, 58"}
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+{"epoch_s": 1790772900.0737712, "memory": {"MemTotal_KiB": 65208820, "MemAvailable_KiB": 17694368, "SwapTotal_KiB": 8388604, "SwapFree_KiB": 7952892}, "gpu": "31785, 32607, 0, 68.64, 57"}
+{"epoch_s": 1790772901.0949237, "memory": {"MemTotal_KiB": 65208820, "MemAvailable_KiB": 17701040, "SwapTotal_KiB": 8388604, "SwapFree_KiB": 7952892}, "gpu": "31785, 32607, 0, 68.54, 57"}
+{"epoch_s": 1790772902.1162262, "memory": {"MemTotal_KiB": 65208820, "MemAvailable_KiB": 17704052, "SwapTotal_KiB": 8388604, "SwapFree_KiB": 7952892}, "gpu": "31785, 32607, 0, 68.07, 56"}
+{"epoch_s": 1790772903.1371994, "memory": {"MemTotal_KiB": 65208820, "MemAvailable_KiB": 17716036, "SwapTotal_KiB": 8388604, "SwapFree_KiB": 7952892}, "gpu": "31785, 32607, 0, 67.54, 56"}
+{"epoch_s": 1790772904.1573613, "memory": {"MemTotal_KiB": 65208820, "MemAvailable_KiB": 17729424, "SwapTotal_KiB": 8388604, "SwapFree_KiB": 7952892}, "gpu": "31785, 32607, 0, 66.79, 56"}
+{"epoch_s": 1790772905.178784, "memory": {"MemTotal_KiB": 65208820, "MemAvailable_KiB": 17734376, "SwapTotal_KiB": 8388604, "SwapFree_KiB": 7952892}, "gpu": "31785, 32607, 0, 66.78, 55"}
+{"epoch_s": 1790772906.2002432, "memory": {"MemTotal_KiB": 65208820, "MemAvailable_KiB": 17741828, "SwapTotal_KiB": 8388604, "SwapFree_KiB": 7952892}, "gpu": "31785, 32607, 0, 66.73, 55"}
+{"epoch_s": 1790772907.2226708, "memory": {"MemTotal_KiB": 65208820, "MemAvailable_KiB": 17744104, "SwapTotal_KiB": 8388604, "SwapFree_KiB": 7952892}, "gpu": "31785, 32607, 0, 66.62, 54"}
+{"epoch_s": 1790772908.2435381, "memory": {"MemTotal_KiB": 65208820, "MemAvailable_KiB": 17741708, "SwapTotal_KiB": 8388604, "SwapFree_KiB": 7952892}, "gpu": "31785, 32607, 0, 65.50, 54"}
+{"epoch_s": 1790772909.2710078, "memory": {"MemTotal_KiB": 65208820, "MemAvailable_KiB": 17724256, "SwapTotal_KiB": 8388604, "SwapFree_KiB": 7952892}, "gpu": "31785, 32607, 0, 40.74, 53"}
+{"epoch_s": 1790772910.2964864, "memory": {"MemTotal_KiB": 65208820, "MemAvailable_KiB": 17746392, "SwapTotal_KiB": 8388604, "SwapFree_KiB": 7952892}, "gpu": "31785, 32607, 0, 31.75, 53"}
+{"epoch_s": 1790772911.3218958, "memory": {"MemTotal_KiB": 65208820, "MemAvailable_KiB": 17745548, "SwapTotal_KiB": 8388604, "SwapFree_KiB": 7952892}, "gpu": "31785, 32607, 0, 19.67, 53"}
+{"epoch_s": 1790772912.3480182, "memory": {"MemTotal_KiB": 65208820, "MemAvailable_KiB": 17746012, "SwapTotal_KiB": 8388604, "SwapFree_KiB": 7952892}, "gpu": "31785, 32607, 0, 14.41, 52"}
+{"epoch_s": 1790772913.3754818, "memory": {"MemTotal_KiB": 65208820, "MemAvailable_KiB": 17733056, "SwapTotal_KiB": 8388604, "SwapFree_KiB": 7952892}, "gpu": "31785, 32607, 0, 14.42, 52"}
+{"epoch_s": 1790772914.402011, "memory": {"MemTotal_KiB": 65208820, "MemAvailable_KiB": 17739736, "SwapTotal_KiB": 8388604, "SwapFree_KiB": 7952892}, "gpu": "31785, 32607, 0, 14.55, 52"}
+{"epoch_s": 1790772915.4289236, "memory": {"MemTotal_KiB": 65208820, "MemAvailable_KiB": 17733576, "SwapTotal_KiB": 8388604, "SwapFree_KiB": 7952892}, "gpu": "31785, 32607, 0, 14.24, 51"}
+{"epoch_s": 1790772916.4575953, "memory": {"MemTotal_KiB": 65208820, "MemAvailable_KiB": 17753656, "SwapTotal_KiB": 8388604, "SwapFree_KiB": 7952892}, "gpu": "31785, 32607, 0, 14.35, 51"}
+{"epoch_s": 1790772917.4829435, "memory": {"MemTotal_KiB": 65208820, "MemAvailable_KiB": 17752892, "SwapTotal_KiB": 8388604, "SwapFree_KiB": 7952892}, "gpu": "31785, 32607, 0, 14.47, 51"}
+{"epoch_s": 1790772918.5089724, "memory": {"MemTotal_KiB": 65208820, "MemAvailable_KiB": 17753536, "SwapTotal_KiB": 8388604, "SwapFree_KiB": 7952892}, "gpu": "31785, 32607, 0, 12.31, 51"}
+{"epoch_s": 1790772919.5349586, "memory": {"MemTotal_KiB": 65208820, "MemAvailable_KiB": 17755604, "SwapTotal_KiB": 8388604, "SwapFree_KiB": 7952892}, "gpu": "31785, 32607, 0, 12.49, 51"}
+{"epoch_s": 1790772920.5655951, "memory": {"MemTotal_KiB": 65208820, "MemAvailable_KiB": 17766328, "SwapTotal_KiB": 8388604, "SwapFree_KiB": 7952892}, "gpu": "31785, 32607, 0, 14.14, 50"}
+{"epoch_s": 1790772921.5931342, "memory": {"MemTotal_KiB": 65208820, "MemAvailable_KiB": 17743004, "SwapTotal_KiB": 8388604, "SwapFree_KiB": 7952892}, "gpu": "31785, 32607, 0, 14.21, 50"}
+{"epoch_s": 1790772922.637384, "memory": {"MemTotal_KiB": 65208820, "MemAvailable_KiB": 20934704, "SwapTotal_KiB": 8388604, "SwapFree_KiB": 7952892}, "gpu": "30128, 32607, 4, 16.71, 50"}
+{"epoch_s": 1790772924.2924495, "memory": {"MemTotal_KiB": 65208820, "MemAvailable_KiB": 37200652, "SwapTotal_KiB": 8388604, "SwapFree_KiB": 7952892}, "gpu": "2, 32607, 0, 60.26, 50"}

bench/results/2026-09-30-tc-gemv-sm75/README.md

@@ -0,0 +1,57 @@
+# BF16 GEMV on Turing tensor cores (sm_75): measured negative
+
+i7-8700K + RTX 2070 8 GB (sm_75, 36 SMs), engine 0.1.27 tree (`bf16_gemv.cu` is unchanged through 0.1.30),
+qwen3.8-flash-next (~125.7B MoE, GGUF-verified) Q2_0 in production. Isolated benchmark only -
+[`bench_tc_gemv.cu`](bench_tc_gemv.cu) touches nothing in the inference path. Question: the QSA prompt attention
+already rides the Turing tensor cores (`mma.sync.m16n8k8`, the #270 path); can the decode GEMVs do the same?
+
+## Setup
+
+- Baseline: the production `bf16_gemv` warp kernel, transcribed verbatim
+- TC variant: `mma.sync.aligned.m16n8k8.row.col.f32.f16.f16.f32` on BF16->FP16 (the sm_75 MMA has native f16 only).
+  BF16->FP16 conversion is exact (0 / 2,000,000 values inexact - bf16's 7-bit mantissa fits in fp16's 10 bits); the
+  A-fragment packing is the same one #270 validated (`a0` = row `gid`, k columns `2t..2t+1`; broadcast makes
+  `a0 == a1 == {x[2t], x[2t+1]}`)
+- Three TC variants to cover the conversion overhead from every angle: W preconverted to fp16 once at load
+  (`pc`), W converted in-register every call (`in`), and a fused ks=1 form that writes the output directly and skips
+  the reduce kernel (`fused`)
+- The k-split across blocks (`ks`) is swept over all values that divide n_in, and the best is reported - the TC
+  kernel gets its best occupancy
+- Parity against an fp64 host reference; 2000 iterations x 3 reps, warmed CUDA events
+
+## Results (best TC config per shape)
+
+| Shape (decode, n_in = 2560, BF16 W) | Baseline us | Best TC us | Speedup | Config |
+| --- | ---: | ---: | ---: | --- |
+| ssm_alpha/beta [2560, 48] | 8.73 | 11.32 | 0.77x | pc, ks=16 |
+| indexer.k_proj [2560, 128] | 7.14 | 12.09 | 0.59x | pc, ks=16 |
+| indexer.q_proj / router [2560, 512] | 9.48 | 15.78 | 0.60x | pc, ks=4 |
+| ple_value [2560, 2560] | 35.08 | 77.73 | 0.45x | pc, ks=40 |
+
+- Numerics pass: TC max error vs fp64 reference 1.7e-6 .. 7.2e-6 (fp16-accumulation level), baseline
+  0.7e-6 .. 2.3e-6. The TC variant computes the same thing, it is just slower.
+- The warp kernel is already bandwidth-bound at 276-374 GB/s effective W bandwidth (374 GB/s on the 13 MB
+  [2560, 2560] shape); the TC path reaches only 166-169 GB/s (4-byte lane loads, conversion instructions, in-tile
+  launch overhead).
+- A single-token GEMV broadcasts one token across the whole m16 MMA tile, so 15/16 of the MMA throughput is thrown
+  away - but the baseline is not compute-bound, so the wasted throughput buys nothing. The k-split sweep and the
+  fused variant do not recover it.
+- One-time W conversion cost is negligible (2 ms for 13 MB), so preconverting W for the TC path does not help.
+
+## How to rerun
+
+```
+nvcc -arch=sm_75 -O3 -o bench_tc_gemv.exe bench_tc_gemv.cu
+./bench_tc_gemv.exe
+```
+
+Prints the table above, per-shape latency/parity and the conversion exactness scan. On a newer card, adjust
+`-arch`; the packed-A layout comment in the source documents the fragment mapping it relies on.
+
+## Conclusion
+
+**Keep the warp kernel. Tensor cores are not a promising direction for the decode GEMVs on sm_75**, and this was a
+measured loss (1.3-2.2x slower), not a failed attempt to optimize. The Turing MMA stays worthwhile where the input
+really has m16 rows of work: the QSA prompt-attention tiles, which is where #270 uses it. Someone looking to
+re-evaluate on future hardware (a card with native bf16 or denser GEMV batching) can rerun this instead of
+rebuilding it.

