fix(build): dma_batch.hpp copy_expert_blobs uses plain arrays (nvcc on Linux rejects its std::array locals)

dma_batch_parity.cu failed to compile on Linux/WSL (nvcc 13.0 with g++): 'template argument 2 is invalid' at the
std::array<T, cap> locals of copy_expert_blobs, whatever the form of the size (int, size_t, a literal). The engine
objects and the Windows build were not affected; the Linux tests build was.

.dockerignore

@@ -0,0 +1,27 @@
+# Keep the image to the source only: everything generated is re-created on
+# first start (inside the container or on the /data volume).
+.git
+
+# local Python environment (rebuilt in the image)
+.venv/
+__pycache__/
+*.pyc
+.pytest_cache/
+
+# setup.py outputs and downloads (engine, packs, models, logs)
+/engine/
+/build/
+/build*/
+/dist/
+/packs/
+/mtp/
+/models/
+/Strata-data/
+third_party/llama.cpp/
+third_party/*.zip
+*.done
+*.log
+strata-*.json
+strata-*.txt
+run-*.sh
+run-*.bat

.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

.github/FUNDING.yml

@@ -0,0 +1 @@
+buy_me_a_coffee: strataengine

.gitignore

@@ -18,6 +18,8 @@ __pycache__/
 
 # plan v0.3 P1 (repository hygiene, review E1): profiler captures and large diagnostic dumps stay out of git.
 *.nsys-rep
+
+# Pi harness metadata (local session state, not project source)
 *.sqlite
 *.npz
 *.occ1
@@ -26,7 +28,7 @@ td.log
 # created by setup.py on the user's machine
 /.venv/
 /build/
-/third_party/llama.cpp/
+/third_party/llama.cpp
 /models/
 /packs/
 /mtp/
@@ -35,15 +37,27 @@ td.log
 /strata-*.json
 /strata-*.log
 /build-strata.bat
+/build-strata-cuda12.bat
+/build-cuda12/
+/.strata-mcp/
 
 # image encoder (setup.py --vision): build output and the generated build script
 build-vision*/
 build-vision.bat
+build-vision-cuda12.bat
 
 # setup.py outputs (engine, downloads) and release builds
 /engine/
+/engine-cuda12/
 /build-release*/
 /dist/
 *.done
 /third_party/*.zip
 
+# IDEs
+.vscode/
+.idea/
+.cursor/
+
+/.ccache/
+/.rocm-win/

AGENTS.md

@@ -0,0 +1,23 @@
+# AGENTS.md
+
+Strata runs the Qwen3.8-Flash-Next mixture-of-experts model (and its Coder, Swift 1.5 and Unsloth variants) on a
+normal PC: one NVIDIA or AMD graphics card plus system RAM, on Windows or Linux. It has a C++/CUDA/HIP engine
+(`src/`, `include/`), a Python server with an OpenAI- and Anthropic-compatible API and a web app (`serve/`), and a
+one-click installer (`setup.py`, started by `START-HERE.bat` / `setup.sh`).
+
+## Installing Strata for a user
+
+Follow **[docs/AI_SETUP.md](docs/AI_SETUP.md)**: check the PC, pick the model by RAM, run setup non-interactively,
+start and verify the server, and connect the user's apps. Never expose the server beyond `127.0.0.1` without
+`--api-key`. As an alternative to shell commands, Strata's MCP server ([docs/MCP_SERVER.md](docs/MCP_SERVER.md))
+offers the same steps as tools.
+
+## Working on the code
+
+- How the engine works, every measured number, the API and all settings: [docs/DETAILS.md](docs/DETAILS.md) and
+  the [paper](docs/paper/Strata-Paper.pdf).
+- AMD (HIP) build and validation: [docs/AMD_HIP.md](docs/AMD_HIP.md); multi-GPU: [docs/MULTI_GPU.md](docs/MULTI_GPU.md).
+- Setup's own tests run without a GPU or downloads: `python tools/test_setup_<name>.py` (for example
+  `tools/test_setup_amd.py`, `tools/test_setup_choices.py`).
+- Keep the docs' style: plain words, measured numbers with what they were measured on, no claims without a
+  measurement.