bench/results/2026-09-30-tc-gemv-sm75/bench_tc_gemv.cu

@@ -0,0 +1,305 @@
+// bench_tc_gemv.cu - ISOLATED benchmark: BF16 GEMV baseline vs Tensor Core (mma.m16n8k8, BF16->FP16) on sm_75.
+// Nothing in the engine changes. Shapes are the REAL Strata decode GEMVs (BF16 weights, n_in = n_embd = 2560):
+//   ssm_alpha / ssm_beta   [2560, 48]   project_bf16(split=true)  -> bf16_gemv_split(TPR=32) -> warp kernel
+//   indexer.k_proj         [2560, 128]  project_bf16(split=false) -> bf16_gemv -> warp kernel
+//   indexer.q_proj / ffn_gate_inp / mlp.gate [2560, 512]  warp / split(TPR=32) -> warp kernel
+//   ple_value              [2560, 2560] warp
+// Baseline = bf16_gemv_warp_kernel transcribed verbatim from src/kernels/cuda/bf16_gemv.cu (production).
+// TC variant = same maths; A = x broadcast down m16 (single-token GEMV), k-chunk = 8;
+//   (a) W preconverted bf16->fp16 once, x converted in-kernel (timed)   [tc_preconv]
+//   (b) fully inline: W read as bf16 and converted in-register           [tc_inline]
+// Numerics: fp64 host reference; max abs err for baseline and both TC variants; bf16->fp16 exactness scan.
+#include <cuda_runtime.h>
+#include <cuda_fp16.h>
+#include <cstdio>
+#include <cstdint>
+#include <cstring>
+#include <cstdlib>
+#include <cmath>
+#include <vector>
+#include <string>
+#include <functional>
+#include <algorithm>
+
+#define CHECK(e) do { cudaError_t err_ = (e); if (err_ != cudaSuccess) { \
+    fprintf(stderr, "CUDA %s:%d %s\n", __FILE__, __LINE__, cudaGetErrorString(err_)); exit(1); } } while (0)
+
+__device__ __forceinline__ float bf16_f32(uint16_t v) { return __int_as_float((int) ((unsigned) v << 16)); }
+static float bf16_f32_host(uint16_t v) { unsigned u = (unsigned) v << 16; float f; memcpy(&f, &u, 4); return f; }
+static uint16_t f32_bf16_host(float f) {
+    unsigned u, r; memcpy(&u, &f, 4);
+    r = ((u >> 16) ^ (u >> 15)) & 1; u += 0x7fff + r;
+    return (uint16_t) (u >> 16);
+}
+
+// ============ baseline: production warp kernel (verbatim semantics from bf16_gemv.cu) ============
+__global__ void gemv_warp(const uint16_t* __restrict__ x, const uint16_t* __restrict__ w,
+                          float* __restrict__ y, long long n_in, long long n_out) {
+    const long long o = (long long) blockIdx.x * (blockDim.x >> 5) + (threadIdx.x >> 5);
+    if (o >= n_out) return;
+    const int lane = threadIdx.x & 31;
+    const uint16_t* row = w + o * n_in;
+    float acc = 0.0f;
+    for (long long i = lane; i < n_in; i += 32)
+        acc += bf16_f32(x[i]) * bf16_f32(row[i]);
+    for (int off = 16; off > 0; off >>= 1) acc += __shfl_down_sync(0xFFFFFFFFu, acc, off);
+    if (lane == 0) y[o] = acc;
+}
+
+// ============ TC: A(x) broadcast, k-split over blocks, partial D row 0 -> scratch ============
+// A fragment (m16n8k8, .f16): lane l: a0 = A[m=l>>2][k=2(l&3)], a1 = A[m][k=2(l&3)+1],
+//                               a2/a3 = same cols at row m+8.  Broadcast => a2=a0, a3=a1.
+// B fragment (.f16, col-major): lane l: b0 = B[k=2(l&3)][n=(l>>2)], b1 = B[k=2(l&3)+1][n].
+//   B[k][n] = W[n][k], so lane l loads W[n][2(l&3)], W[n][2(l&3)+1] - coalesced within the row.
+// D fragment: c0/c1 = D[m=l>>2][cols 2(l&3), +1], c2/c3 = row m+8.  All m rows identical -> row 0.
+__global__ void tc_preconv(const uint16_t* __restrict__ x_b, const uint16_t* __restrict__ w_f,
+                           float* __restrict__ partials, long long n_in, long long n_out, int ks) {
+    const int tile = blockIdx.x, ks_i = blockIdx.y, lane = threadIdx.x & 31;
+    const long long n_base = (long long) tile * 8;
+    const long long k_per = n_in / (long long) ks;
+    const long long k0 = (long long) ks_i * k_per;
+    const int t2 = 2 * (lane & 3);
+    float c0 = 0.f, c1 = 0.f, c2 = 0.f, c3 = 0.f;
+    for (long long kk0 = k0; kk0 < k0 + k_per; kk0 += 8) {
+        // A (2 x f16x2): a0 = {A[m][2t], A[m+8][2t]}, a1 = {A[m][2t+1], A[m+8][2t+1]} (broadcast -> dup halves)
+        unsigned xh = (unsigned) x_b[kk0 + t2] | ((unsigned) x_b[kk0 + t2 + 1] << 16);
+        // fp16 conversion in-register: bf16 -> f32 -> f16 (exact within fp16 range)
+        __half2 ha = __floats2half2_rn(bf16_f32((uint16_t) (xh & 0xFFFF)), bf16_f32((uint16_t) (xh >> 16)));
+        __half2 hb = __floats2half2_rn(bf16_f32((uint16_t) (xh & 0xFFFF)), bf16_f32((uint16_t) (xh >> 16)));
+        unsigned a0 = *reinterpret_cast<unsigned*>(&ha);
+        unsigned a1 = *reinterpret_cast<unsigned*>(&hb);
+        // B is already fp16 here: load as __half2 directly (bf16 path must NOT convert twice)
+        __half2 wbh = *reinterpret_cast<const __half2*>(&w_f[(n_base + (lane >> 2)) * n_in + kk0 + t2]);
+        unsigned b0 = *reinterpret_cast<unsigned*>(&wbh);
+        asm volatile("mma.sync.aligned.m16n8k8.row.col.f32.f16.f16.f32 {%0,%1,%2,%3}, {%4,%5}, {%6}, {%0,%1,%2,%3};\n"
+                     : "+f"(c0), "+f"(c1), "+f"(c2), "+f"(c3)
+                     : "r"(a0), "r"(a1), "r"(b0));
+    }
+    if (lane < 4) { float* p = partials + (size_t) (tile * ks + ks_i) * 8; p[t2] = c0; p[t2 + 1] = c1; }
+}
+
+// same, but W stays bf16 and converts in-register (fully fair per-call cost)
+__global__ void tc_inline(const uint16_t* __restrict__ x_b, const uint16_t* __restrict__ w_b,
+                          float* __restrict__ partials, long long n_in, long long n_out, int ks) {
+    const int tile = blockIdx.x, ks_i = blockIdx.y, lane = threadIdx.x & 31;
+    const long long n_base = (long long) tile * 8;
+    const long long k_per = n_in / (long long) ks;
+    const long long k0 = (long long) ks_i * k_per;
+    const int t2 = 2 * (lane & 3);
+    float c0 = 0.f, c1 = 0.f, c2 = 0.f, c3 = 0.f;
+    for (long long kk0 = k0; kk0 < k0 + k_per; kk0 += 8) {
+        unsigned xh = (unsigned) x_b[kk0 + t2] | ((unsigned) x_b[kk0 + t2 + 1] << 16);
+        __half2 ha = __floats2half2_rn(bf16_f32((uint16_t) (xh & 0xFFFF)), bf16_f32((uint16_t) (xh >> 16)));
+        __half2 hb = __floats2half2_rn(bf16_f32((uint16_t) (xh & 0xFFFF)), bf16_f32((uint16_t) (xh >> 16)));
+        unsigned a0 = *reinterpret_cast<unsigned*>(&ha);
+        unsigned a1 = *reinterpret_cast<unsigned*>(&hb);
+        const uint16_t* wbv = w_b + (n_base + (lane >> 2)) * n_in + kk0 + t2;
+        __half2 wbh = __floats2half2_rn(bf16_f32(wbv[0]), bf16_f32(wbv[1]));
+        unsigned b0 = *reinterpret_cast<unsigned*>(&wbh);
+        asm volatile("mma.sync.aligned.m16n8k8.row.col.f32.f16.f16.f32 {%0,%1,%2,%3}, {%4,%5}, {%6}, {%0,%1,%2,%3};\n"
+                     : "+f"(c0), "+f"(c1), "+f"(c2), "+f"(c3)
+                     : "r"(a0), "r"(a1), "r"(b0));
+    }
+    if (lane < 4) { float* p = partials + (size_t) (tile * ks + ks_i) * 8; p[t2] = c0; p[t2 + 1] = c1; }
+}
+
+__global__ void tc_reduce(const float* __restrict__ partials, float* __restrict__ y, long long n_out, int ks) {
+    const long long o = blockIdx.x * blockDim.x + threadIdx.x;
+    if (o >= n_out) return;
+    float s = 0.f;
+    for (int q = 0; q < ks; ++q) s += partials[(size_t) ((o >> 3) * ks + q) * 8 + (o & 7)];
+    y[o] = s;
+}
+
+// variant with the OTHER candidate A layout: a0 = {A[gid][2t], A[gid+8][2t]} (same k-col, two rows),
+// a1 = same for col 2t+1. Broadcast => both halves of a0 hold x[2t], both halves of a1 hold x[2t+1].
+__global__ void tc_dupcol(const uint16_t* __restrict__ x_b, const uint16_t* __restrict__ w_b,
+                          float* __restrict__ partials, long long n_in, long long n_out, int ks) {
+    const int tile = blockIdx.x, ks_i = blockIdx.y, lane = threadIdx.x & 31;
+    const long long n_base = (long long) tile * 8;
+    const long long k_per = n_in / (long long) ks;
+    const long long k0 = (long long) ks_i * k_per;
+    const int t2 = 2 * (lane & 3);
+    float c0 = 0.f, c1 = 0.f, c2 = 0.f, c3 = 0.f;
+    for (long long kk0 = k0; kk0 < k0 + k_per; kk0 += 8) {
+        __half2 ha = __floats2half2_rn(bf16_f32(x_b[kk0 + t2]), bf16_f32(x_b[kk0 + t2]));
+        __half2 hb = __floats2half2_rn(bf16_f32(x_b[kk0 + t2 + 1]), bf16_f32(x_b[kk0 + t2 + 1]));
+        unsigned a0 = *reinterpret_cast<unsigned*>(&ha);
+        unsigned a1 = *reinterpret_cast<unsigned*>(&hb);
+        const uint16_t* wbv = w_b + (n_base + (lane >> 2)) * n_in + kk0 + t2;
+        __half2 wbh = __floats2half2_rn(bf16_f32(wbv[0]), bf16_f32(wbv[1]));
+        unsigned b0 = *reinterpret_cast<unsigned*>(&wbh);
+        asm volatile("mma.sync.aligned.m16n8k8.row.col.f32.f16.f16.f32 {%0,%1,%2,%3}, {%4,%5}, {%6}, {%0,%1,%2,%3};\n"
+                     : "+f"(c0), "+f"(c1), "+f"(c2), "+f"(c3)
+                     : "r"(a0), "r"(a1), "r"(b0));
+    }
+    if (lane < 4) { float* p = partials + (size_t) (tile * ks + ks_i) * 8; p[t2] = c0; p[t2 + 1] = c1; }
+}
+
+// FUSED, ks=1 only: with a single k-fragment the partial IS the answer -> write y directly, no reduce kernel,
+// no scratch. Fully inline bf16 W conversion (fairest per-call TC cost).
+__global__ void tc_fused(const uint16_t* __restrict__ x_b, const uint16_t* __restrict__ w_b,
+                         float* __restrict__ y, long long n_in, long long n_out) {
+    const int tile = blockIdx.x, lane = threadIdx.x & 31;
+    const long long n_base = (long long) tile * 8;
+    const int t2 = 2 * (lane & 3);
+    float c0 = 0.f, c1 = 0.f, c2 = 0.f, c3 = 0.f;
+    for (long long kk0 = 0; kk0 < n_in; kk0 += 8) {
+        __half2 ha = __floats2half2_rn(bf16_f32(x_b[kk0 + t2]), bf16_f32(x_b[kk0 + t2 + 1]));
+        __half2 hb = ha;   // broadcast: row gid+8 = row gid (single-token GEMV)
+        unsigned a0 = *reinterpret_cast<unsigned*>(&ha);
+        unsigned a1 = *reinterpret_cast<unsigned*>(&hb);
+        const uint16_t* wbv = w_b + (n_base + (lane >> 2)) * n_in + kk0 + t2;
+        __half2 wbh = __floats2half2_rn(bf16_f32(wbv[0]), bf16_f32(wbv[1]));
+        unsigned b0 = *reinterpret_cast<unsigned*>(&wbh);
+        asm volatile("mma.sync.aligned.m16n8k8.row.col.f32.f16.f16.f32 {%0,%1,%2,%3}, {%4,%5}, {%6}, {%0,%1,%2,%3};\n"
+                     : "+f"(c0), "+f"(c1), "+f"(c2), "+f"(c3)
+                     : "r"(a0), "r"(a1), "r"(b0));
+    }
+    if (lane < 4) { y[n_base + t2] = c0; y[n_base + t2 + 1] = c1; }
+}
+
+int main() {
+    CHECK(cudaSetDevice(0));
+    cudaDeviceProp prop; CHECK(cudaGetDeviceProperties(&prop, 0));
+    printf("device: %s, SMs %d, capability %d.%d\n", prop.name, prop.multiProcessorCount, prop.major, prop.minor);
+
+    struct Shape { const char* name; long long n_in, n_out; };
+    Shape shapes[] = {
+        {"ssm_alpha/beta [2560,48]", 2560, 48},
+        {"indexer.k_proj [2560,128]", 2560, 128},
+        {"q_proj/router [2560,512]", 2560, 512},
+        {"ple_value [2560,2560]", 2560, 2560},
+    };
+    auto rng = [](unsigned& s) { s = s * 1664525u + 1013904223u; return (float) ((s >> 8) & 0xFFFF) / 65535.0f - 0.5f; };
+    const int ITERS = 2000, WARM = 300;
+
+    printf("\n%-26s %13s | %s\n", "shape", "baseline us", "tc variants (pc = W preconverted, in = inline conversion, fused = no reduce), speedup vs baseline");
+    printf("%s\n", std::string(150, '-').c_str());
+
+    for (auto& sh : shapes) {
+        const long long n_in = sh.n_in, n_out = sh.n_out;
+        const size_t wn = (size_t) n_out * n_in;
+        std::vector<uint16_t> hw(wn), hx(n_in), hf(wn);
+        std::vector<float> hyb(n_out), hypc(n_out), hyin(n_out), hyfu(n_out);
+        std::vector<double> href(n_out);
+        unsigned seed = 991u + (unsigned) (n_out & 0xFFF);
+        for (size_t i = 0; i < wn; ++i) hw[i] = f32_bf16_host(rng(seed) * 0.3f);
+        for (long long i = 0; i < n_in; ++i) hx[i] = f32_bf16_host(rng(seed) * 3.0f);
+        for (long long o = 0; o < n_out; ++o) {
+            double s = 0.0;
+            for (long long i = 0; i < n_in; ++i) s += (double) bf16_f32_host(hx[i]) * (double) bf16_f32_host(hw[(size_t) o * n_in + i]);
+            href[o] = s;
+        }
+        // preconvert W to fp16 (bf16 -> fp16 is exact within fp16 range)
+        for (size_t i = 0; i < wn; ++i) { __half h = __float2half_rn(bf16_f32_host(hw[i])); memcpy(&hf[i], &h, 2); }
+
+        uint16_t *dx, *dw, *dwf; float *dy, *dyp;
+        CHECK(cudaMalloc(&dx, n_in * 2));
+        CHECK(cudaMalloc(&dw, wn * 2)); CHECK(cudaMalloc(&dwf, wn * 2));
+        CHECK(cudaMalloc(&dy, n_out * 4));
+        CHECK(cudaMalloc(&dyp, (size_t)(n_out / 8) * 65 * 8 * 4));
+        CHECK(cudaMemcpy(dx, hx.data(), n_in * 2, cudaMemcpyHostToDevice));
+        CHECK(cudaMemcpy(dw, hw.data(), wn * 2, cudaMemcpyHostToDevice));
+        CHECK(cudaMemcpy(dwf, hf.data(), wn * 2, cudaMemcpyHostToDevice));
+
+        // one-time W conversion cost (host->device, reported separately; the engine would fold this
+        // into the weight load exactly like Gemm::bf16 does for sm<80)
+        {
+            cudaEvent_t e0, e1; CHECK(cudaEventCreate(&e0)); CHECK(cudaEventCreate(&e1));
+            CHECK(cudaEventRecord(e0));
+            CHECK(cudaMemcpy(dwf, hf.data(), wn * 2, cudaMemcpyHostToDevice));
+            CHECK(cudaEventRecord(e1)); CHECK(cudaEventSynchronize(e1));
+            float ms = 0; CHECK(cudaEventElapsedTime(&ms, e0, e1));
+            printf("%-26s one-time W convert: %.2f ms (%.1f MB), tiles=%lld\n", sh.name, ms, wn * 2 / 1e6, n_out / 8);
+        }
+
+        int tiles = (int) (n_out / 8);
+        // valid ks must divide n_in so each k-chunk is a multiple of 8; sweep ALL of them, report the best
+        const int cands[11] = {1, 2, 4, 5, 8, 10, 16, 20, 32, 40, 64};
+
+        auto timeit = [&](const char* tag, const std::function<void()>& fn) -> double {
+            for (int i = 0; i < WARM; ++i) fn();
+            CHECK(cudaDeviceSynchronize());
+            double best = 1e30;
+            for (int rep = 0; rep < 3; ++rep) {
+                cudaEvent_t e0, e1; CHECK(cudaEventCreate(&e0)); CHECK(cudaEventCreate(&e1));
+                CHECK(cudaEventRecord(e0));
+                for (int i = 0; i < ITERS; ++i) fn();
+                CHECK(cudaEventRecord(e1)); CHECK(cudaEventSynchronize(e1));
+                float ms = 0; CHECK(cudaEventElapsedTime(&ms, e0, e1));
+                double us = ms * 1000.0 / ITERS;
+                if (us < best) best = us;
+            }
+            (void) tag; return best;
+        };
+
+        double t_base = timeit("base", [&]{ gemv_warp<<<(unsigned) ((n_out + 7) / 8), 256>>>(dx, dw, dy, n_in, n_out); });
+        gemv_warp<<<(unsigned) ((n_out + 7) / 8), 256>>>(dx, dw, dy, n_in, n_out);
+        CHECK(cudaMemcpy(hyb.data(), dy, n_out * 4, cudaMemcpyDeviceToHost));
+
+        // occupancy sweep over k-chunk count for the preconverted-W TC kernel
+        int best_ks = 1; double t_pc = 1e30;
+        for (int i = 0; i < 11; ++i) {
+            const int ksv = cands[i];
+            if (n_in % ksv != 0) continue;
+            dim3 g((unsigned) tiles, (unsigned) ksv);
+            double t = timeit("pcs", [&]{ tc_preconv<<<g, 32>>>(dx, dwf, dyp, n_in, n_out, ksv);
+                                          tc_reduce<<<(unsigned) ((n_out + 255) / 256), 256>>>(dyp, dy, n_out, ksv); });
+            if (t < t_pc) { t_pc = t; best_ks = ksv; }
+        }
+        dim3 gtc((unsigned) tiles, (unsigned) best_ks);
+        tc_preconv<<<gtc, 32>>>(dx, dwf, dyp, n_in, n_out, best_ks);
+        tc_reduce<<<(unsigned) ((n_out + 255) / 256), 256>>>(dyp, dy, n_out, best_ks);
+        CHECK(cudaMemcpy(hypc.data(), dy, n_out * 4, cudaMemcpyDeviceToHost));
+
+        double t_ti = timeit("tci", [&]{ tc_inline<<<gtc, 32>>>(dx, dw, dyp, n_in, n_out, best_ks);
+                                         tc_reduce<<<(unsigned) ((n_out + 255) / 256), 256>>>(dyp, dy, n_out, best_ks); });
+        tc_inline<<<gtc, 32>>>(dx, dw, dyp, n_in, n_out, best_ks);
+        tc_reduce<<<(unsigned) ((n_out + 255) / 256), 256>>>(dyp, dy, n_out, best_ks);
+        CHECK(cudaMemcpy(hyin.data(), dy, n_out * 4, cudaMemcpyDeviceToHost));
+
+        double t_fu = timeit("tcf", [&]{ tc_fused<<<(unsigned) tiles, 32>>>(dx, dw, dy, n_in, n_out); });
+        tc_fused<<<(unsigned) tiles, 32>>>(dx, dw, dy, n_in, n_out);
+        CHECK(cudaMemcpy(hyfu.data(), dy, n_out * 4, cudaMemcpyDeviceToHost));
+
+        double best_tc = t_pc; const char* best_var = "preconv";
+        if (t_ti < best_tc) { best_tc = t_ti; best_var = "inline"; }
+        if (t_fu < best_tc) { best_tc = t_fu; best_var = "fused"; }
+
+        double e_tc = 0, e_base = 0, sc = 0;
+        if (strcmp(best_var, "preconv") == 0) {
+            for (long long o = 0; o < n_out; ++o) { e_tc = std::max(e_tc, std::fabs((double) hypc[o] - href[o])); }
+        } else if (strcmp(best_var, "inline") == 0) {
+            for (long long o = 0; o < n_out; ++o) { e_tc = std::max(e_tc, std::fabs((double) hyin[o] - href[o])); }
+        } else {
+            for (long long o = 0; o < n_out; ++o) { e_tc = std::max(e_tc, std::fabs((double) hyfu[o] - href[o])); }
+        }
+        for (long long o = 0; o < n_out; ++o) {
+            e_base = std::max(e_base, std::fabs((double) hyb[o] - href[o]));
+            sc = std::max(sc, std::fabs(href[o]));
+        }
+        double gb_base = (double) (wn * 2) / t_base / 1e3;
+        double gb_tc = (double) (wn * 2) / best_tc / 1e3;
+        printf("%-26s %13.3f | pc %13.3f (ks=%d) | in %13.3f | fused %13.3f | BEST %s %7.3fx | maxerr tc %.2e base %.2e  (scale %.1f, BW base %.0f tc %.0f GB/s)\n",
+               sh.name, t_base, t_pc, best_ks, t_ti, t_fu, best_var, t_base / best_tc,
+               e_tc, e_base, sc, gb_base, gb_tc);
+
+        CHECK(cudaFree(dx)); CHECK(cudaFree(dw)); CHECK(cudaFree(dwf)); CHECK(cudaFree(dy)); CHECK(cudaFree(dyp));
+    }
+
+    {   // bf16 -> fp16 exactness scan
+        unsigned seed = 777u; double worst = 0; int bad = 0, n = 2000000;
+        for (int i = 0; i < n; ++i) {
+            float f = (rng(seed) * 2.0f - 1.0f) * 100.0f;
+            uint16_t b = f32_bf16_host(f);
+            __half h = __float2half_rn(bf16_f32_host(b));
+            if ((double) __half2float(h) != (double) bf16_f32_host(b)) {
+                bad++; worst = std::max(worst, std::fabs((double) __half2float(h) - (double) bf16_f32_host(b)));
+            }
+        }
+        printf("\nbf16->fp16 conversion exactness: %d / %d inexact, worst %.3g\n", bad, n, worst);
+    }
+    printf("BENCH_DONE\n");
+    return 0;
+}

cmake/hip_backend.cmake

@@ -1,23 +1,28 @@
-# Opt-in HIP configuration. Strata's CUDA-shaped kernels target wave32 RDNA3 / RDNA4 (64 KiB LDS per workgroup,
-# the signed dot4 instruction). CMake/compiler discovery stays machine-independent; pass CMAKE_HIP_COMPILER when it
+# Opt-in HIP configuration. Strata's CUDA-shaped kernels target wave32 RDNA2 / RDNA3 / RDNA4 (64 KiB LDS per
+# workgroup, a signed dot4 instruction). CMake/compiler discovery stays machine-independent; pass CMAKE_HIP_COMPILER when it
 # is not on PATH.
 if(NOT DEFINED CMAKE_HIP_ARCHITECTURES OR CMAKE_HIP_ARCHITECTURES STREQUAL "")
   set(CMAKE_HIP_ARCHITECTURES gfx1100 CACHE STRING "Strata HIP target architecture(s), e.g. gfx1100 or gfx1100;gfx1201")
 endif()
-# Validated on real cards: gfx1100 (RX 7900 XT / XTX) and gfx1201 (RX 9070 / 9070 XT, Radeon AI PRO R9700).
-# The other RDNA3 / RDNA4 wave32 chips have the same LDS limit and dot4 instruction and build the same code, but
-# the maintainers have not run them (community reports: gfx1102 #192, gfx1200 #176).
+# Validated on real cards: gfx1100 (RX 7900 XT / XTX) and gfx1201 (RX 9070 / 9070 XT, Radeon AI PRO R9700) by the
+# maintainers; gfx1101 (RX 7800 XT, #254) and gfx1200 (RX 9060 XT, #256) by their owners. gfx1102 (RX 7600) has the
+# same LDS limit and dot4 instruction and passed ctest (#192), but no model run has been reported yet. RDNA2 gfx1030 (RX 6800 / 6900) has the
+# same LDS limit and wave32 but an older dot4 instruction (v_dot4_i32_i8, hip_compat/intrinsics.hpp); a community
+# report ran it (#311), the maintainers have not.
 set(_strata_hip_validated gfx1100 gfx1201)
-set(_strata_hip_unvalidated gfx1101 gfx1102 gfx1200)
+set(_strata_hip_community gfx1101 gfx1200)
+set(_strata_hip_unvalidated gfx1102 gfx1030)
 set(STRATA_HIP_ARCH_LIST "")
 foreach(_arch IN LISTS CMAKE_HIP_ARCHITECTURES)
   string(REGEX REPLACE ":.*$" "" _base "${_arch}")      # gfx1100:xnack- -> gfx1100
   if(_base IN_LIST _strata_hip_validated)
+  elseif(_base IN_LIST _strata_hip_community)
+    message(STATUS "Strata HIP: ${_base} was validated by community reports (docs/AMD_HIP.md)")
   elseif(_base IN_LIST _strata_hip_unvalidated)
     message(WARNING "Strata HIP: ${_base} builds, but it is not validated on a real card yet; please report results")
   else()
     message(FATAL_ERROR
-      "Strata HIP supports wave32 gfx1100 and gfx1201 (unvalidated: ${_strata_hip_unvalidated}); "
+      "Strata HIP supports wave32 gfx1100, gfx1101, gfx1200 and gfx1201 (unvalidated: ${_strata_hip_unvalidated}); "
       "CMAKE_HIP_ARCHITECTURES is '${CMAKE_HIP_ARCHITECTURES}'")
   endif()
   list(APPEND STRATA_HIP_ARCH_LIST "${_base}")

docs/AMD_HIP.md

@@ -1,12 +1,13 @@
-# Experimental AMD HIP backend (gfx1100, gfx1201)
+# Experimental AMD HIP backend (gfx1100, gfx1101, gfx1200, gfx1201, gfx1030)
 
 This is a Linux source build for the RX 7900 XT / XTX (RDNA3, gfx1100) and the
-RX 9070 / 9070 XT / Radeon AI PRO R9700 (RDNA4, gfx1201; see [RDNA4](#rdna4-gfx1201)).
-It is opt-in; the NVIDIA installer and CUDA build remain the default. Other AMD
+RX 9070 / 9070 XT / Radeon AI PRO R9700 (RDNA4, gfx1201; see [RDNA4](#rdna4-gfx1201)). The RX 7800 XT / 7700 XT
+(gfx1101) and the RX 9060 XT (gfx1200) were validated by their owners (see [Community-validated
+cards](#community-validated-cards)); the RX 6800 / 6900 series (RDNA2, gfx1030) builds and runs too, reported by a community machine and not yet validated by the maintainers (see [RDNA2](#rdna2-gfx1030)). It is opt-in; the NVIDIA installer and CUDA build remain the default. Other AMD
 architectures, wave64, Windows HIP, and mixed AMD/NVIDIA execution are outside this contribution.
 
 The backend maps the CUDA-shaped runtime and BLAS calls to HIP/hipBLAS, uses
-RDNA3/RDNA4's signed integer dot instruction for quantized kernels, and supplies
+RDNA2/RDNA3/RDNA4's signed integer dot instruction for quantized kernels, and supplies
 wave32 shuffle/packed-byte operations. CUDA-only QSA matrix instructions have
 an ordered FP32 fallback. Prefill supports both dequantization plus hipBLAS GEMM and opt-in HIP ggml MMQ.
 An optional, calibrated hipBLASLt path accelerates dense projections (per-architecture tables in `tools/hip`).
@@ -15,8 +16,8 @@ This does not claim bit-identical model answers across backends. See
 
 ## Install with setup (recommended)
 
-On Linux with an RX 7900 XT / XTX or an RX 9070 / 9070 XT / Radeon AI PRO R9700 and the kernel's amdgpu driver
-(no ROCm install needed):
+On Linux with an RX 7900 XT / XTX, RX 7800 XT / 7700 XT, RX 9060 XT, RX 9070 / 9070 XT or Radeon AI PRO R9700 and
+the kernel's amdgpu driver (no ROCm install needed):
 
 ```sh
 ./setup.sh --backend hip
@@ -26,8 +27,10 @@ On Linux with an RX 7900 XT / XTX or an RX 9070 / 9070 XT / Radeon AI PRO R9700
   supported. On a PC without an NVIDIA card Strata can use, `--backend hip` is chosen automatically.
 - **ROCm:** a system ROCm 7 in `/opt/rocm` (or `$ROCM_PATH`) with hipcc and hipBLAS is used when present. Otherwise
   (or when it is older than 7.0) ROCm is installed into `.venv` from AMD's TheRock wheels (~10 GB, no sudo), pinned
-  to the version this backend was tested with, from the card family's index: `gfx110X-dgpu` for gfx1100,
-  `gfx120X-all` for gfx1201 (`STRATA_ROCM_VERSION` / `STRATA_ROCM_INDEX` override them).
+  to the version this backend was tested with, from the card family's index: `gfx110X-dgpu` for gfx1100 / gfx1101,
+  `gfx120X-all` for gfx1200 / gfx1201, `gfx103X-all` for gfx1030 (`STRATA_ROCM_VERSION` /
+  `STRATA_ROCM_INDEX` override them; the gfx1030 index is not checked to carry the pinned version: a system
+  ROCm 7 is the tested path there).
 - **Engine:** compiled on your PC for the card's architecture (10-20 minutes, once; again after a `git pull` that
   changes it, or when you pick a card of another architecture). This needs a C++ compiler and git
   (`sudo apt install build-essential git`).
@@ -35,7 +38,14 @@ On Linux with an RX 7900 XT / XTX or an RX 9070 / 9070 XT / Radeon AI PRO R9700
   card's architecture and the installed hipBLASLt version (read from `hipblaslt-version.h`; 1.2.0 is `100200`).
   Otherwise it says so and the prompt's dense matrix products use plain hipBLAS (slower prompts, same answers).
   A table's solution ids are valid only for that pair, and the engine refuses any other table.
-- **Limits for now:** one GPU, no images, no calibration. The Monitor shows no GPU statistics.
+- **Several cards:** setup takes one card (the one with the most VRAM, or `--gpu N`) unless you name more:
+  `./setup.sh --backend hip --gpus 1,0` splits the model's layers across them, the first one the main card (numbers
+  as setup lists them; `--gpus all` = every supported card, the most VRAM first). Every chosen card must be one of the
+  architectures above; the engine is compiled for each of them (cards of two families, e.g. gfx1100 + gfx1201, need
+  a system ROCm 7: AMD's wheels hold one family). A split pays only when no single card holds the model's experts
+  (see RDNA4 below).
+- **Limits for now:** images only through the CPU encoder (`--vision cpu`, 0.1.32), no calibration. The Monitor
+  shows the card's load, VRAM, temperature and power from Linux sysfs (0.1.32).
 
 The rest of setup is the same as on NVIDIA: the model download, the start script, the server.
 
@@ -54,9 +64,9 @@ cmake -S . -B build-hip \
 cmake --build build-hip --target strata -j2
 ```
 
-`CMAKE_HIP_ARCHITECTURES` is `gfx1100`, `gfx1201`, or a list such as `"gfx1100;gfx1201"` (one binary for both).
-gfx1101, gfx1102 and gfx1200 (the same wave32, 64 KiB LDS and dot4 instruction) build with a warning: they have
-not been validated on a real card here. At startup the engine and `strata-device` compare each GPU they use
+`CMAKE_HIP_ARCHITECTURES` is `gfx1100`, `gfx1101`, `gfx1200`, `gfx1201`, or a list such as `"gfx1100;gfx1201"`
+(one binary for both). gfx1102 (the same wave32, 64 KiB LDS and dot4 instruction) builds with a warning: it passed
+ctest (#192) but no model run has been reported; so does gfx1030 (RDNA2: the older `v_dot4_i32_i8`, a community run in #311). At startup the engine and `strata-device` compare each GPU they use
 (`gcnArchName` up to the `:` feature suffix) with the architectures the binary was compiled for, and require
 wave32. A binary carried to another card stops with the card's name, its architecture and the build's list,
 instead of failing later with "invalid device function".
@@ -114,8 +124,7 @@ The 4K context is a smoke-test starting point, not a model limit. The expert cac
 sizes itself automatically and leaves 1 GiB of VRAM headroom.
 
 The installer supports this backend (see "Install with setup" above). The vision helper is NVIDIA-only for now.
-Setup installs one AMD card; the engine's layer split also runs on two AMD cards when the config is written by hand
-(see RDNA4 below).
+Setup installs one AMD card, or several with `--gpus` (the engine's layer split; see RDNA4 below).
 
 ## RDNA4 (gfx1201)
 
@@ -145,12 +154,16 @@ an RX 9070 XT 16 GB and a Radeon AI PRO R9700 32 GB (both gfx1201), a Ryzen 9 39
   | RX 9070 XT 16 GB | 4,931 slots, 9.4 GiB | 15.6 GiB | 782 tok/s | 30.8 tok/s | 1,235 tok/s | 35.4 tok/s |
   | R9700, TheRock 7.10 wheels | 12,288 slots | | 957 tok/s | 44.2 tok/s | 1,284 tok/s | 43.9 tok/s |
 
-  The engine's resident memory was about 26 GB in every run.
-- **hipBLASLt:** there is no gfx1201 table in `tools/hip`. A table calibrated on the R9700 at the engine's shapes
-  (hipBLASLt 1.4.1; 0.98-1.76x per GEMM over hipBLAS) changed the end-to-end prompt speed by 0-3%, within noise,
-  so none is shipped: on gfx1201 the plain hipBLAS path is already close.
+  The engine's resident memory was about 26 GB in every run. Since 0.1.31 `__byte_perm` is one `v_perm_b32` and the
+  packed byte subtracts/compare work on four lanes at once (#262, ttio2tech): decode +15% on the R9700 (46.0 -> 53.0
+  tok/s on a 4K prompt, 52.0 -> 60.5 warm) and +5-7% on the 9070 XT, prompts unchanged, the same tokens.
+- **hipBLASLt:** the validation above used hipBLASLt 1.4.1. A table calibrated on the R9700 at the engine's shapes
+  with that version (0.98-1.76x per GEMM over hipBLAS) changed the end-to-end prompt speed by 0-3%, within noise,
+  so none was shipped for it: there the plain hipBLAS path is already close. For hipBLASLt 1.5.0 (ROCm
+  10.2.0a nightly) `tools/hip/gfx1201-hipblaslt-100500.txt` is shipped (see "Tuning table" below); on one R9700 it
+  measured +3.9% prompt speed on 4,210-token prompts (1,590 vs 1,531 tok/s), a modest gain.
 - **Both cards in one run (layer split, engine 0.1.30):** the config's `"backend": "hip", "gpu": [1, 0]` (R9700
-  first) runs through `serve/server.py`; setup does not offer it yet. Auto split put layers 0-27 on the R9700 and
+  first) runs through `serve/server.py` (setup writes it with `--gpus 1,0` since 0.1.31). Auto split put layers 0-27 on the R9700 and
   28-47 on the 9070 XT. With every expert on the GPUs the split gives exactly the tokens of the R9700 alone (4K and
   16K prompts); checkpoints on the split (second turn, rewind, a prompt sharing a prefix, a cancelled prompt
   retried) give exactly the tokens of a fresh read. The conversation cache refuses a split at start (exit 2).
@@ -162,12 +175,88 @@ an RX 9070 XT 16 GB and a Radeon AI PRO R9700 32 GB (both gfx1201), a Ryzen 9 39
   | R9700 + 9070 XT, auto split | 1,384 tok/s | 1,906 tok/s | 42-43 tok/s |
   | RX 9070 XT alone | 1,016 tok/s | 1,519 tok/s | 38-40 tok/s |
 