CMakeLists.txt

@@ -8,7 +8,18 @@
 # 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 0.1.27 LANGUAGES CXX)
+project(strata VERSION 0.1.40 LANGUAGES CXX)
+# EXPERIMENTAL: the Intel Arc engine, the SYCL port in sycl/ (PR #423; icpx + oneMKL, docs/INTEL_ARC.md). It is its
+# own project that takes the unported sources from src/; with this on, nothing below is configured.
+option(STRATA_ENABLE_SYCL "EXPERIMENTAL: build the Intel Arc (SYCL) engine from sycl/ - docs/INTEL_ARC.md" OFF)
+if(STRATA_ENABLE_SYCL)
+  if(STRATA_ENABLE_CUDA OR STRATA_ENABLE_HIP)
+    message(FATAL_ERROR "STRATA_ENABLE_SYCL is a separate backend; leave STRATA_ENABLE_CUDA and STRATA_ENABLE_HIP off")
+  endif()
+  message(WARNING "STRATA_ENABLE_SYCL: the Intel Arc engine is experimental (community port, docs/INTEL_ARC.md)")
+  add_subdirectory(sycl)
+  return()
+endif()
 add_compile_definitions(STRATA_VERSION="${PROJECT_VERSION}")
 # issue #31: release builds keep a PDB beside the exe (not shipped in it), so a minidump from a stalled engine can
 # be read back to the exact source line
@@ -21,6 +32,18 @@ endif()
 # instead of this machine's (Strata's own AVX-512 kernels are per-file and chosen at run time either way), and
 # the MSVC runtime linked in, so no Visual C++ redistributable is needed.
 option(STRATA_PORTABLE "build for other PCs: AVX2 baseline, static MSVC runtime" OFF)
+# EXPERIMENTAL, opt-in: an engine for CPUs without AVX2 (#394, #595, #623), compiled on that PC.  ggml-cpu, which then
+# computes every CPU expert row, is built for this floor instead of AVX2 or the build host: "avx" (Sandy/Ivy Bridge:
+# AVX, no FMA/F16C/AVX2) or "none" (SSE4.2 + POPCNT: Nehalem/Westmere).  Strata's own AVX2/AVX-512 kernels stay in the
+# binary behind their CPU checks; the router lookahead gets an AVX1 kernel.  Empty (the default) changes nothing.
+set(STRATA_ISA_FLOOR "" CACHE STRING "experimental older-CPU build: empty (default, AVX2 floor), avx, or none (SSE4.2)")
+set_property(CACHE STRATA_ISA_FLOOR PROPERTY STRINGS "" avx none)
+if(NOT STRATA_ISA_FLOOR STREQUAL "" AND NOT STRATA_ISA_FLOOR STREQUAL "avx" AND NOT STRATA_ISA_FLOOR STREQUAL "none")
+  message(FATAL_ERROR "STRATA_ISA_FLOOR must be empty, avx or none (got '${STRATA_ISA_FLOOR}')")
+endif()
+if(STRATA_ISA_FLOOR AND STRATA_PORTABLE)
+  message(FATAL_ERROR "STRATA_ISA_FLOOR (an older-CPU build) and STRATA_PORTABLE (the AVX2 release build) exclude each other")
+endif()
 if(STRATA_PORTABLE AND MSVC)
   set(CMAKE_MSVC_RUNTIME_LIBRARY "MultiThreaded")