-  A split is worth it when no single card holds the model's experts. On Linux the split pins at most 8 GiB of the
-  expert arena (a Windows limit that also applies here).
+  A split is worth it when no single card holds the model's experts. Since 0.1.31 a split pins the whole expert
+  arena on Linux (the 8 GiB cap is for Windows/WSL2 only, #253): with an expert cache of 1,500 on the R9700 (most
+  experts streamed) the split read a 16K prompt at 1,803 tok/s instead of 1,287, with the same tokens in 5 + 5
+  starts. `"split_skip_if_fits": true` in the config (0.1.31, opt-in) runs such a pair on the first card alone when it
+  holds every profiled expert: with the R9700 first, 4K prompts 1,776 tok/s (split: 1,244) and decode ~60 tok/s
+  (split: ~51), the tokens of the R9700 alone (docs/MULTI_GPU.md).
+- **Speed switches (engine 0.1.32, measured on the R9700 / 9070 XT with the Coder IQ1_M pack):**
+  - the MoE router (`router_top10`) runs a HIP kernel without its serial FP64 sum and block barriers by default: the
+    same ids and weights bit for bit (`hip_router_fast` checks 65,536 rows), 39 -> 9-12 us per call, decode
+    62.4 -> 70.0 tok/s on the R9700 and +4% on the 9070 XT, the same greedy tokens (5 + 5 starts).
+    `STRATA_HIP_ROUTER_OLD=1` runs the portable kernel.
+  - `STRATA_HIP_WMMA=1` (opt-in, gfx12 only, int8 KV): the prompt path's QSA attention on RDNA4 matrix cores
+    (`v_wmma_f32_16x16x16_f16`, FP16 hi + lo halves like the CUDA tensor-core kernel; `hip_prompt_attn_wmma` bounds it
+    against the FP32 kernel and FP64). 7.2-7.5x the portable kernel; R9700 prompts 4K 1,784 -> 2,427 tok/s, 16K
+    1,797 -> 2,700. Not bitwise: greedy text differs from token ~50 on, as with the CUDA tensor-core attention. With it
+    the prompt path's expert ring is 96 slots (as STRATA_PREFILL_RING=96): with the default 384 the 9070 XT's 4K
+    prompts fell to 718 tok/s; with 96 they gain (1,017 -> 1,211; 16K 1,518 -> 2,032). PR #329
+    (bsorensen110) contributed an equivalent gfx12 WMMA kernel of the same speed (within 1%); this one also masks KV
+    pages that KV streaming has not made resident, as the decode kernel does.
+  - The prompt path's QSA top-k picks its kernel by the blocks a query actually has, not the cache's capacity (#337,
+    bsorensen110): the same ids, on by default with AMD (NVIDIA keeps its capacity rule: there the 64K prompts read
+    1-3% slower with it). `STRATA_SELECT_WMMA=1` (opt-in, gfx12) adds #337's
+    matrix-core block scorer; it selects slightly differently (254 of 256 queries the same) and gained +1.5% on 16K
+    prompts at a 262K context on the R9700.
 - **Known:** rarely (about 1 start in 10) a HIP run's greedy output differs from another start's at some token, on
   one card or two and on engine 0.1.29 as well; not yet explained.
-- **Not validated:** gfx1200 (RX 9060 XT; a community report is #176), images, long contexts beyond 16K,
-  answer-quality benchmarks.
+- **Not validated:** images, long contexts beyond 16K, answer-quality benchmarks.
+
+## Community-validated cards
+
+Run by their owners, not on the maintainers' machines; setup accepts them like gfx1100 / gfx1201. No hipBLASLt table
+is shipped for them (make one with [Tuning table](#tuning-table) and compare the prompt speed with and without it).
+
+- **gfx1101, RX 7800 XT 16 GB** (jhohertz, #254; engine 0.1.29, Ryzen 9 5950X, 121 GiB RAM, system ROCm with
+  hipBLASLt 1.4.1): `./setup.sh --backend hip` detected the card and compiled the engine; `strata-device --selftest`
+  passed; ctest 30/32 (`ple_parity` needs the Q2_0 fixture, `platform_memory_test` the memlock limit). Coder IQ1_M,
+  64K context, MTP, with a table the owner calibrated: fresh prompts of 4K-9K tokens at 898-953 tok/s, decode
+  38-44 tok/s (128 tokens).
+- **gfx1200, RX 9060 XT 16 GB** (Efeisot, #256 after #176; engine 0.1.29, Ryzen 9 7950X, 64 GiB RAM, ROCm 7.2 with
+  hipBLASLt 1.2.2): ctest 32/32 (without `ple_parity` and `platform_memory_test`). Coder IQ1_M, greedy, MTP
+  `--spec 4 --spec-min-p 0.5`, with a table the owner calibrated:
+
+  | prompt | prompt speed | decode |
+  |---|---|---|
+  | 2,374 tokens | 540 tok/s | 27.1 tok/s (0.69 draft acceptance; 31.0 at 1.00) |
+  | 65K (`--kv int8 --kv-resident 65536 --prefill 16384`) | 748-753 tok/s | 26-40 tok/s by acceptance |
+  | 130K (the same flags) | 725 tok/s | - |
+
+  Greedy output was the same across runs. For comparison, llama.cpp's HIP build measured 20 tok/s decode and
+  450 tok/s prompt on that card.
+
+## RDNA2 (gfx1030)
+
+The RX 6800 / 6800 XT / 6900 XT / 6950 XT run the same kernels: wave32 and 64 KiB of LDS per workgroup. The one
+difference is the dot instruction: RDNA2 has no `v_dot4_i32_iu8` (gfx11 and newer), so
+`dp4a` uses the plain signed `v_dot4_i32_i8` through `__builtin_amdgcn_sdot4`, which compiles to a single
+`v_dot4c_i32_i8` (same signed x signed byte products, modulo 2^32). There is no WMMA; the QSA scorer takes the
+same ordered FP32 fallback as gfx1100. CMake lists gfx1030 as unvalidated (the build warns) until a maintainer has
+run it; the report below is from a community machine: an RX 6900 XT 16 GB (gfx1030), an i7-13700KF (8 P-cores and
+8 E-cores, AVX2, no AVX-512), 63 GB RAM, NixOS, ROCm 7.2.3 from nixpkgs (clang 22, hipBLAS 3.2).
+
+- **Build and tests** (engine 0.1.26): a complete HIP build for gfx1030, made by hand with cmake and ROCm's own
+  `clang++` (the nixpkgs ROCm is not an `/opt/rocm` tree, so setup's `build_engine_hip` was not exercised; the
+  binary was placed in `engine/` for setup to use). All 28 registered ctest tests pass on the card, including
+  `hip_device_selftest`. An engine 0.1.30 build of this branch configures, builds and runs clean on the same card;
+  the speeds below are measured on it.
+- **End to end** (Swift 1.5 IQ3_XXS, `--context 131072` with `--kv-resident 32768 --adapt-every 1
+  --vram-reserve-mib 1024`, 200 greedy tokens): 38-42 tok/s decode with the default 15 CPU pool workers (one per
+  physical core except the host thread; 8 workers: 36; 24 = every logical core: 27), consistent even with the
+  131,072-token context full.
+- **Prefill** (the same 15-worker configuration): 246 tok/s on a 2,000-token prompt, 330-339 tok/s at the auto
+  8,192-token chunk (7,997 and 15,967 tokens; time to first token 8.9 and 48.3 s), 133 tok/s on a 522-token
+  prompt - short prompts are fixed overhead (19-25 tok/s on 34 tokens). Decode after a 16K prefill holds at
+  45.5 tok/s.
+- **16 GB card:** the expert cache holds 6,310 slots (10.2 GiB) at 16K context and 5,247 slots at 131,072, where
+  setup keeps the KV cache in VRAM because IQ3_XXS needs about 60 GB of RAM plus the cache to stream it. The
+  `--pcie-frac 0` and `--adapt-every 0` of the 7900 XTX configuration in
+  [AMD_HIP_PERFORMANCE.md](AMD_HIP_PERFORMANCE.md) cost 8.6 and 13.6 tok/s here (30 with the defaults): keep the
+  defaults on a 16 GB card.
+- **hipBLASLt:** ROCm's hipBLASLt ships no gfx1030 kernels, so there is no table and the plain hipBLAS path runs.
+- **Not validated:** gfx1031 / gfx1032 (the same `dp4a` path, no hardware report), setup's own build path and the
+  `gfx103X-all` wheels on gfx1030, images, answer-quality benchmarks.
 
 ## Tuning table
 
@@ -185,6 +274,20 @@ The file's second line names the architecture and version (`STRATA_HIPBLASLT_TUN
 as `tools/hip/<arch>-hipblaslt-<version>.txt` for setup, or point `STRATA_HIPBLASLT_TUNING` at it. Compare the
 prompt speed with and without it before keeping it.
 
+Shipped tables:
+
+- `gfx1100-hipblaslt-100100.txt`, `gfx1100-hipblaslt-100200.txt`: RX 7900 XTX.
+- `gfx1201-hipblaslt-100500.txt`: Radeon AI PRO R9700 (gfx1201, 32 GB), calibrated with ROCm 10.2.0a20260914
+  (AMD's `gfx120X-all` nightly, hipBLASLt 1.5.0, library build `d3164197`). 16 dense GEMM geometries at T=4096 and
+  T=8192, 32 rows. setup uses it only when the installed hipBLASLt reports 1.5.0 (it is found in `/opt/rocm`
+  when that is a system ROCm 7 or newer). The version number is the only thing the engine can check, so another
+  1.5.0 build could number its solutions differently. `hip_prefill_hipblaslt_gemm` (with `STRATA_HIPBLASLT_TUNING`
+  set) is a smoke test: it refuses a table for another architecture or version and checks that one BF16 and one
+  F16 row exist and agree with hipBLASEx, which covers 2 of the 32 rows. It does not prove that a solution id is
+  valid: the engine falls back to hipBLASEx for an id the library rejects, and the test still passes. Run it with
+  `STRATA_HIPBLASLT_VERBOSE=1` and look for `fallbacks=0` in its summary line, and recalibrate with
+  `tune_hipblaslt` before using this table with a different 1.5.0 build.
+
 ## Original backend validation (PR #94)
 
 The following is historical validation of the original backend, not a fresh

docs/COMMUNITY_BENCHMARKS.md

@@ -0,0 +1,167 @@
+# Community benchmark results
+
+Share measurements from your own hardware so other users can judge how Strata
+might run on a similar machine. This guide suggests a report format and a pull
+request workflow; a single model, GPU, or context length is enough to contribute.
+
+Existing [benchmark reports](../bench/results/) provide examples. The
+[engine 0.1.26 speed report](../bench/results/2026-09-29-speed-0126/README.md)
+includes hardware, settings, and per-run JSON. The
+[technical details](DETAILS.md#speed-measured) explain the published measurements
+and their limits. Report what you actually measured and label estimates separately.
+
+## Community reports
+
+- [2026-09-30: RTX 5090, Core Ultra 9 285K, 64 GB RAM](../bench/results/2026-09-30-community-rtx-5090/README.md):
+  Strata 0.1.29, original Flash-Next IQ2_XS, 131,072-token context; three runs
+  each at 4,096, 32,768, and 128,000 prompt tokens, plus six recall checks.
+
+## What to record
+
+Include enough information for someone else to repeat your run:
+
+- **Hardware:** GPU model, VRAM, selected GPUs for a multi-GPU run, CPU, installed
+  RAM, and storage type. Include PCIe link speed and width, GPU power limits, and
+  other workloads when known or relevant.
+- **Software:** OS, Strata commit and engine version, driver and CUDA version
+  (or ROCm for HIP), and release binary or source build. Include changed build options.
+- **Model:** exact repository and revision when available, quantization, GGUF
+  filenames, and vision encoder if enabled. Identify custom packs, expert
+  profiles, or draft vocabularies, with hashes or reproducible preparation steps.
+- **Settings:** launch command and relevant run configuration, context limit, KV
+  type and streaming window, expert cache, low-RAM mode, prefill size, CPU workers,
+  MTP, reasoning effort, sampling, and vision. State whether calibration or
+  experimental speed projection was enabled; attach the resulting settings.
+- **Workload:** benchmark command or script, shareable prompts or instructions to
+  build them, actual prompt and generated token counts, output cap, repetitions,
+  and cache state for each run.
+
+Use GB or GiB consistently and name the unit. Mark unavailable values explicitly
+instead of guessing. Remove credentials and private content from configurations,
+prompts, and logs before publishing them.
+
+## How to measure
+
+Start with a configuration that already works on your machine. You do not need to
+download every model or test the largest context. A useful initial speed report
+tests one short prompt and one longer prompt that fits, with a fixed output cap
+(for example, 256 tokens). Record the actual output length if the model ends early.
+
+For each configuration, try at least three measured runs and publish all of them
+with a median and range. If you only have one run, say so. Explain any warm-up and
+whether model loading is included in the timing.
+
+Strata reuses conversation prefixes and adapts its expert cache. Separate prompts
+that are fully processed from follow-ups that reuse tokens, and report the number
+of reused and freshly read tokens from the engine log. Describe how you reset or
+retained state between runs. A warmed expert cache and a reused prompt prefix are
+different conditions; record both when possible.
+
+Keep these measurements separate:
+
+- **Prompt throughput:** freshly processed prompt tokens per second, from the
+  engine's timing output. Keep the corresponding token count and duration.
+- **Decode throughput:** generated tokens per second, from the engine's timing
+  output. Count reasoning tokens too, or explain any different convention.
+- **Time to first token:** seconds from sending the request to its first generated
+  token. For a streaming API measurement, ignore keep-alives and empty deltas and
+  state whether the first token is reasoning or answer text.
+- **Total latency:** elapsed seconds for the whole request. State whether measured
+  at the engine or client, and whether loading, vision encoding, or queueing is included.
+- **Memory:** observed RAM and VRAM usage, including whether the value is a startup
+  snapshot or a peak during inference. Report paging or out-of-memory failures.
+
+Attach the engine timing lines and your measurement script if you used one. Avoid
+calculating decode throughput by dividing generated tokens by total request time,
+which also includes prompt processing. Cancelled or failed requests belong in the
+report as failures, outside the successful throughput summary.
+
+When comparing versions or settings, run both on the same machine and workload
+and list every changed setting. Preserve the per-run results: output text and
+draft acceptance can change speed even when the input is identical.
+
+## Optional correctness checks
+
+Speed measurements alone do not establish answer quality. Include a small check
+relevant to your workload, such as a completed coding task with test results, a
+tool call followed by its result, or an image question with an expected answer.
+
+For long-context recall, the repository includes
+[`tools/needle_bench.py`](../tools/needle_bench.py). With a running local server,
+from the repository root:
+
+```bash
+python tools/needle_bench.py --help
+python tools/needle_bench.py --url http://127.0.0.1:8080 --lengths 32k,128k --depths 10,50,90 --out needles.json
+```
+
+Choose lengths your configured context can hold. The script supports `--api-key`
+for an authenticated server; keep that key out of published commands. Save the
+reported actual prompt lengths, misses, errors, and skipped cases as well as
+successes. A needle test measures recall on those inputs, not overall model quality.
+
+## Submit a report
+
+1. Fork Strata and create a branch for your report.
+2. Add a folder such as `bench/results/YYYY-MM-DD-community-rtx-5090/`. Use the
+   measurement date and a short hardware label; add a suffix if the name exists.
+3. Put a `README.md` based on the template below in that folder, together with
+   per-run JSON or CSV and small scripts or prompts needed to repeat it. Use your
+   own data format and document its fields and units. Keep large model files and
+   generated packs out of the PR.
+4. Open a pull request against `Niko1221/Strata:main`. Summarize the hardware,
+   model, configurations tested, and limitations. Keep a results-only submission
+   separate from engine changes so reviewers can assess the measurements directly.
+
+Maintainers can review the report and decide where to include it. Adding a report
+does not require changing the README's headline performance claims. For a failed
+run or a suspected bug, a GitHub issue with the configuration and relevant logs
+may be more useful than a benchmark PR.
+
+## Report template
+
+Copy this into your report's `README.md`, replace the placeholders, and remove
+sections that do not apply. Use `not measured` for missing measurements; the table
+contains no example performance numbers.
+
+````markdown
+# Community benchmark on GPU name
+
+Measured on DATE by HANDLE. State what was tested and the main limitation.
+
+## Hardware and software
+
+- GPU and VRAM; CPU; installed RAM; storage; PCIe link if known:
+- OS; driver; CUDA or ROCm:
+- Strata commit; engine version; release binary or source build:
+- Background workloads and any power limits:
+
+## Model and configuration
+
+- Model repository and revision; quantization; GGUF filenames:
+- Vision encoder; custom packs or profiles:
+- Context; KV type and streaming; cache; prefill; low-RAM mode:
+- MTP; reasoning; sampling; calibration; experimental speed projection:
+
+```text
+Exact launch command and relevant configuration, with credentials removed.
+```
+
+## Method
+
+Link prompts and scripts. Describe output cap, repetitions, warm-up, loading,
+prompt reuse, expert-cache state, timing boundaries, and memory measurement.
+
+## Results
+
+| Configuration | Actual prompt tokens | Reused tokens | Generated tokens | Runs | Prompt tok/s median and range | Decode tok/s median and range | TTFT seconds median and range |
+| --- | ---: | ---: | ---: | ---: | --- | --- | --- |
+| Fill in measured values | | | | | | | |
+
+Link per-run data and logs. Include total latency and memory observations when
+measured, with units and timing boundaries. List failures and skipped cases.
+
+## Correctness and limitations
+
+Describe checks, expected answers, observed results, and untested behavior.
+````

docs/DETAILS.md

@@ -83,7 +83,10 @@ of the vocabulary (`mtp/rt/draft_vocab.bin`). Since 0.1.27 the subset includes e
 token (106,299 ids), so answers in those languages are 15-38% faster (Q2_0, RTX 5070). Its head takes ~180 MiB of
 VRAM, which the expert cache leaves free for it (0.1.28). `START-HERE.bat --setup --draft-vocab en` keeps the
 English/code subset from before (40,525 ids, ~110 MiB less VRAM, English answers 1-2% faster; CJK answers get
-almost no drafts). `tools/draft_vocab.py` builds and inspects subsets.
+almost no drafts). `--draft-vocab cyrillic` takes the English/code subset plus the whole Cyrillic script (58,963
+ids): the shipped subsets hold 142 of the vocabulary's 18,580 Cyrillic tokens, so Ukrainian or Russian answers got
+1.4 tokens a round; with it 2.1, and 83 -> 109 tokens/s (RTX 5090, the NVFP4 fork), English unchanged.
+`tools/draft_vocab.py` builds and inspects subsets.
 
 **Low-RAM mode (engine 0.1.26, chosen by setup):** normally all of a model's experts are copied into RAM (23-50 GB,
 pinned) and the GPU holds a copy of the most-used ones. On a PC whose RAM cannot hold them beside the system (the
@@ -112,6 +115,32 @@ other ~18 GB), a 32 GB PC with a 12-16 GB GPU the Coder; IQ3_XXS on a 32 GB PC s
   reads went to the file (`resident RAM: ... blob reads from the file`: 0 in steady use).
 - `--low-ram resident|mmap` forces one variant (also on a PC with enough RAM, e.g. to try it).
 
+**Low-RAM mode without `experts.bin` (engine 0.1.31):** for the native packs (IQ2_XS, IQ3_XXS, IQ3_S, the Coder, Swift,
+Q2_0 packed by `tools/iq_pack.py`; not the canonical Q2_0 pack setup makes for AVX-512 CPUs) the mapped mode no longer
+needs the pack's `experts.bin`: when the pack has none, the engine
+maps the model's GGUF files themselves and reads each expert's gate, up and down rows from where `native_experts.txt`
+says they are (the files are checked against it first: every tensor's name, type, shape, offset and bounds). That
+saves the 23-50 GB copy on the disk. The answers are the same: on the Coder, 64 greedy tokens from `experts.bin` and
+from the GGUF gave identical tokens and logits. An expert read from the GGUF is three reads instead of one, so the
+engine fetches a layer's missing experts on 8 threads (`STRATA_FETCH_THREADS`) with one batched page request
+(Windows `PrefetchVirtualMemory`). With an `experts.bin` in the pack, nothing changes. Setup does not use this yet.
+
+**A RAM budget (engine 0.1.31, `--resident-budget-gib N`):** the resident variant for a model whose experts do not all
+fit: the N GiB of experts the GPU cache does not hold that the expert profile ranks hottest are copied into RAM at
+start (locked; page-locked when the driver allows the whole budget), and the rest are read from the files through the
+OS file cache. It implies `--mmap-experts` and leaves 4 GB of free RAM (a larger N is clamped, with a message). With
+the GGUF read in place it also warms the next layer's likely experts: while the CPU works on a layer, a thread applies
+the next layer's router to this layer's input and asks the OS for the pages of the predicted experts that neither the
+GPU nor the RAM budget holds (only pages - the experts computed are the same; `STRATA_LOOKAHEAD=0` turns it off). This
+is what runs [Unsloth's UD-Q4_K_XL](UNSLOTH_Q4.md) (72 GiB of experts) on a 64 GB PC: 7-8.5 tokens/s at N = 40 on an
+RTX 5070, against ~3 tokens/s before these changes.
+
+**How much came from where:** with `--stats` the engine prints the tiers of the decode (`expert tiers`: blobs from the
+RAM copy, blobs and MB from the files, the time spent reading them; `routing prefetch`: how many of the file reads had
+been warmed). The server log has the same per request (`expert tiers: GPU ... hits ...; RAM ... blobs, files ...
+blobs ... MB read`), and `GET /metrics` lists `ram_blobs`, `file_blobs` and `file_mb` for each recent request (with
+engine 0.1.31 or newer).
+
 Time to first token is prompt length / prompt speed: with Q2_0 about 4 s at 4K, 25 s at 32K, under 2 minutes at 128K
 and 4.5 minutes at 262K (engine 0.1.13 made long prompts about twice as fast, below).
 
@@ -195,6 +224,15 @@ thought about for 1,524 tokens. Not a benchmark, but consistent with the claim.
 START-HERE.bat --setup --family swift --model IQ2_XS
 ```
 
+### Experimental: Unsloth's UD-Q4_K_XL
+
+A 4-bit quantization of the same model (111 GB, 72 GiB of experts). Setup offers it from engine 0.1.32
+(`--family unsloth --model UD-Q4_K_XL`: a RAM budget of your RAM less 24 GB, the rest read from the SSD); what it
+does and the manual workflow are in **[docs/UNSLOTH_Q4.md](UNSLOTH_Q4.md)**. On a 64 GB PC with a 12 GB RTX 5070 it writes 7-8.5
+tokens/s, most experts read from the SSD; it picks the same tokens as llama.cpp on the same file at 97.5-99% of
+the positions of short greedy answers, 90-91% after a 16K prompt, differing mostly at near-ties
+([measured](UNSLOTH_Q4.md#quality-against-llamacpp-on-the-same-file)).
+
 ## Before you start
 
 You need **only an NVIDIA driver** (version 580 or newer; update it with the NVIDIA App or from
@@ -383,6 +421,16 @@ print(r.choices[0].message.content)
   Without a setting the model uses its own default, **high**. `none` answers at once (fastest); `low` keeps the thinking
   short. The levels are instructions the model was trained with, not a hard token limit: on easy questions all three
   think briefly, on hard ones `high` thinks longest and is most accurate.
+- **A hard thinking budget (opt-in).** `"reasoning_budget_tokens": N` in a request (OpenAI or Anthropic) caps the
+  thinking at N tokens: when it gets there the server ends it with a short wrap-up line and `</think>`, and the model
+  answers from there (the engine continues from what it already holds, so nothing is read again). The wrap-up is
+  part of the thinking the client sees and counts as output tokens. `"reasoning_budget_tokens": N` in
+  `strata-<model>.json` sets it for every request; a request's own value wins, and `0` means no budget. Off by default;
+  Anthropic's `"thinking": {"budget_tokens": N}` still only chooses the level, as above.
+- **Anthropic requests that don't ask for thinking (opt-in, 0.1.32, #278).** By default a `/v1/messages` request
+  with no `"thinking"`, effort or budget thinks as the model's template does. `"anthropic_thinking": "on_request"` in
+  `strata-<model>.json` renders such a request without thinking - Anthropic's own rule, and what Claude Code's short
+  helper calls (a session title in a few dozen tokens) need; its real turns ask for thinking when it is on there.
 - **Streaming.** With `"stream": true` everything arrives as it is made: the thinking, the answer, and tool calls
   (the tool's name first, then its arguments piece by piece, like OpenAI and Anthropic do). While the model reads a
   long prompt the stream sends keep-alives, so agents do not time out; the server window prints progress every
@@ -396,6 +444,9 @@ print(r.choices[0].message.content)
   `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.
+- **Model aliases** (0.1.32). `"aliases": ["qwen", "local-model"]` in `strata-<model>.json` lists the model under
+  those names too in `/v1/models` (each with its own `id`, and in the model's `aliases`), like llama-server's
+  `--alias`; a request naming one is answered under that name. Any other name is still served, as before.
 - **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
@@ -406,7 +457,12 @@ print(r.choices[0].message.content)
 - **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.
+  clients then send it as their API key. Streamed answers carry `X-Accel-Buffering: no`, so nginx-style proxies pass
+  each token on at once. The web app's settings and MCP tools only answer Strata's own page: when you open it through
+  a proxy or tunnel whose address differs, add that address, e.g. `"trusted_origins": ["https://strata.example.com"]`.
+- **From web apps in a browser (CORS).** Off by default. `"cors_origins": ["https://chat.example.com"]` lets pages of
+  those origins call `/v1/*` from the browser (Open WebUI's direct connections, browser extensions); `["*"]` lets any
+  page do it - only sensible with an API key. It never opens `/settings`, `/unload` or the MCP tools.
 
 **Conversation cache.** A request that continues a chat reads only the part after what the engine already holds: the
 live session, or one of the checkpoints it keeps in RAM (up to 6, ~118 MB each, taken at the start of each new
@@ -732,3 +788,56 @@ Strata itself: [MIT](../LICENSE). The model files are not part of it; their lice
   `serve/web/fonts/OFL.txt`). Its Monitor tab started from @code-martin's dashboard idea (PR #22).
 - The experimental speed projection's vector (`data/experimental-speed-projection/`): Qwen Community License 1.0,
   made from the model's activations (see its README).
+
+### Start the text API without occupying the GPU
+
+`serve/server.py --engine strata --config strata-<model>.json --lazy` (or `"lazy_load": true` in that
+config) starts the lightweight HTTP API without spawning the native engine. The first generation request
+loads it through the existing reload path, including `before_load` and `min_free_vram_mib`. Eager startup
+remains the default. This option is text-only: a vision configuration with lazy startup is rejected explicitly.
+
+`POST /v1/load` and `/v1/unload` are JSON control aliases for integrations, accepting `{}` or
+`{"model":"<configured model>"}` and returning model status. They require the configured API key,
+`application/json`, and no foreign browser Origin. They return **409** while a request is active or queued,
+and **404** for an unknown model. Existing `/load` and `/unload` behavior is preserved. `/api/health` aliases
+`/health`; `/v1/status` exposes `loaded` and `auto_load`. The unloaded model remains discoverable.
+
+Unloading and shutdown close the native engine's stdin after sending `QUIT`, allowing Windows' detached
+stdin reader to see EOF. Cleanup waits for process exit before releasing handles; if forced shutdown still
+times out, the server keeps ownership and reports an error rather than claiming the model was unloaded.
+
+### JSON response formats
+
+`POST /v1/chat/completions` accepts `response_format: {"type":"json_object"}` or
+`{"type":"json_schema","json_schema":{"name":"answer","strict":true,"schema":{"type":"object","properties":{"answer":{"type":"integer"}},"required":["answer"],"additionalProperties":false}}}`.
+The schema must describe an object at its root. Local `#` references work; remote references are refused.
+`json_schema` is checked with the Python package `jsonschema` when it is installed (`python -m pip install
+"jsonschema>=4.23,<5"`; setup does not add it); without it the answer is only checked to be one JSON object, and the
+server says so once.
+
+This is **schema prompting followed by server validation**, not grammar-constrained decoding. One generation
+is made per request, with no hidden retry. Successful responses contain a validated JSON object. Malformed JSON,
+duplicate keys, non-finite numbers, schema violations and incomplete generations return **502** with
+`error.code: structured_output_failed`; invalid request schemas return **400**. JSON formats combined with
+tools/MCP are refused explicitly. Without `response_format`, ordinary text and tool behavior stays the same.
+
+Structured SSE buffers the answer while sending keep-alive comments. It emits content only after validation,
+then usage/timings and `[DONE]`; failures emit an SSE error and `[DONE]` without invalid content deltas.
+`/v1/status.structured_output` advertises the formats, validation method and buffered streaming behavior.
+
+### API request monitor
+
+Off by default, since it keeps prompts and answers in memory: turn it on with `"api_monitor": true` in
+`strata-<model>.json` (or `serve/server.py --api-monitor`); otherwise nothing is recorded and the two endpoints below
+answer 404.
+Open `/api-monitor` to inspect API traffic without opening a chat. It shows the model state, safe
+load/unload controls, active/queued requests, original request bodies, output, separate reasoning and
+non-stream response bodies. Total wall-clock includes FIFO waits and automatic loading; load, queue,
+first-token, prompt/output tokens and engine decode timing are shown separately.
+
+`GET /api/requests` returns compact summaries; `GET /api/requests?id=<id>` returns one retained request.
+Both use the existing API-key check. The monitor retains the newest **100 requests in memory** until restart,
+with **262,144 characters per input/output/reasoning/response field** and visible truncation flags. The actual API
+responses are unaffected. Headers are not recorded, and the monitor key is kept in this tab's session storage.
+Treat request history as sensitive input/output when exposing Strata on a network: set an API key as above.
+The page uses relative URLs and works through the existing host binding or a reverse proxy.

docs/MULTI_GPU.md

@@ -44,8 +44,8 @@ now on; the answer is kept.
 - a card older than the RTX 20 series (compute capability below 7.5: GTX 10 and older);
 - a card with less than 8 GB of VRAM, together with others (each card holds a copy of the dense weights and its
   own prompt buffers);
-- AMD and Intel GPUs, and a mix of NVIDIA with them. (Two AMD RDNA4 cards run the split from a hand-written
-  config; see [AMD_HIP.md](AMD_HIP.md#rdna4-gfx1201).)
+- Intel GPUs, and a mix of NVIDIA and AMD cards. (AMD cards share a model among themselves: `./setup.sh --backend
+  hip --gpus 1,0`, see [AMD_HIP.md](AMD_HIP.md).)
 
 Or edit an existing config (`strata-*.json`), then restart:
 
@@ -54,6 +54,21 @@ Or edit an existing config (`strata-*.json`), then restart:
 "layer_split": "auto"
 ```
 
+**Skip the split when the first card holds everything** (opt-in, 0.1.31): `"split_skip_if_fits": true` in the config
+(engine flag `--split-skip-if-fits`, with `--layer-split auto`) runs on the first card alone when it holds every
+profiled expert plus the context's KV, the draft layer and the reserve, and says so in the log; otherwise the split
+stays. On an R9700 32 GB + RX 9070 XT the R9700 holds all of the Coder's experts: with the flag 4K prompts read at
+1,776 tok/s instead of 1,244 (split) and decode runs at ~60 tok/s instead of ~51 (16K prompts ~5% slower than split).
+
+**Short prompts on a split (0.1.32, #340).** 0.1.30 gave each card's prompt path a loan from its own expert cache,
+refilled after every request; on cards that hold nearly all their experts that cost short prompts up to a third of
+their speed. 0.1.32 refills all cards at once, uses a smaller streaming ring on a split, and lets a card with free
+VRAM keep its own prompt buffers - the same output as 0.1.31, measured on an R9700 + RX 9070 XT: 2K prompts 993 ->
+1,265 tok/s, 16K 1,852 -> 1,950, decode unchanged. `STRATA_SPLIT_OWN=1` (opt-in) gives every card its own buffers:
+2K 1,450 and 16K 2,227 tok/s there, but a full card then keeps a different set of experts resident, so the output
+differs from the default's (stable and coherent); `STRATA_SPLIT_OWN=auto` does that only where the buffers are at
+most 12% of each card's VRAM.
+
 The engine flags behind it: `--layer-split K1[,K2..]|auto` and `--split-device D1[,D2..]` (the later stages'
 devices; default the next visible ones). `--layer-split K --split-device 0` runs both stages on one card sharing
 everything - the bit-exact check of the hand-off, not a speed mode.
@@ -95,8 +110,10 @@ into the card that owns the layer.
 - The prompt path has its own buffers on every card (1.5 GB each at the default 2048-token chunk; `--prefill 1024`
   halves that) instead of borrowing cache slots as one card does. An explicit `--expert-cache` on the first card is
   capped to leave room for them.
-- On Windows only 8 GiB of the expert arena is pinned (more, mapped into two GPU contexts, leaves WDDM refusing
-  allocations); the PCIe share covers those layers.
+- Under WDDM (Windows, and WSL2) only 8 GiB of the expert arena is pinned (more, mapped into two GPU contexts,
+  leaves WDDM refusing allocations); the rest streams through the pinned staging ring. A Linux driver has no such
+  limit, so there the whole arena is pinned (since 0.1.31; the cap cost a 4090 + 3060 split two thirds of its
+  prompt speed, #253). `STRATA_ARENA_PIN_GIB=N` pins at most N GiB, `0` the whole arena, on any OS.
 - Every card needs compute capability 7.5 (RTX 20 or newer). The pre-sm_80 QSA scorer path is fp32 FMAs, so a
   Turing card runs the same kernels instead of the tensor-core prompt attention.
 

docs/ORCA_Q4_K_S.md

@@ -0,0 +1,36 @@
+# OrcaRouter Uncensored Q4_K_S (manual setup)
+
+This path uses the three original `orcarouter/Qwen3.8-Flash-Next-Uncensored-GGUF` Q4_K_S shards without rewriting their expert, embedding or PLE table bytes. It is separate from the installer model menu. Keep all three shard filenames together.
+
+The ordinary GGUF quantizes some small projections which Strata reads as BF16. `--compat-bf16` dequantizes and rounds those projections to BF16 in the separate pack; it does not restore their original precision. The Q5_0 PLE table remains in the source GGUF and currently uses the mapped reader (`--ple-io mmap`). The Q4_K gate/up experts and Q5_0/Q5_1 down experts remain in GGUF form, including layers whose three tensors cross shard boundaries (each role is read from its own shard). The GPU expert kernels take Q4_K with Q5_1 (or Q8_0) downs; Q5_0 downs are not among them yet, and the engine refuses to start on a pack with such a layer (it names the layer).
+
+## Build and prepare
+
+Build Strata with CUDA, native experts, `STRATA_ORCA_Q4KS_MMQ=ON` (and `STRATA_BUILD_TESTS=ON` for the two parity programs below). This opt-in compiles the Q5_0 GGML MMQ instance for CUDA or HIP (the Q4_K and Q5_1 instances come with the engine's K-quant MMQ build on CUDA); the usual Q8_0 draft-layer instance remains in both builds. HIP also needs `STRATA_PREFILL_MMQ=ON`. Select your card's compute capability, for example `89` on an L40S. On Windows with Ninja and CUDA 12.6, use the Visual Studio developer environment and `-DCMAKE_CUDA_RUNTIME_LIBRARY=Shared` to keep the CUDA runtime linkage consistent. The default pinned ggml checkout is supported by the project build.
+
+```sh
+cmake -S . -B build -G Ninja -DSTRATA_ENABLE_CUDA=ON -DSTRATA_NATIVE_EXPERTS=ON -DSTRATA_ORCA_Q4KS_MMQ=ON -DCMAKE_CUDA_ARCHITECTURES=89 -DCMAKE_CUDA_RUNTIME_LIBRARY=Shared -DCMAKE_BUILD_TYPE=Release
+cmake --build build --target strata native_expert_parity ple_q5_parity
+python tools/iq_pack.py --gguf /models/Qwen3.8-Flash-Next-Uncensored-Q4_K_S-00001-of-00003.gguf --out packs/orca-q4_k_s --compat-bf16
+```
+
+`iq_pack.py` needs NumPy and the `gguf-py` module from the pinned llama.cpp dependency (`STRATA_GGUF_PY` can point to that directory). It writes a roughly 1.43 GiB dense pack and a native-expert manifest. The original 112 GB of model shards are read in place; `experts.bin` is optional and is not written by this command.
+
+Check the changed kernels against the real files before starting the service:
+
+```sh
+build/native_expert_parity /models/Qwen3.8-Flash-Next-Uncensored-Q4_K_S-00001-of-00003.gguf /models/Qwen3.8-Flash-Next-Uncensored-Q4_K_S-00002-of-00003.gguf /models/Qwen3.8-Flash-Next-Uncensored-Q4_K_S-00003-of-00003.gguf 0 5 6 20 47
+build/ple_q5_parity /models/Qwen3.8-Flash-Next-Uncensored-Q4_K_S-00001-of-00003.gguf
+```
+
+## Engine flags
+
+Pass the same first shard to `--native` and `--ple-gguf`; Strata discovers the other two shards by name. For a first text-only run, use 32K context and a modest cache. Once that passes, increase the context and cache with memory measurements on the target machine.
+
+```sh
+--pack packs/orca-q4_k_s --native /models/Qwen3.8-Flash-Next-Uncensored-Q4_K_S-00001-of-00003.gguf --ple-gguf /models/Qwen3.8-Flash-Next-Uncensored-Q4_K_S-00001-of-00003.gguf --ple-io mmap --expert-profile data/expert-profile.bin --expert-cache auto --prefill 512 --spec 4 --mtp /path/to/mtp/rt --max-context 32768 --kv int8
+```
+
+For two GPUs in persistent server mode, add `--layer-split auto --split-device 1` (with CUDA0 and CUDA1 visible). The usual Strata MTP runtime is built from the original model's draft layer; draft acceptance on the Uncensored fine-tune must be measured. Keep this model's own tokenizer from the pack. Do not combine `--expert-cache-per-layer` with a variable-size native cache: that policy's slot ordering is independent of the pack's per-layer blob sizes.
+
+This port does not claim that Q4_K_S beats llama.cpp on a given computer. Compare the same model bytes, prompt, context, output length and GPU load; report prefill and decode separately.

docs/SECOND_GPU.md

@@ -34,8 +34,9 @@ compute capacity.
 