 endif()
@@ -33,12 +56,36 @@ 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 gfx1100 wave32 hardware" OFF)
+# AMD gfx906 (MI50, wave64): the same .cu sources compiled as HIP through the gfx906 compat layer.  The CUDA runtime and
+# cuBLAS names are mapped by the headers in include/strata/platform/hip_compat/, which go first on the include path,
+# and CUDA::cudart / CUDA::cublas become aliases of HIP's targets, so every CUDA target below stays as it is.  This is
+# separate from STRATA_ENABLE_HIP (the wave32 RDNA backend): with it on, STRATA_ENABLE_CUDA is on and
+# STRATA_ENABLE_HIP stays off, so every "CUDA" branch of the tree is the one taken.
+option(STRATA_HIP_GFX906 "Build the CUDA targets for AMD gfx906 through HIP (wave64 compat layer)" OFF)
+if(STRATA_HIP_GFX906)
+  if(STRATA_ENABLE_HIP)
+    message(FATAL_ERROR "STRATA_HIP_GFX906 and STRATA_ENABLE_HIP are separate backends; enable only one")
+  endif()
+  set(STRATA_ENABLE_CUDA ON CACHE BOOL "" FORCE)
+endif()
+option(STRATA_ENABLE_HIP "Build the HIP targets for wave32 AMD GPUs (gfx1100, gfx1201; gfx1012/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 MMQ prompt kernels for Q4_K / Q5_K / Q5_1 / Q6_K: the CUDA experts of Unsloth's UD-Q4_K_XL, and on HIP the dense GGUF projections (STRATA_DENSE_MMQ)" OFF)
+# CUDA: Q6_K gate/up in the grouped expert kernels (Unsloth's UD-Q6_K_XL, docs/UNSLOTH_Q6.md).  Off by default: every
+# grouped-kernel instance is loaded when the engine starts (CUDA_MODULE_LOADING=EAGER), so one more costs VRAM that
+# every other model would lose as expert slots (PR #860 review)
+option(STRATA_Q6K_EXPERTS "Build the Q6_K gate/up grouped expert kernels (UD-Q6_K_XL)" OFF)
+if(STRATA_Q6K_EXPERTS)
+  add_compile_definitions(STRATA_Q6K_EXPERTS=1)
+endif()
+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()
-if(STRATA_ENABLE_CUDA AND STRATA_ENABLE_HIP)
+if(STRATA_ENABLE_CUDA AND STRATA_ENABLE_HIP AND NOT STRATA_HIP_GFX906)
   message(FATAL_ERROR "STRATA_ENABLE_CUDA and STRATA_ENABLE_HIP are separate backends; enable only one")
 endif()
 if(STRATA_ENABLE_HIP)
@@ -50,13 +97,58 @@ if(EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/tests/CMakeLists.txt AND EXISTS ${CMAKE_CU
   set(_strata_tests_default ON)
 endif()
 option(STRATA_BUILD_TESTS "Build the test suite" ${_strata_tests_default})
+option(STRATA_BUILD_CONVERSATION_TESTS "Build standalone conversation cache tests" OFF)
 option(STRATA_WERROR "Treat compiler warnings as errors" OFF)
 
-if(STRATA_ENABLE_CUDA)
+if(STRATA_BUILD_CONVERSATION_TESTS)
+  enable_testing()
+  add_executable(conversation_cache_test src/core/conversation_cache_test.cpp)
+  target_include_directories(conversation_cache_test PRIVATE include)
+  add_test(NAME conversation_cache_test COMMAND conversation_cache_test)
+  add_executable(conversation_split_failure_test src/core/conversation_split_failure_test.cpp)
+  target_include_directories(conversation_split_failure_test PRIVATE include)
+  add_test(NAME conversation_split_failure_test COMMAND conversation_split_failure_test)
+  add_executable(conversation_memory_test src/core/conversation_memory_test.cpp src/core/conversation_memory.cpp)
+  target_include_directories(conversation_memory_test PRIVATE include)
+  add_test(NAME conversation_memory_test COMMAND conversation_memory_test)
+  add_executable(conversation_file_test src/core/conversation_file_test.cpp src/core/conversation_file.cpp)
+  target_include_directories(conversation_file_test PRIVATE include)
+  add_test(NAME conversation_file_test COMMAND conversation_file_test)
+endif()
+
+if(STRATA_HIP_GFX906)
+  if(NOT DEFINED CMAKE_HIP_ARCHITECTURES)
+    set(CMAKE_HIP_ARCHITECTURES gfx906 CACHE STRING "HIP architectures" FORCE)
+  endif()
+  enable_language(HIP)
+  find_package(hip REQUIRED)
+  find_package(hipblas REQUIRED)
+  add_library(CUDA::cudart INTERFACE IMPORTED)
+  target_link_libraries(CUDA::cudart INTERFACE hip::host)
+  target_compile_definitions(CUDA::cudart INTERFACE STRATA_HIP_GFX906=1 __HIP_PLATFORM_AMD__=1)
+  target_include_directories(CUDA::cudart BEFORE INTERFACE ${CMAKE_CURRENT_SOURCE_DIR}/include/strata/platform/hip_compat)
+  add_library(CUDA::cublas INTERFACE IMPORTED)
+  target_link_libraries(CUDA::cublas INTERFACE roc::hipblas CUDA::cudart)
+  set(CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES ${CMAKE_CURRENT_SOURCE_DIR}/include/strata/platform/hip_compat)
+  include_directories(BEFORE ${CMAKE_CURRENT_SOURCE_DIR}/include/strata/platform/hip_compat)
+  add_compile_definitions(STRATA_HIP_GFX906=1)
+  message(STATUS "Strata: HIP (gfx906 compat) enabled, arch ${CMAKE_HIP_ARCHITECTURES}")
+  set(_strata_gpu_runtime_target CUDA::cudart)
+  set(_strata_gpu_blas_target CUDA::cublas)
+  set(_strata_gpu_include_directories ${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES})
+elseif(STRATA_ENABLE_CUDA)
+  # #585: CMake links CUDA code with the STATIC runtime by default, and the targets below also link CUDA::cudart (the
+  # DLL's import library).  MSVC with a CUDA 12.x toolkit - the experimental Pascal/Volta build - then fails with
+  # LNK1169 (every cudart symbol defined twice); GNU ld lets it pass.  That build links the shared runtime alone.  The
+  # ready-made engine (CUDA 13, no STRATA_EXPERIMENTAL_SM60) is unchanged; -DCMAKE_CUDA_RUNTIME_LIBRARY overrides.
+  if(MSVC AND STRATA_EXPERIMENTAL_SM60 AND NOT DEFINED CMAKE_CUDA_RUNTIME_LIBRARY)
+    set(CMAKE_CUDA_RUNTIME_LIBRARY Shared)
+  endif()
   enable_language(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()
@@ -68,19 +160,40 @@ if(STRATA_ENABLE_CUDA)
     if(_base STREQUAL "native" OR _base STREQUAL "all" OR _base STREQUAL "all-major")
       continue()
     endif()
-    if(_base LESS 75)
+    # 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}'.")
+        "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 "
+                      "CUDA 12.8 crashed on long prompts on these cards (issues #220, #224). Use CUDA 13.0 or newer.")
     endif()
   endforeach()
+  if(STRATA_EXPERIMENTAL_SM60)
+    # every target sees it: the Pascal / Volta code paths (the Volta prompt attention, PR #600) are compiled into this
+    # build only, so the ready-made engine (sm_75+) keeps exactly the code it had
+    add_compile_definitions(STRATA_EXPERIMENTAL_SM60=1)
+  endif()
   message(STATUS "Strata: CUDA enabled, arch ${CMAKE_CUDA_ARCHITECTURES}")
   set(_strata_gpu_runtime_target CUDA::cudart)
   set(_strata_gpu_blas_target CUDA::cublas)
   set(_strata_gpu_include_directories ${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES})
 elseif(STRATA_ENABLE_HIP)
   set(_strata_gpu_runtime_target strata_hip_runtime)
-  set(_strata_gpu_blas_target roc::hipblas)
+  set(_strata_gpu_blas_target ${STRATA_HIP_BLAS_TARGETS})
   set(_strata_gpu_include_directories ${STRATA_HIP_COMPAT_INCLUDE_DIR})
 endif()
 