 The experiment requires the corresponding visible CUDA devices and a CUDA
 enabled build. Each card must keep at least 512 MiB free. Tiers must be enabled
-in order. On Windows, CUDA registration of the host expert arena is capped at
-8 GiB to leave room for the contexts and MTP on CUDA0. The rest remains
+in order. Under WDDM (Windows, WSL2), CUDA registration of the host expert arena is capped at
+8 GiB to leave room for the contexts and MTP on CUDA0 (`STRATA_ARENA_PIN_GIB` overrides it; on Linux the whole
+arena is registered). The rest remains
 available to the CPU pool; the PCIe expert path is available only for the
 registered layers. Without secondary GPUs, the original uncapped registration
 behavior applies.

docs/UNSLOTH_Q4.md

@@ -0,0 +1,232 @@
+# Unsloth UD-Q4_K_XL (experimental)
+
+**Experimental.** Setup offers it from engine 0.1.32 ([below](#setup)); the manual workflow after that section works
+with engine 0.1.31 or newer. Validated on one PC: Windows 10, an RTX 5070 (12 GB), 64 GB of RAM and an AVX-512 CPU,
+on 2026-10-01.
+
+The target is Unsloth's 4-bit quantization of the same model the other packs use:
+[unsloth/Qwen3.8-Flash-Next-GGUF, `UD-Q4_K_XL`, revision `38bb39e`](https://huggingface.co/unsloth/Qwen3.8-Flash-Next-GGUF/tree/38bb39ee97821de2c9009abb7e93950eec396e66/UD-Q4_K_XL).
+"UD-Q4_K_XL" is a mix of formats, not Q4_K everywhere: the routed experts are Q4_K (Q5_K in layer 2) for gate/up and
+Q5_1 (Q8_0 in layers 2, 4, 30, 46, 47) for down; the token embedding, the output head, the attention and shared-expert
+projections and the PLE key are Q8_0; the 28.8 GB PLE table is IQ4_NL. The routed experts are 71.7 GiB, about twice
+the 3-bit models'.
+
+## Setup
+
+`START-HERE.bat --setup` (Linux: `./setup.sh --setup`) and choose **Qwen3.8-Flash-Next (Unsloth)**, marked
+`[experimental]`; or directly:
+
+```sh
+START-HERE.bat --setup --family unsloth --model UD-Q4_K_XL
+```
+
+What setup does differently for this model:
+
+- It needs 48 GB of RAM or more and engine 0.1.32 or newer (checked before anything is downloaded), and an NVIDIA
+  GPU. One GPU only: with `--gpus` it uses the first one and says so. No images (the vision encoder is not wired to
+  this file yet) and no experimental speed projection (not tested with it).
+- It downloads the four shards below from the pinned revision `38bb39e` (resumable, like the other models), then
+  checks each one's size and SHA-256 against the table below; the check takes a few minutes once and is remembered
+  in the file's finish mark. A file with the wrong hash is deleted, so the next run downloads it again.
+- It packs with `--compat-bf16` and never writes `experts.bin` (`--low-ram` does not apply).
+- The RAM budget is the PC's RAM less 24 GB: `--resident-budget-gib 40` on 64 GB, at most all 71 GiB of experts on
+  96 GB or more. From a 64K context up, where the KV cache moves to RAM, its size comes out of the budget.
+- It recommends an 8K context on a GPU under 14 GB (every GB of KV cache is a GB less of cached experts).
+
+## The files
+
+Four shards, 111,334,654,784 bytes (111.3 GB) together. Shard 1 holds only the metadata; layer 11's down projection is
+in shard 2 and its gate/up in shard 3, which this engine handles (`native_experts.txt` v4).
+
+| File | Bytes | SHA-256 |
+| --- | --- | --- |
+| `Qwen3.8-Flash-Next-UD-Q4_K_XL-00001-of-00004.gguf` | 10,946,624 | `4448186216b3af4cc558bbce2c3213f01608f8f8b2e5267a9767971dd3ec8082` |
+| `Qwen3.8-Flash-Next-UD-Q4_K_XL-00002-of-00004.gguf` | 49,859,583,136 | `3f342f1c1580473f1ee94ddd5b28206e8c07a70fa1a366f59d1d6c922919a6c9` |
+| `Qwen3.8-Flash-Next-UD-Q4_K_XL-00003-of-00004.gguf` | 49,376,141,504 | `56758f40269cad5cd9b0d3d6fbae0f40f6d5be6de49e4ab392dbe83157d9cbd3` |
+| `Qwen3.8-Flash-Next-UD-Q4_K_XL-00004-of-00004.gguf` | 12,087,983,520 | `753bda48b98ba4f1636134a90a967de1b2d3908a236c026e464777342e53510a` |
+
+Keep the four files together in one folder under these names (the engine finds shards 2-4 from shard 1's name; a
+missing shard is an error that names it). Setup checks them itself; by hand, check them before packing
+(`sha256sum -c`, or `Get-FileHash` on Windows).
+Hugging Face snapshot symlinks work if you pass the snapshot's file name, not the hash-named blob it points to.
+
+## What it needs
+
+| | |
+| --- | --- |
+| Disk | 111.3 GB for the four files, 1.4 GB for the pack, ~6 GB for the MTP draft layer if you have none yet. **No `experts.bin`**: the engine reads the experts from the GGUF files in place. An NVMe SSD matters: the experts that fit neither VRAM nor the RAM budget are read from it for every token. |
+| RAM | 64 GB measured. The RAM budget (below) holds the most-used experts; the rest come from the SSD through the OS file cache. |
+| GPU | 12 GB measured (RTX 5070): after the weights, the draft layer and the buffers, the expert cache held 1,280 of the 24,576 experts (3.7 GiB) at 4K context. |
+
+## The pack (by hand)
+
+Build Strata (or install 0.1.31+), then, from the repository root, with gguf-py from the pinned llama.cpp
+(setup installs it; `STRATA_GGUF_PY` can point to its `gguf-py` folder):
+
+```sh
+.venv/bin/python tools/iq_pack.py \
+  --gguf /path/to/Qwen3.8-Flash-Next-UD-Q4_K_XL-00001-of-00004.gguf \
+  --out packs/ud-q4_k_xl --compat-bf16
+```
+
+About 30 seconds. It writes `index.txt`, `dense.bin` (1.4 GB), `native_experts.txt`, the tokenizer, `conversions.json`
+and `compat-bf16.json`. **`--compat-bf16` is required**: this file stores 195 small projections (the hyper-connection
+up/down matrices, `output_hc_*` and the PLE value) as Q8_0, while the engine reads them as BF16. Without the flag the
+packer refuses and writes nothing; with it, they are dequantized and rounded to BF16 (nearest-even). Do not add
+`--experts-bin`: it would write a 77 GB copy of the experts that this mode does not need.
+
+## The numbers: what is native and what is converted
+
+The routed experts, the token embedding, the output head, the attention and shared-expert projections, the PLE key and
+the PLE table are used as the GGUF stores them (the engine has GPU kernels and CPU (ggml) paths for Q4_K, Q5_K, Q5_1 and
+Q8_0). The small tensors the engine reads as floats are converted, and `conversions.json` lists every one with its
+source/destination type, method, whether it is exact, the largest absolute error and the source bytes' SHA-256:
+264 F32 tensors (routers, injections, SSM gates) whose values are already BF16's are stored as BF16 exactly; the 195
+Q8_0 projections above are rounded to BF16 (largest absolute error 0.0144, in `hc_ffn_up`); the PLE convolution (F32)
+is narrowed to F16 when the engine loads it (largest error 3e-8). That is the compatibility Unsloth's file needs; it is
+not the original BF16 checkpoint. The output matches llama.cpp's on the same file:
+[Quality](#quality-against-llamacpp-on-the-same-file).
+
+## The MTP draft layer
+
+The draft layer is the base model's, the same one the other packs use. If setup installed Strata, you have it
+(`mtp/rt` in `Strata-data`). Otherwise build it as [docs/ORCA.md](ORCA.md#preparation) shows (`tools/mtp_fetch.py`,
+`tools/mtp_pack.py`, `tools/mtp_rt.py`, then copy `data/draft_vocab.bin` to `mtp/rt/`).
+
+## The server
+
+Save as `strata-ud-q4_k_xl.json` at the repository root (or next to setup's other `strata-*.json`), with `/path/to/`
+pointing at **shard 1**. `--ple-gguf` is not needed: the engine finds the shard that holds the PLE table
+(`per_layer_token_embd.weight`, shard 2) by name. `--resident-budget-gib` turns on the mapped mode with a RAM budget
+(it implies `--mmap-experts`).
+
+```json
+{
+  "exe": "engine/strata.exe",
+  "args": [
+    "--pack", "packs/ud-q4_k_xl",
+    "--native", "/path/to/Qwen3.8-Flash-Next-UD-Q4_K_XL-00001-of-00004.gguf",
+    "--resident-budget-gib", "40",
+    "--expert-profile", "data/expert-profile.bin", "--expert-cache", "auto",
+    "--prefill", "auto", "--spec", "4", "--spec-min-p", "0.5",
+    "--mtp", "mtp/rt", "--max-context", "8192"
+  ],
+  "cwd": ".",
+  "tokenizer": "packs/ud-q4_k_xl/tokenizer",
+  "model_name": "qwen3.8-flash-next-ud-q4_k_xl",
+  "log": "strata-ud-q4_k_xl.log",
+  "host": "127.0.0.1",
+  "port": 8080
+}
+```
+
+```sh
+.venv/bin/python -m serve.server --engine strata --config strata-ud-q4_k_xl.json --port 8080
+```
+
+(`engine/strata.exe` is setup's engine on Windows; a self-built one is `build/strata`.) The expert profile is the base
+model's (`data/expert-profile.bin`); it ranks the same 48 x 512 experts and decides which ones the GPU cache and the RAM
+budget take first.
+
+**Choosing the RAM budget N:** your RAM minus 20-24 GB (the OS, the engine itself, the 126 MB router copy, and room for
+the OS file cache that serves the rest). On 64 GB, 40. The engine clamps a budget larger than the RAM it finds free
+(minus 4 GB) and says so. Everything above N comes from the SSD for every token, so N is the setting that matters
+most; a bigger budget was faster in every measurement (24 / 32 / 40 GiB). When the driver page-locks the whole budget
+(24 GiB did on this PC, 32 and 40 did not), the GPU also computes a share of the misses over PCIe, as in the resident
+low-RAM mode; `STRATA_RESIDENT_PIN=0` keeps the budget locked only (the CPU then computes every miss).
+
+**Context:** measured at 4K. The KV cache takes VRAM from the expert cache, so a longer context makes decoding slower;
+8K is a reasonable start on 12 GB. `--kv int8` halves the KV cache's VRAM.
+
+## What to expect (RTX 5070 12 GB, 64 GB RAM, Windows, one short greedy prompt)
+
+| RAM budget | Greedy output | Notes |
+| --- | --- | --- |
+| 24 GiB | 5.1-5.2 tok/s (2 runs, before the routing prefetch below) | 2.2 GB read from the SSD per verify round (~3.5 tokens) |
+| 40 GiB | **7-8.5 tok/s** (6 runs: 6.7-8.6, mean 7.9) | 1.1 GB read from the SSD per verify round, ~0.33 GB per token |
+
+- The first answer comes about 60 s after the engine starts (mostly loading: the RAM budget is copied from the GGUF
+  files at start). A 30-token prompt then took 5-6 s.
+- Long prompts (engine 0.1.32): a 16K prompt is read at **160 tokens/s** (15,873 tokens in 99 s; 0.1.31: 57 tokens/s,
+  273-282 s). Reading a prompt streams every expert once per 4K chunk, and the third of them outside the RAM budget
+  come from the SSD (~35 GB per chunk), so the prompt path now reads them with 32 threads and up to 128 blobs in
+  flight when the experts are read from the GGUF in place (`STRATA_STAGER_THREADS` / `STRATA_STAGER_RING` override
+  it; 4 and 16 before, still the default for every other model). Between the chunks, the 28.8 GB PLE table's rows
+  take ~5 s per chunk.
+- An engine compiled with `-DSTRATA_MMQ_KQUANTS=ON` multiplies the Q4_K / Q5_K / Q5_1 experts of a prompt with
+  llama.cpp's MMQ kernels (as the other models' formats always are) instead of dequantizing them to FP16: 4x less GPU
+  time for those products, but on this PC the prompt waits for the SSD either way (29 s per 4K chunk with or
+  without). Off in the released engine: it loads every kernel at start, and these took 1-3 expert slots of VRAM from
+  every model.
+- The speed varies from run to run (6.7-8.6 tok/s at 40 GiB for the same prompt), with what the OS file cache holds.
+- For comparison, IQ3_S (all its experts in RAM) writes ~53 tok/s on the same PC ([the speed tables](DETAILS.md#speed-measured)).
+- Where the time goes (`--stats` on the command line): at 40 GiB about 550-750 ms of each verify round (3.5 tokens) is
+  reading experts from the SSD, ~75 ms the CPU's expert kernels, ~13 ms the GPU.
+
+## Quality: against llama.cpp on the same file
+
+Strata and llama.cpp (the pinned commit `3cf0325`, a CPU build reading the GGUF memory-mapped) were given the same
+token sequences, and at every position each wrote its 20 most likely next tokens with their log-probabilities: Strata
+through its verify windows (`STRATA_LOGPOS` with `STRATA_LOGPOS_TOPK=20`), llama.cpp from `llama_get_logits_ith` over
+one batch. Strata ran with the recommended settings (RAM budget 40 GiB, the MTP draft layer on, greedy,
+`--adapt-every 100000` so the expert cache does not move during the run). Per position: whether the most likely token is the same (argmax
+agreement), how many of the 5 / 10 most likely tokens both have, the KL divergence (llama.cpp's distribution against
+Strata's, over llama.cpp's top 20 plus one bucket for the rest), and the perplexity of the sequence under each.
+
+| Token set | Positions | Argmax agreement | Top-5 / top-10 overlap | KL | Perplexity Strata / llama.cpp |
+| --- | ---: | ---: | ---: | ---: | ---: |
+| Code: Strata's greedy answer to a coding prompt (an LRU cache in Python, 56-token prompt) | 400 | **99.0%** | 95.6% / 95.3% | 0.0008 | 1.067 / 1.070 |
+| Thinking: Strata's greedy answer to a reasoning puzzle (with thinking, 55-token prompt) | 324 | **97.5%** | 96.2% / 96.4% | 0.0016 | 1.107 / 1.106 |
+| After a 16K prompt: Strata's greedy continuation (256 tokens) of the 16,384 real-text tokens below | 256 | 90.2% | 88.1% / 89.3% | 0.039 | 1.619 / 1.623 |
+| Real text after a 16K prompt: the last 510 tokens of 16,384 tokens of this repository's docs and code | 510 | 91.2% | 88.3% / 89.9% | 0.035 | 4.27 / 4.29 |
+| The same 511 positions with only 512 tokens before them | 511 | 89.2% | 86.0% / 86.3% | 0.061 | 8.99 / 8.87 |
+
+For comparison, [eddoursul/Strata](https://github.com/eddoursul/Strata) reported 98.2-99.3% (code), 95.6-95.8%
+(thinking) and 97.7% (after a 16K prompt) for the same file against llama.cpp on an RTX 3090.
+
+**Where they differ:** at near-ties. Grouped by llama.cpp's own gap between its two most likely tokens, Strata picks
+the same token at every position of the code and thinking answers where that gap is 0.2 or more (695 of 695) and at
+every position after the 16K prompt where it is 0.5 or more (144 of 144 in the continuation, 254 of 254 in the real
+text; 50 of 54 and 117 of 121 between 0.2 and 0.5). Text after a long document has many more near-ties than a short
+answer (58 of the continuation's 256 positions and 135 of the real text's 510 have a gap under 0.2, against 13 of the
+code answer's 400), which is why its agreement is lower. It is not the long context itself: the same real-text
+positions after only 512 tokens agree a little less (89.2%), and the perplexities match within 3% in every set. For
+scale, Strata's own greedy run and its teacher-forced rerun of the continuation pick different tokens at 6% of the
+positions (93.8% the same: other window sizes, other experts in VRAM), the same kind of near-tie flips. The 16K
+prompts were read by Strata's batched prompt path (15,872 tokens), the rest through the verify windows. These numbers
+are the FP16 prompt path's (the released engine's); with the MMQ prompt path (`-DSTRATA_MMQ_KQUANTS=ON`, above) the
+continuation after the 16K prompt agrees at 92.2% (KL 0.029, three runs, identical).
+
+To repeat it: Strata's side is `STRATA_LOGPOS=<file>` with `STRATA_LOGPOS_TOPK=20` (engine 0.1.32) on a serve
+engine with `--short-read` covering the positions to compare; llama.cpp's side was a short program over
+`llama_decode` / `llama_get_logits_ith` writing the same top 20 per position.
+
+## Opt-ins and switches (environment variables)
+
+| | |
+| --- | --- |
+| `STRATA_LOOKAHEAD=0` | Turns off the routing-aware prefetch (on by default in this mode): while the CPU works on a layer, a thread applies the next layer's router to this layer's input and asks the OS to read the predicted experts' pages. Pages only, the answers are the same. About half of the SSD reads were predicted; mean +14% (6.9 -> 7.9 tok/s). `STRATA_LOOKAHEAD_K` sets the experts per token (default 10). |
+| `STRATA_KQ256=1` | Multi-token AVX2 kernels for the Q4_K / Q5_1 / Q8_0 experts. Bit-exact with ggml's, but measured no faster, so off. |
+| `STRATA_PARTIAL_PIN=1` | Registers the hottest part of the RAM budget (up to `STRATA_PARTIAL_PIN_GIB`, default 24) with the GPU driver, so the GPU computes a share of the misses over PCIe (`--pcie-frac`). Measured no faster on this PC, and it changes the numerics of those experts (GPU kernels instead of the CPU's), so off. |
+| `STRATA_FETCH_THREADS=N` | Threads that read the experts from the GGUF while it answers (default 8; 16 was no faster). The prompt path has its own: `STRATA_STAGER_THREADS` (default 32 here) and `STRATA_STAGER_RING` (128). |
+
+## Scope and validation
+
+- Only UD-Q4_K_XL at revision `38bb39e` is targeted. Other Unsloth quantizations use formats this engine may not
+  have kernels for; the engine checks every layer's formats at start and refuses an unsupported one by name.
+- Tests: the packer's synthetic 4-shard and conversion tests (`.venv/bin/python -m unittest discover -s tools -p
+  test_iq_pack.py`); CTests `gguf_split_test`, `expert_layout_test`, `native_expert_parity_*` (the three real expert
+  format pairs against ggml-cpu, the Q5_1 min term, Q8_0 rows), `prefill_mmq_kquant_test` (with `-DSTRATA_MMQ_KQUANTS=ON`: the
+  prompt path's MMQ products for Q4_K / Q5_K / Q5_1 / Q8_0 against ggml's dequantized weights); the in-place mode against `experts.bin` on the Coder
+  (identical tokens and logits).
+- Real runs: greedy answers to a coding prompt (correct) at every budget and setting above, identical across them;
+  the same tokens as llama.cpp at 97.5-99% of the positions of short greedy answers and 90-91% after a 16K prompt,
+  differing mostly at near-ties (above). Not yet: sampled decoding, long conversations. Please report what you see.
+
+## Credits
+
+The split-artifact loader, the expert formats' GPU kernels and the packing of Unsloth's files follow
+[eddoursul/Strata](https://github.com/eddoursul/Strata) (PR #245), which ran these files first. The Q8_0 kernels, the
+per-role shard column and the PLE convolution's F32 -> F16 fix come from @gopinath87607's PR #255; per-role shard names
+were also proposed by @jagsan-cyber in PR #247. The quantization is [Unsloth](https://huggingface.co/unsloth)'s; the
+model is Qwen's ([license](https://huggingface.co/Qwen/Qwen3.8-Flash-Next)).

include/strata/artifact/gguf_reader.hpp

@@ -23,6 +23,8 @@
 #include <cstdint>
 #include <cstdio>
 #include <cstring>
+#include <limits>
+#include <memory>
 #include <string>
 #include <vector>
 #include <map>
@@ -30,6 +32,8 @@
 #include <stdexcept>
 #include <algorithm>
 
+#include "strata/artifact/gguf_split.hpp"
+
 #ifdef _WIN32
 #define WIN32_LEAN_AND_MEAN
 #ifndef NOMINMAX
@@ -92,6 +96,8 @@ inline const char* ggml_type_name(uint32_t t) {
         return "IQ2_S";
     case 23:
         return "IQ4_XS";
+    case 24:
+        return "I8";
     case 30:
         return "BF16";
     case 34:
@@ -200,6 +206,10 @@ inline bool block_geometry(uint32_t t, int& elems, int& bytes) {
         elems = 256;
         bytes = 56;
         return true;
+    case 24:   // I8: raw bytes (the FP8 PLE table of tools/ple_fp8_pack.py)
+        elems = 1;
+        bytes = 1;
+        return true;
     case 42:
         elems = 64;
         bytes = 18;
@@ -342,6 +352,8 @@ public:
     uint32_t version() const { return version_; }
     uint64_t data_start() const { return data_start_; }
     uint64_t file_size() const { return size_; }
+    uint64_t alignment() const { return alignment_; }
+    const std::string& path() const { return path_; }
 
     const TensorInfo* find(const std::string& name) const {
         for (const auto& t : tensors_)
@@ -460,6 +472,104 @@ private:
     std::map<std::string, MetaValue> meta_;
 };
 
+// Bytes of a tensor's payload from its shape and block geometry; 0 when the type is unknown, a row is not whole
+// blocks, or the count overflows.
+inline uint64_t tensor_payload_bytes(const TensorInfo& t) {
+    int be = 0, bb = 0;
+    if (t.shape.empty() || !block_geometry(t.type, be, bb) || t.shape[0] % (uint64_t) be) return 0;
+    uint64_t elements = 1;
+    for (uint64_t d : t.shape) {
+        if (d == 0 || elements > (std::numeric_limits<uint64_t>::max)() / d) return 0;
+        elements *= d;
+    }
+    const uint64_t blocks = elements / (uint64_t) be;
+    if (blocks > (std::numeric_limits<uint64_t>::max)() / (uint64_t) bb) return 0;
+    return blocks * (uint64_t) bb;
+}
+
+// The shards of one model (strata::gguf_split_paths), opened together; tensors are looked up across all of them.
+// From eddoursul/Strata 8029fa9, with the split-key validation of #255 (gopinath87607) made a property of the
+// model rather than of one loader: a split GGUF carries the model's metadata (general.architecture and the
+// qwen4exp keys) in its FIRST shard only, and the later shards just declare split.count / split.no /
+// split.tensors.count.  Unsloth's UD-Q4_K_XL is the extreme case: shard 1 holds the metadata and no tensor at
+// all, and output.weight, token_embd.weight and the PLE table live in shard 2.  So the architecture is checked
+// on `meta()` and a tensor is read from the shard that holds it.
+//
+// Refused at construction: a later shard whose split keys disagree with shard 1's (another model's shard, or a
+// shard renamed into the family), a split model whose tensor directories do not add up to split.tensors.count,
+// and a tensor name present in two shards (GGUF has no index to say which one is meant).
+class GgufModel {
+public:
+    explicit GgufModel(const std::vector<std::string>& paths) {
+        if (paths.empty()) throw std::runtime_error("GGUF: a model needs at least one shard");
+        for (const auto& p : paths) shards_.push_back(std::make_unique<GgufFile>(p));
+        validate_split();
+        for (size_t i = 0; i < shards_.size(); ++i)
+            for (const auto& t : shards_[i]->tensors()) {
+                const auto ins = index_.emplace(t.name, std::make_pair(i, &t));
+                if (!ins.second)
+                    throw std::runtime_error("GGUF: tensor " + t.name + " is in two shards (" +
+                                             shards_[ins.first->second.first]->path() + " and " +
+                                             shards_[i]->path() + ")");
+            }
+    }
+    /// Opens every shard of the model that `any_shard` belongs to (throws when one is missing).
+    static GgufModel open(const std::string& any_shard) { return GgufModel(gguf_split_paths(any_shard)); }
+
+    size_t size() const { return shards_.size(); }
+    const GgufFile& shard(size_t i) const { return *shards_[i]; }
+    /// The metadata shard: general.architecture and the model's keys.
+    const GgufFile& meta() const { return *shards_[0]; }
+    /// The tensor named `name` (its shard index in `*shard`), or nullptr.
+    const TensorInfo* find(const std::string& name, size_t* shard = nullptr) const {
+        const auto it = index_.find(name);
+        if (it == index_.end()) return nullptr;
+        if (shard) *shard = it->second.first;
+        return it->second.second;
+    }
+    /// Whether `t` (a tensor of shard `s`) has a known byte count that lies inside its file.
+    bool in_bounds(const TensorInfo& t, size_t s) const {
+        const GgufFile& g = *shards_[s];
+        const uint64_t bytes = tensor_payload_bytes(t);
+        const uint64_t payload = g.file_size() - g.data_start();
+        return bytes != 0 && t.offset <= payload && bytes <= payload - t.offset;
+    }
+
+private:
+    void validate_split() const {
+        const size_t n = shards_.size();
+        const MetaValue* count0 = shards_[0]->get("split.count");
+        if (n == 1) {
+            if (count0 && count0->u > 1)
+                throw std::runtime_error("GGUF: " + shards_[0]->path() + " is shard 1 of " + std::to_string(count0->u) +
+                                         ", but it was opened as a whole model");
+            return;
+        }
+        if (!shards_[0]->get("general.architecture"))
+            throw std::runtime_error("GGUF: " + shards_[0]->path() + " has no general.architecture; the first shard "
+                                     "of a split model carries the metadata");
+        const MetaValue* total = shards_[0]->get("split.tensors.count");
+        uint64_t tensors = 0;
+        for (size_t i = 0; i < n; ++i) {
+            const GgufFile& g = *shards_[i];
+            const MetaValue* count = g.get("split.count");
+            const MetaValue* no = g.get("split.no");
+            const MetaValue* tc = g.get("split.tensors.count");
+            if (!count || !no || count->u != n || no->u != i || (total && (!tc || tc->u != total->u)))
+                throw std::runtime_error("GGUF: " + g.path() + " does not declare itself shard " + std::to_string(i + 1) +
+                                         " of " + std::to_string(n) + " of this model (split.count / split.no / "
+                                         "split.tensors.count)");
+            tensors += g.tensors().size();
+        }
+        if (total && tensors != total->u)
+            throw std::runtime_error("GGUF: the " + std::to_string(n) + " shards hold " + std::to_string(tensors) +
+                                     " tensors, but split.tensors.count is " + std::to_string(total->u));
+    }
+
+    std::vector<std::unique_ptr<GgufFile>> shards_;
+    std::map<std::string, std::pair<size_t, const TensorInfo*>> index_;
+};
+
 // ---- architecture guard (P1.S2). The engine is specialised to ONE model; anything else must be
 // refused with a precise error rather than silently mis-run.
 struct Qwen4ExpGuard {

include/strata/artifact/gguf_split.hpp

@@ -0,0 +1,52 @@
+// include/strata/artifact/gguf_split.hpp - the shards of a split model.  Separate from gguf_reader.hpp, which
+// includes <windows.h>, so any translation unit can resolve a model's shards.
+//
+// From eddoursul/Strata 8029fa9 (gguf_split_paths), with one change of contract: upstream's model_shards()
+// skipped a shard it could not open and returned the rest, so a download that was still running (or a shard
+// deleted by hand) produced a model with some layers missing and an error much later, or none.  A missing shard
+// is now an error that names it.
+#pragma once
+
+#include <cstdio>
+#include <fstream>
+#include <stdexcept>
+#include <string>
+#include <vector>
+
+namespace strata {
+
+// Split models: `<name>-00001-of-0000N.gguf` ... `<name>-0000N-of-0000N.gguf`, as llama.cpp's gguf-split names
+// them.  Given ANY shard's path, the shards in split order (the metadata shard, 00001, first); a file without that
+// suffix is a model of one shard.  Only the five digits of the shard number are rewritten, so the directory and
+// the stem are the caller's own spelling (a symlinked Hugging Face snapshot keeps its split names).  Throws when
+// a shard is missing.
+inline std::vector<std::string> gguf_split_paths(const std::string& any) {
+    static const char tag[] = "-of-";
+    const size_t at = any.rfind(tag);
+    // "...-NNNNN-of-MMMMM.gguf": five digits before the tag, five after it, then the extension
+    if (at == std::string::npos || at < 6 || any[at - 6] != '-' || any.size() != at + 4 + 5 + 5 ||
+        any.compare(at + 9, 5, ".gguf") != 0)
+        return {any};
+    int no = 0, n = 0;
+    for (size_t i = 0; i < 5; ++i) {
+        const char a = any[at - 5 + i], b = any[at + 4 + i];
+        if (a < '0' || a > '9' || b < '0' || b > '9') return {any};
+        no = no * 10 + (a - '0');
+        n = n * 10 + (b - '0');
+    }
+    if (n < 1 || no < 1 || no > n) return {any};
+    std::vector<std::string> out;
+    for (int i = 1; i <= n; ++i) {
+        char num[8];
+        std::snprintf(num, sizeof num, "%05d", i);
+        std::string p = any;
+        p.replace(at - 5, 5, num);
+        if (!std::ifstream(p, std::ios::binary))
+            throw std::runtime_error("missing model shard " + p + " (shard " + std::to_string(i) + " of " +
+                                     std::to_string(n) + "; is the download complete?)");
+        out.push_back(std::move(p));
+    }
+    return out;
+}
+
+}  // namespace strata

include/strata/core/conversation_cache.hpp

@@ -164,8 +164,44 @@ public:
         return true;
     }
 
+    // #342: drop the parked entries an outgoing conversation (its live tokens and checkpoint chain) supersedes:
+    // the same conversation a turn back, whose DEEPEST checkpoint the outgoing chain still holds, so all it adds
+    // is the tail the client rewrote (the reply as it was generated, before the next request re-rendered it) and
+    // checkpoints older than that one.  A subagent's successive turns parked one such copy each, and make_room's
+    // oldest-first eviction then pushed the parent conversation out after `slots` turns.  An entry without
+    // checkpoints, or whose deepest checkpoint the outgoing chain does not hold (another conversation that only
+    // shares the system prompt's root with it), is kept.  Returns how many were dropped.
+    size_t drop_superseded(const std::vector<int32_t>& ids, const std::vector<ConversationImageKey>& images,
+                           const std::vector<ConversationCheckpoint>& checkpoints, bool cvec) {
+        auto held = [&](const ConversationCheckpoint& c) {
+            if (c.ids == ids && c.imgs == images) return true;
+            for (const auto& k : checkpoints)
+                if (k.ids == c.ids && k.imgs == c.imgs) return true;
+            return false;
+        };
+        size_t dropped = 0;
+        for (size_t i = 0; i < entries_.size();) {
+            const auto& e = entries_[i];
+            const ConversationCheckpoint* deepest = nullptr;
+            for (const auto& c : e.checkpoints)
+                if (!deepest || c.ids.size() > deepest->ids.size()) deepest = &c;
+            if (e.cvec == cvec && deepest && !deepest->ids.empty() && held(*deepest)) {
+                bytes_ -= e.bytes();
+                entries_.erase(entries_.begin() + (std::ptrdiff_t) i);
+                ++dropped;
+                continue;
+            }
+            ++i;
+        }
+        superseded_ += dropped;
+        return dropped;
+    }
+    size_t superseded() const { return superseded_; }
+
     bool put(SavedConversation&& image, size_t held = 0) {
         const size_t n = image.bytes();
+        if (!enabled() || held > budget_ || n > budget_ - held) return false;   // make_room's refusal, first
+        drop_superseded(image.live.ids, image.live.imgs, image.checkpoints, image.cvec);
         if (!make_room(n, held)) return false;
         entries_.push_back(std::move(image));
         bytes_ += n;
@@ -173,7 +209,7 @@ public:
     }
 
 private:
-    size_t budget_ = 0, slots_ = 0, bytes_ = 0, evictions_ = 0;
+    size_t budget_ = 0, slots_ = 0, bytes_ = 0, evictions_ = 0, superseded_ = 0;
     std::deque<SavedConversation> entries_; // least recently active first
     ConversationKvReuse reuse_;
 };

include/strata/core/coupled_draft.hpp

@@ -49,15 +49,23 @@ namespace strata::core {
 /// The longest penalty window the coupled drafter mirrors (serve's kPenaltyWindowCap).
 inline constexpr int kCoupledHistCap = 4096;
 
-/// STRATA_SPEC_COUPLED=1 (any value but empty or "0"): read once.
-inline bool coupled_draft_env() {
-    static const bool on = [] {
+inline bool& coupled_draft_state() {
+    static bool on = [] {
         const char* e = std::getenv("STRATA_SPEC_COUPLED");
         return e != nullptr && *e != '\0' && std::strcmp(e, "0") != 0;
     }();
     return on;
 }
 
+/// Whether coupled draft sampling is enabled (via STRATA_SPEC_COUPLED=1 or --coupled-draft CLI flag).
+inline bool coupled_draft_env() {
+    return coupled_draft_state();
+}
+
+inline void set_coupled_draft(bool on) {
+    coupled_draft_state() = on;
+}
+
 /// The MTP cell of chain step j after a window at `p` with `a` drafts accepted (draft() runs step j there).
 STRATA_COUPLED_HD inline int64_t coupled_draft_cell(int64_t p, int a, int j) { return p + a + j; }
 

include/strata/core/device.hpp

@@ -43,6 +43,10 @@ const char* compiled_gpu_archs();
 // this is the matching check at run time (a binary can be carried to a different machine).
 DeviceInfo device_info(int ordinal = 0);
 