@@ -127,6 +240,16 @@ 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)
+
+  # the hipBLASLt tuning header's architecture gate: RDNA4 siblings share solution IDs, RDNA3 siblings share
+  # theirs, cross-family pairs and version mismatches stay rejected (synthetic tables: no model, no GPU)
+  add_executable(tuning_header_arch_family_test tests/prefill/tuning_header_arch_family_test.cpp)
+  target_include_directories(tuning_header_arch_family_test PRIVATE "${CMAKE_CURRENT_SOURCE_DIR}/src/prefill")
+  add_test(NAME tuning_header_arch_family_test COMMAND tuning_header_arch_family_test)
 endif()
 
 # ---- strata_plan ----------------------------------------------------------
@@ -145,6 +268,10 @@ 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.
@@ -227,12 +354,30 @@ if(STRATA_ENABLE_CUDA OR STRATA_ENABLE_HIP)
                           src/platform/direct_file.cpp)
   target_link_libraries(strata_kernels PUBLIC strata_core strata_artifact)
   # perf-review D-1: the tensor-core prompt attention vs the FP32 kernel and FP64 (GPU, synthetic, no model); with
-  # the tests, or alone with -DSTRATA_PARITY_PROMPT_ATTN=ON
-  if(STRATA_BUILD_TESTS OR STRATA_PARITY_PROMPT_ATTN)
+  # the tests, or alone with -DSTRATA_PARITY_PROMPT_ATTN=ON.  CUDA only: HIP compiles the tensor-core kernel out.
+  if((STRATA_BUILD_TESTS OR STRATA_PARITY_PROMPT_ATTN) AND STRATA_ENABLE_CUDA)
     add_executable(qsa_prompt_attn_parity src/kernels/qsa_prompt_attn_parity.cpp)
     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()
+  # #512: the top-k's active-block bound (Turing's dispatch, forced on any card by STRATA_TOPK_ACTIVE_ANY) against
+  # the capacity rule, selected ids bitwise
+  if(STRATA_BUILD_TESTS AND STRATA_ENABLE_CUDA)
+    add_executable(qsa_topk_active_parity src/kernels/qsa_topk_active_parity.cpp)
+    target_include_directories(qsa_topk_active_parity PRIVATE ${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES})
+    target_link_libraries(qsa_topk_active_parity PRIVATE strata_kernels CUDA::cudart)
+    add_test(NAME qsa_topk_active_parity COMMAND qsa_topk_active_parity)
+    set_tests_properties(qsa_topk_active_parity PROPERTIES SKIP_RETURN_CODE 77)
+  endif()
+  # the QSA selection (block scores on the warp kernel and the tensor-core one, top-k): a GPU bench with a
+  # scorer-vs-scorer comparison and an FP64 accuracy gate (synthetic, no model); the ctest runs a 32K context under a
+  # 262144-cell capacity (the top-k dispatch follows the capacity) and exits non-zero on a failed gate
+  if((STRATA_BUILD_TESTS OR STRATA_PARITY_PROMPT_ATTN) AND STRATA_ENABLE_HIP)
+    add_executable(qsa_select_bench src/kernels/qsa_select_bench.cpp)
+    set_source_files_properties(src/kernels/qsa_select_bench.cpp PROPERTIES LANGUAGE HIP)
+    target_link_libraries(qsa_select_bench PRIVATE strata_kernels strata_hip_runtime)
+    add_test(NAME qsa_select_bench COMMAND qsa_select_bench 32768 256 3 262144)
+  endif()
   # plan v0.3 P6: llama.cpp's block layouts and codebook grids for the i-quant formats (MIT, third_party/ggml)
   target_include_directories(strata_kernels PRIVATE third_party/ggml)
   # Match the pinned llama.cpp CUDA arithmetic contract for this adapter only.
@@ -250,7 +395,7 @@ if(STRATA_ENABLE_CUDA OR STRATA_ENABLE_HIP)
       src/kernels/cuda/native_flash_attn.cu
       src/kernels/cuda/native_qsa_indexer.cu
       src/kernels/cuda/native_qsa_score.cu
-      PROPERTIES COMPILE_OPTIONS "--use_fast_math")
+      PROPERTIES COMPILE_OPTIONS "$<IF:$<BOOL:${STRATA_HIP_GFX906}>,,--use_fast_math>")
   elseif(STRATA_ENABLE_HIP)
     # Keep Q8_K's multiply and magic-bias rounding as separate FP32 operations on HIP; contraction to an
     # FMA changes tie results. The embedding gather also relies on separately rounded operations.
@@ -289,15 +434,79 @@ if(STRATA_ENABLE_CUDA OR STRATA_ENABLE_HIP)
   # library is the honest shape of the dependency graph: core knows about devices and weights, kernels know
   # about arithmetic, and this one knows the ORDER the arithmetic happens in.
   add_library(strata_engine STATIC src/core/layer.cpp src/core/session.cpp src/core/expert_source.cpp src/core/remote_experts.cpp
-    src/core/expert_cache.cpp src/core/native_head.cpp src/core/native_dense.cpp src/core/verify.cpp src/core/mtp.cpp)
+    src/core/remote_expert_opt.cu
+    src/core/expert_cache.cpp src/core/native_head.cpp src/core/native_dense.cpp src/core/verify.cpp src/core/mtp.cpp
+    src/core/conversation_snapshot.cpp src/core/conversation_state.cpp src/core/conversation_memory.cpp src/core/peer_experts.cpp
+    src/core/arch_defaults.cpp src/core/vmm.cpp src/core/conversation_file.cpp)
   target_include_directories(strata_engine PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/include)
   target_link_libraries(strata_engine PUBLIC strata_core strata_kernels strata_kernels_cpu ${_strata_gpu_runtime_target})
+  if(STRATA_BUILD_TESTS AND EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/tests/core/exchange_storage_test.cpp")
+    add_executable(exchange_storage_test tests/core/exchange_storage_test.cpp)
+    target_include_directories(exchange_storage_test PRIVATE include)
+    target_link_libraries(exchange_storage_test PRIVATE Threads::Threads)
+    add_test(NAME exchange_storage_test COMMAND exchange_storage_test)
+    set_tests_properties(exchange_storage_test PROPERTIES TIMEOUT 60)
+  endif()
+
   if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/tests/core/file_expert_source_test.cpp")
     enable_testing()
     add_executable(file_expert_source_test tests/core/file_expert_source_test.cpp)
     target_link_libraries(file_expert_source_test PRIVATE strata_engine)
     add_test(NAME file_expert_source_test COMMAND file_expert_source_test)
     set_tests_properties(file_expert_source_test PROPERTIES TIMEOUT 60)
+    add_executable(vmm_test tests/core/vmm_test.cpp)
+    target_link_libraries(vmm_test PRIVATE strata_engine)
+    add_test(NAME vmm_test COMMAND vmm_test)
+    set_tests_properties(vmm_test PROPERTIES TIMEOUT 60)
+  endif()
+  # #477: the learned expert profile (--expert-profile-save): its order and make_profile.py's file format (CPU only)
+  if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/tests/core/expert_profile_save_test.cpp")
+    enable_testing()
+    add_executable(expert_profile_save_test tests/core/expert_profile_save_test.cpp)
+    target_link_libraries(expert_profile_save_test PRIVATE strata_engine)
+    add_test(NAME expert_profile_save_test COMMAND expert_profile_save_test)
+    set_tests_properties(expert_profile_save_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)
+    # #369: per-layer admission on a sized (native pack) cache starts each layer in its own range
+    add_executable(expert_cache_per_layer_test tests/core/expert_cache_per_layer_test.cpp)
+    target_link_libraries(expert_cache_per_layer_test PRIVATE strata_engine)
+    add_test(NAME expert_cache_per_layer_test COMMAND expert_cache_per_layer_test)
+    set_tests_properties(expert_cache_per_layer_test PROPERTIES SKIP_RETURN_CODE 77 TIMEOUT 60)
+    add_executable(expert_cache_segmented_test tests/core/expert_cache_segmented_test.cpp)
+    target_link_libraries(expert_cache_segmented_test PRIVATE strata_engine)
+    add_test(NAME expert_cache_segmented_test COMMAND expert_cache_segmented_test)
+    set_tests_properties(expert_cache_segmented_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)
+    add_test(NAME conversation_snapshot_test COMMAND conversation_snapshot_test)
+    set_tests_properties(conversation_snapshot_test PROPERTIES SKIP_RETURN_CODE 77 TIMEOUT 120)
+    add_executable(conversation_validation_test src/core/conversation_validation_test.cpp)
+    target_link_libraries(conversation_validation_test PRIVATE strata_engine)
+    add_test(NAME conversation_validation_test COMMAND conversation_validation_test)
+    set_tests_properties(conversation_validation_test PROPERTIES ENVIRONMENT "CUDA_VISIBLE_DEVICES=-1" TIMEOUT 120)
+    if(NOT STRATA_ENABLE_HIP AND UNIX AND NOT APPLE AND CMAKE_CXX_COMPILER_ID MATCHES "GNU|Clang")
+      add_executable(conversation_transfer_test src/core/conversation_validation_test.cpp)
+      target_compile_definitions(conversation_transfer_test PRIVATE CONVERSATION_TEST_TRANSFERS)
+      target_link_options(conversation_transfer_test PRIVATE
+        -Wl,--wrap=cudaMemcpy -Wl,--wrap=cudaDeviceSynchronize -Wl,--wrap=cudaGetLastError)
+      target_link_libraries(conversation_transfer_test PRIVATE strata_engine)
+      add_test(NAME conversation_transfer_test COMMAND conversation_transfer_test)
+      set_tests_properties(conversation_transfer_test PROPERTIES ENVIRONMENT "CUDA_VISIBLE_DEVICES=-1" TIMEOUT 120)
+    endif()
   endif()
 