+/// "" when this build has device code for the current device, else CUDA's error: a build for other GPUs would
+/// otherwise fail at its first kernel launch, with nothing that names the cause.
+std::string device_code_error();
+
 class CudaError : public std::runtime_error {
 public:
     CudaError(const std::string& what, int code) : std::runtime_error(what), code_(code) {}

include/strata/core/emulate.hpp

@@ -0,0 +1,36 @@
+// strata/core/emulate.hpp - STRATA_EMULATE_CC: another GPU generation's code paths, run on this card (tests only).
+//
+// A build whose device code is PTX for an older architecture (CMAKE_CUDA_ARCHITECTURES=86-virtual) runs on a newer
+// card through the driver's JIT, and every kernel then sees __CUDA_ARCH__ = 860.  What the host decides from the
+// device must agree with that, or it picks paths and shared-memory sizes the device code was not built for:
+// STRATA_EMULATE_CC=75|80|86|89 makes each such query (compute capability, shared memory per block) answer as that
+// card would.  Speed under it says nothing about the real card; results do - the kernels are the real card's.
+#pragma once
+
+#include <cstdlib>
+
+namespace strata {
+
+/// the emulated compute capability as 10 * major + minor, or 0 (off)
+inline int emulated_cc() {
+    static const int cc = [] {
+        const char* e = std::getenv("STRATA_EMULATE_CC");
+        const int v = e ? std::atoi(e) : 0;
+        return v >= 75 && v < 120 ? v : 0;
+    }();
+    return cc;
+}
+inline int cc_major_of(int real_major) { return emulated_cc() ? emulated_cc() / 10 : real_major; }
+inline int cc_minor_of(int real_minor) { return emulated_cc() ? emulated_cc() % 10 : real_minor; }
+/// cudaDevAttrMaxSharedMemoryPerBlockOptin as the emulated card reports it: 64 KB on Turing, 163 KB on the A100,
+/// 99 KB on the other Ampere and Ada cards
+inline int smem_optin_of(int real) {
+    switch (emulated_cc()) {
+        case 0: return real;
+        case 75: return 65536;
+        case 80: return 166912;
+        default: return real < 101376 ? real : 101376;
+    }
+}
+
+}  // namespace strata

include/strata/core/expert_source.hpp

@@ -28,13 +28,23 @@
 #include <atomic>
 #include <cstdint>
 #include <cstddef>
+#include <condition_variable>
+#include <memory>
+#include <mutex>
+#include <thread>
+#include <unordered_map>
 #include <utility>
 #include <string>
 #include <vector>
 
+namespace strata::kernels::cpu {
+struct ExpertLayout;
+}
+
 namespace strata::core {
 
 class RemoteExperts;
+struct LoadStats;
 
 namespace detail {
 
@@ -115,6 +125,61 @@ public:
     /// checked with `pinned`).  The arena answers per layer through its expert 0; the resident RAM mode's compact
     /// copy has no expert 0 when the GPU cache holds it, so it answers for the whole copy.
     virtual bool pcie_layer(int64_t layer) const { return device_alias(layer, 0) != nullptr; }
+
+    /// CS-T: whether `blob(layer, expert)` would be assembled into a short-lived buffer (a native pack read from its
+    /// GGUF shards in place, where an expert's gate, up and down rows are three separate slices).  Such a pointer
+    /// stays valid for the layer it was asked in and the next one or two; a consumer that keeps a blob longer (the
+    /// prompt path's stager queues a whole chunk) copies it with `copy_blob` instead.
+    virtual bool transient(int64_t layer, int64_t expert) const { (void) layer; (void) expert; return false; }
+    /// The blob's bytes into `dst` (blob_bytes(layer) of them).  Safe from several threads for a source whose
+    /// `transient` can be true.
+    virtual bool copy_blob(int64_t layer, int64_t expert, uint8_t* dst);
+    /// The `n` experts of `layer` the CPU is about to ask `blob` for, all at once: a source that reads a file may
+    /// fetch them in parallel.  The bytes `blob` then returns are the same.  Default: nothing.
+    virtual void prefetch(int64_t layer, const int64_t* experts, int64_t n) { (void) layer; (void) experts; (void) n; }
+    /// CS-T: the experts of `layer` a predictor expects next - a source that reads a file may start reading their
+    /// pages now, in the background.  Only warms: what `blob` returns is unchanged.  Default: nothing.
+    virtual void warm(int64_t layer, const int64_t* experts, int64_t n) { (void) layer; (void) experts; (void) n; }
+    /// Whether `warm` does anything (the predictor is not run otherwise).
+    virtual bool warms() const { return false; }
+};
+
+/// CS-T, routing-aware prefetch of the file tier: when the CPU pool starts layer `l`, a worker thread applies layer
+/// l+1's router (BF16, host copy) to layer l's MoE input - the residual stream changes little from one layer to the
+/// next - takes each token's top `k` experts, drops the ones the GPU cache or the RAM copy holds, and asks the
+/// source to `warm` the rest, so their pages are on the way while layer l computes.  A prediction only warms pages:
+/// it never changes which experts are computed or how.
+class RouterLookahead {
+public:
+    RouterLookahead() = default;
+    ~RouterLookahead();
+    RouterLookahead(const RouterLookahead&) = delete;
+    RouterLookahead& operator=(const RouterLookahead&) = delete;
+    /// `routers[l]`: layer l's ffn_gate_inp as BF16 bits, n_expert rows of n_embd.
+    bool start(std::vector<std::vector<uint16_t>> routers, int64_t n_embd, int64_t n_expert, int k, ExpertSource* src,
+               std::string& err);
+    /// Layer `layer`'s MoE input for `n_tok` tokens (host floats): predict and warm layer + 1.  Never waits: a
+    /// prediction still running for an earlier layer makes this one skip.
+    void submit(int64_t layer, const float* x, int64_t n_tok, const int32_t* host_res);
+    int64_t predicted() const { return predicted_.load(std::memory_order_relaxed); }
+    int64_t skipped() const { return skipped_.load(std::memory_order_relaxed); }
+    double busy_ms() const { return (double) busy_us_.load(std::memory_order_relaxed) / 1000.0; }
+
+private:
+    void run();
+    std::vector<std::vector<uint16_t>> routers_;
+    int64_t n_embd_ = 0, n_expert_ = 0;
+    int k_ = 10;
+    ExpertSource* src_ = nullptr;
+    std::thread thread_;
+    std::mutex mu_;
+    std::condition_variable cv_;
+    bool quit_ = false, pending_ = false, busy_ = false;
+    int64_t layer_ = -1, n_tok_ = 0;
+    const int32_t* host_res_ = nullptr;
+    std::vector<float> x_;
+    std::atomic<int64_t> predicted_{0}, skipped_{0};
+    std::atomic<uint64_t> busy_us_{0};
 };
 
 /// Plan v0.3 P6: what the GPU computes in a verify window's layer, written by the pool (mapped host memory) right
@@ -148,6 +213,7 @@ struct GpuPlanSink {
 struct ExpertDispatch {
     strata::kernels::cpu::ExpertPool* pool = nullptr;
     ExpertSource* src = nullptr;
+    RouterLookahead* lookahead = nullptr;   ///< CS-T: warms the next layer's predicted file-tier experts
     RemoteExperts* remote[3] = {}; ///< optional CUDA1..3 tiers for otherwise CPU-served rows
     int remote_count = 0;
     int64_t n_expert = strata::kernels::cpu::NE;
@@ -329,7 +395,16 @@ public:
     ///
     /// The size check is not a formality: a short file would fault at the END of a long sequence, and an
     /// over-long one means the pack is not the one the geometry came from.  Refuses with the two numbers.
+    ///
+    /// CS-T: a native pack WITHOUT experts.bin, after `set_gguf`, maps the model's GGUF shards instead (every file
+    /// native_experts.txt names, after `check_experts_gguf`), and assembles a blob from its three role slices when
+    /// it is asked for: the SSD tier, read in place through the OS file cache, with no 30-77 GB experts.bin copy.
+    /// With experts.bin present nothing changes.
     bool open(const std::string& pack_dir, int64_t n_layers, int64_t n_expert, std::string& err);
+    /// CS-T: the --native shard (native_experts.txt names the other files beside it); see `open`.
+    void set_gguf(const std::string& native) { gguf_ = native; }
+    /// Whether the experts are read from the GGUF shards in place (no experts.bin).
+    bool gguf_mode() const { return !role_ptr_.empty(); }
     /// Pin a compact host mirror of experts absent from a fully filled static GPU cache. The mmap remains open
     /// as a fallback for later cache reloads. This is opt-in because the complement may still be a large allocation.
     ///
@@ -342,15 +417,20 @@ public:
     ///     experts are kept in RAM too, from the last slot down, as far as `available RAM - headroom_bytes` allows
     ///     (a lent slot's expert is streamed during the prompt and copied back after it).
     ///   - the rest (the experts no slot holds) must fit that budget, or nothing is allocated and this returns false.
+    ///
+    /// CS-T, `budget_bytes` > 0 (`--resident-budget-gib`): only as many of those experts as fit `budget_bytes`, taken
+    /// in `rank` order (the expert profile: the hottest after the GPU cache's), are copied; the rest stay on the
+    /// mapped files (the SSD tier).  No lend region then (a lent slot's expert is read from the files).
     bool pin_cache_complement(
         const ExpertCache& cache, std::string& err, bool pin = true,
         const std::vector<std::pair<int32_t, int32_t>>& additional_gpu_pairs = {}, int64_t lend_from_slot = -1,
-        uint64_t headroom_bytes = 8ull << 30);
+        uint64_t headroom_bytes = 8ull << 30, uint64_t budget_bytes = 0,
+        const std::vector<std::pair<int32_t, int32_t>>* rank = nullptr);
     void close();
 
     bool mapped() const { return base_ != nullptr; }
     int64_t blobs() const { return blobs_; }
-    uint64_t pinned_bytes() const { return complement_pinned_ ? complement_bytes_ : 0; }
+    uint64_t pinned_bytes() const { return complement_pinned_ ? complement_pin_limit_ : 0; }
     uint64_t resident_bytes() const { return complement_bytes_; }
     bool complement_pinned() const { return complement_pinned_; }
     bool complement_ready() const { return complement_ready_; }
@@ -377,11 +457,34 @@ public:
     /// With the compact copy ready: blobs read from the mapped file since (what the plain mmap mode may read from
     /// the SSD).  0 in a steady resident mode; lend-region experts that did not fit the RAM count here.
     int64_t file_reads() const { return file_reads_.load(std::memory_order_relaxed); }
+    /// CS-T per-tier counters: blobs served from the RAM copy, and the bytes read from the mapped files (the blobs
+    /// `file_reads` counts, plus the prompt path's copies of them).
+    int64_t ram_reads() const { return ram_reads_.load(std::memory_order_relaxed); }
+    uint64_t file_read_bytes() const { return file_read_bytes_.load(std::memory_order_relaxed); }
+    /// Of those, the bytes `blob` read (decode windows, adaptive swaps; the rest are the prompt path's copies), and
+    /// the time spent reading the files, summed over threads.
+    uint64_t file_blob_bytes() const { return file_blob_bytes_.load(std::memory_order_relaxed); }
+    double file_ms() const { return (double) file_us_.load(std::memory_order_relaxed) / 1000.0; }
+    /// Threads `prefetch` reads the GGUF with (STRATA_FETCH_THREADS, default 8).
+    void set_fetch_threads(int n) { fetch_threads_ = n < 1 ? 1 : n; }
 
     const uint8_t* blob(int64_t layer, int64_t expert) override;
     bool pinned(int64_t layer, int64_t expert) const override;
     const uint8_t* device_alias(int64_t layer, int64_t expert) const override;
     bool pcie_layer(int64_t layer) const override;
+    bool transient(int64_t layer, int64_t expert) const override;
+    bool copy_blob(int64_t layer, int64_t expert, uint8_t* dst) override;
+    /// CS-T: advances the assembled blobs' age (see staged_blob).
+    void begin_layer(int64_t layer, const int32_t* ids, int64_t k) override;
+    /// CS-T: the GGUF in place assembles the missed experts on `fetch_threads_` threads.
+    void prefetch(int64_t layer, const int64_t* experts, int64_t n) override;
+    /// CS-T: the GGUF in place asks the OS for the predicted experts' pages (PrefetchVirtualMemory on Windows,
+    /// madvise(WILLNEED) elsewhere), skipping the RAM copy's.
+    void warm(int64_t layer, const int64_t* experts, int64_t n) override;
+    bool warms() const override { return !role_ptr_.empty(); }
+    /// Of the blobs the file tier read for the decode, how many had been warmed for their layer beforehand.
+    int64_t warmed_hits() const { return warm_hits_.load(std::memory_order_relaxed); }
+    int64_t warmed() const { return warm_count_.load(std::memory_order_relaxed); }
 
     /// Blobs touched, for the driver to report.  With `h = 0` this is `48 * k` per token and the number is only
     /// interesting once Phase 3 makes it not so.
@@ -389,7 +492,50 @@ public:
 
 private:
     const uint8_t* mapped_blob(int64_t layer, int64_t expert) const;
+    /// The blob's bytes from the mapped file(s) - experts.bin, or the three GGUF role slices - into `dst`.
+    bool copy_from_files(int64_t layer, int64_t expert, uint8_t* dst) const;
+    bool open_gguf(std::string& err);
+    const uint8_t* staged_blob(int64_t layer, int64_t expert);
+    bool claim_stage(int64_t key, size_t& v, bool& fill);
+    bool fill_stage(size_t v, int64_t layer, int64_t expert, uint8_t* dst);
     static constexpr uint64_t kNoComplement = detail::kNoCacheComplement;
+    // ---- CS-T: the GGUF shards in place
+    std::string gguf_;
+    struct Map {
+        const uint8_t* base = nullptr;
+        uint64_t bytes = 0;
+#if defined(_WIN32)
+        void* file = nullptr;
+        void* mapping = nullptr;
+#else
+        int fd = -1;
+#endif
+    };
+    std::vector<Map> maps_;
+    std::vector<const uint8_t*> role_ptr_;    ///< 3 x n_layers: gate / up / down of the layer's expert 0
+    std::vector<uint64_t> role_bytes_;        ///< 3 x n_layers: bytes per expert of that role
+    // the blobs assembled for `blob()`: a small pool of buffers, one per recent (layer, expert).  A buffer is
+    // reused only once `kStageAge` layer changes have passed since its blob was last asked for, so a pointer holds
+    // through the layer it was asked in and the next ones (the pool computes a layer's misses before the next).
+    static constexpr uint64_t kStageAge = 3;
+    std::mutex stage_mu_;
+    std::vector<std::unique_ptr<uint8_t[]>> stage_buf_;
+    std::vector<int64_t> stage_key_;
+    std::vector<uint64_t> stage_epoch_, stage_used_;
+    std::vector<char> stage_busy_;            ///< being filled (outside stage_mu_): never a victim
+    std::condition_variable stage_cv_;
+    int fetch_threads_ = 8;
+    std::atomic<uint64_t> file_blob_bytes_{0}, file_us_{0};
+    std::unique_ptr<std::atomic<uint32_t>[]> warm_stamp_;   ///< per (layer, expert): epoch_ + 1 when warmed
+    std::atomic<int64_t> warm_hits_{0}, warm_count_{0};
+    std::unordered_map<int64_t, size_t> stage_of_;
+    uint64_t stage_blob_ = 0;
+    uint64_t stage_seq_ = 0;
+    uint64_t epoch_ = 0;
+    int64_t last_layer_ = -1;
+    bool stage_grew_ = false;
+    std::atomic<int64_t> ram_reads_{0};
+    std::atomic<uint64_t> file_read_bytes_{0};
     const uint8_t* base_ = nullptr;
     int64_t blobs_ = 0;
     int64_t n_layers_ = 0;
@@ -402,6 +548,9 @@ private:
     uint64_t complement_bytes_ = 0;
     std::vector<uint64_t> complement_offsets_;
     bool complement_pinned_ = false;
+    bool complement_partial_ = false;         ///< CS-T: only the first complement_pin_limit_ bytes are registered
+    uint64_t complement_pin_limit_ = 0;
+    uint64_t complement_lock_off_ = 0;        ///< the working-set lock covers [lock_off, lock_off + locked)
     bool complement_ready_ = false;
     uint64_t complement_locked_ = 0;          ///< bytes held in the working set (pin refused)
     int64_t complement_lent_slots_ = 0;
@@ -450,8 +599,9 @@ public:
     /// rate, because those are the two numbers that say whether the arena is the one that was asked for.
     bool open(const std::string& pack_dir, int64_t n_layers, int64_t n_expert, int threads, std::string& err,
               uint64_t max_pinned_bytes = 0, const std::string& shared_arena_file = {});
-    /// Plan v0.3 P6: a native pack without experts.bin takes its experts from the model's shard 1.
-    void set_gguf(const std::string& shard1) { gguf_ = shard1; }
+    /// Plan v0.3 P6: a native pack without experts.bin takes its experts from the model's GGUF: `native` is the
+    /// --native shard, and native_experts.txt names the other shards beside it (per layer, or per role in v4).
+    void set_gguf(const std::string& native) { gguf_ = native; }
     void close();
 
     bool mapped() const { return base_ != nullptr; }
@@ -489,4 +639,12 @@ private:
     std::string gguf_;
 };
 
+/// Plan v0.3 P6: checks native_experts.txt's GGUF spans against the files, before anything is read: each layer's
+/// gate/up/down at its recorded (file, offset) must be that tensor (`blk.L.ffn_<role>_exps.weight`), of the
+/// layout's type and dimensions, and inside the file.  `native` is the --native shard (see set_gguf).
+bool check_experts_gguf(const std::string& native, const strata::kernels::cpu::ExpertLayout& lay, std::string& err);
+/// Fills `dst` (lay.total bytes, the experts.bin layout) from the GGUF files, one role at a time.
+LoadStats load_experts_gguf(const std::string& native, uint8_t* dst, const strata::kernels::cpu::ExpertLayout& lay,
+                            int threads);
+
 }  // namespace strata::core