   # `strata generate` (P2.S6).  The FIRST user-facing program: everything under it is a component, and this is
@@ -305,6 +514,10 @@ if(STRATA_ENABLE_CUDA OR STRATA_ENABLE_HIP)
   # against llama.cpp on the SAME ids and a tokenizer on one side of that comparison is a second variable.
   # plan v0.3 P5: batched prompt processing (tensor-core GEMMs through cuBLAS).
   add_library(strata_prefill STATIC src/prefill/gemm.cu src/prefill/kernels.cu src/prefill/prefill.cpp)
+  if(STRATA_ENABLE_HIP)
+    # #313: the RDNA3 WMMA GEMMs include hip_runtime.h: AMD builds only (a CUDA build never sees this file)
+    target_sources(strata_prefill PRIVATE src/prefill/wmma_gemm.cu)
+  endif()
   target_include_directories(strata_prefill PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/include ${_strata_gpu_include_directories})
   target_link_libraries(strata_prefill PUBLIC strata_engine ${_strata_gpu_blas_target} ${_strata_gpu_runtime_target})
   if(STRATA_ENABLE_HIP AND STRATA_HIPBLASLT_AVAILABLE)
@@ -322,6 +535,18 @@ if(STRATA_ENABLE_CUDA OR STRATA_ENABLE_HIP)
   if(STRATA_ENABLE_HIP)
     enable_testing()
     set(_strata_hip_tests)
+    add_executable(hip_prefill_gemm tests/hip/prefill_gemm.cpp)
+    target_link_libraries(hip_prefill_gemm PRIVATE strata_prefill strata_hip_runtime)
+    add_test(NAME hip_prefill_gemm COMMAND hip_prefill_gemm)
+    list(APPEND _strata_hip_tests hip_prefill_gemm)
+    add_executable(hip_gemm_f16_io_parity tests/hip/gemm_f16_io_parity.cpp)
+    target_link_libraries(hip_gemm_f16_io_parity PRIVATE strata_prefill strata_hip_runtime)
+    add_test(NAME hip_gemm_f16_io_parity COMMAND hip_gemm_f16_io_parity)
+    list(APPEND _strata_hip_tests hip_gemm_f16_io_parity)
+    add_executable(hip_q2_zero tests/hip/q2_zero.cpp)
+    target_link_libraries(hip_q2_zero PRIVATE strata_kernels strata_hip_runtime)
+    add_test(NAME hip_q2_zero COMMAND hip_q2_zero)
+    list(APPEND _strata_hip_tests hip_q2_zero)
     add_executable(hip_expert_cache_staging tests/hip/expert_cache_staging.cpp)
     target_link_libraries(hip_expert_cache_staging PRIVATE strata_engine)
     add_test(NAME hip_expert_cache_staging COMMAND hip_expert_cache_staging)
@@ -338,6 +563,15 @@ if(STRATA_ENABLE_CUDA OR STRATA_ENABLE_HIP)
       add_test(NAME hip_handoff COMMAND hip_handoff)
       list(APPEND _strata_hip_tests hip_handoff)
     endif()
+    if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/tests/hip/mapped_alias.cpp")
+      # #325: reports what a hipHostGetDevicePointer alias is on this stack (on Windows: the host pointer, and a
+      # D2D copy into it does not land - hip_handoff's timeout there). A diagnosis: exits 0 whenever it ran.
+      add_executable(hip_mapped_alias tests/hip/mapped_alias.cpp)
+      target_link_libraries(hip_mapped_alias PRIVATE strata_kernels strata_hip_runtime)
+      add_test(NAME hip_mapped_alias COMMAND hip_mapped_alias)
+      set_tests_properties(hip_mapped_alias PROPERTIES SKIP_RETURN_CODE 77)
+      list(APPEND _strata_hip_tests hip_mapped_alias)
+    endif()
     if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/tests/hip/native_qsa_score.cpp")
       add_executable(hip_native_qsa_score tests/hip/native_qsa_score.cpp)
       target_link_libraries(hip_native_qsa_score PRIVATE strata_kernels strata_hip_runtime)
@@ -350,6 +584,34 @@ if(STRATA_ENABLE_CUDA OR STRATA_ENABLE_HIP)
       add_test(NAME hip_prefill_native_batch COMMAND hip_prefill_native_batch)
       list(APPEND _strata_hip_tests hip_prefill_native_batch)
     endif()
+    # Aurora: the prompt GDN recurrence's per-head kernel against the column-split + norm kernels, bitwise
+    add_executable(hip_gdn_rec_head tests/hip/gdn_rec_head.cpp)
+    target_link_libraries(hip_gdn_rec_head PRIVATE strata_prefill strata_hip_runtime)
+    add_test(NAME hip_gdn_rec_head COMMAND hip_gdn_rec_head)
+    list(APPEND _strata_hip_tests hip_gdn_rec_head)
+    # #313: the RDNA3 / RDNA3.5 WMMA dense GEMMs (opt-in STRATA_WMMA_GEMM=1) against a reference
+    add_executable(hip_prefill_wmma_gemm_parity tests/hip/prefill_wmma_gemm_parity.cpp)
+    target_include_directories(hip_prefill_wmma_gemm_parity PRIVATE "${CMAKE_CURRENT_SOURCE_DIR}/src/prefill")
+    target_link_libraries(hip_prefill_wmma_gemm_parity PRIVATE strata_prefill strata_hip_runtime)
+    add_test(NAME hip_prefill_wmma_gemm_parity COMMAND hip_prefill_wmma_gemm_parity)
+    list(APPEND _strata_hip_tests hip_prefill_wmma_gemm_parity)
+    # S (STRATA_HCD_EXACT): the exact HC down kernel against hipBLASLt solution 1176 / 1177 (needs STRATA_HIPBLASLT_TUNING; exit 77 else)
+    add_executable(hip_prefill_hcd_exact_parity tests/hip/prefill_hcd_exact_parity.cpp)
+    target_link_libraries(hip_prefill_hcd_exact_parity PRIVATE strata_prefill strata_hip_runtime)
+    add_test(NAME hip_prefill_hcd_exact_parity COMMAND hip_prefill_hcd_exact_parity)
+    set_tests_properties(hip_prefill_hcd_exact_parity PROPERTIES SKIP_RETURN_CODE 77)
+    list(APPEND _strata_hip_tests hip_prefill_hcd_exact_parity)
+    # 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")
@@ -385,11 +647,110 @@ 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)
+    add_executable(mmvq_multi_parity src/kernels/mmvq_multi_parity.cpp)
+    target_include_directories(mmvq_multi_parity PRIVATE ${_strata_gpu_include_directories})
+    target_link_libraries(mmvq_multi_parity PRIVATE strata_kernels ${_strata_gpu_runtime_target})
+    add_test(NAME mmvq_multi_parity COMMAND mmvq_multi_parity)
+    if(STRATA_HIP_GFX906)   # the CUDA layout, where the negative control has power (the wave64 one is exact at every T)
+      add_test(NAME mmvq_multi_parity_cuda_layout COMMAND mmvq_multi_parity)
+      set_tests_properties(mmvq_multi_parity_cuda_layout PROPERTIES ENVIRONMENT "STRATA_MMVQ_WAVE=0")
+    endif()
+  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)
+  # the prompt path's DeltaNet recurrence: gdn_rec_kh_kernel against gdn_rec_cols_pipe_kernel, bitwise (GPU,
+  # synthetic, no model; --bench adds timings).  NVIDIA only: cp.async and the CUDA occupancy API (not on gfx906)
+  if(STRATA_ENABLE_CUDA AND NOT STRATA_HIP_GFX906)
+    # Independent pinned expert uploads: byte parity and stream/event ordering.
+    add_executable(dma_batch_parity tests/core/dma_batch_parity.cu)
+    target_include_directories(dma_batch_parity PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include)
+    target_link_libraries(dma_batch_parity PRIVATE ${_strata_gpu_runtime_target})
+    add_test(NAME dma_batch_parity COMMAND dma_batch_parity)
+    set_tests_properties(dma_batch_parity PROPERTIES TIMEOUT 120)
+    if(WIN32)
+      add_test(NAME dma_batch_registered_parity COMMAND dma_batch_parity --registered)
+      set_tests_properties(dma_batch_registered_parity PROPERTIES TIMEOUT 120)
+    endif()
+    add_executable(gdn_rec_parity src/prefill/gdn_rec_parity.cu)
+    target_link_libraries(gdn_rec_parity PRIVATE ${_strata_gpu_runtime_target})
+    add_test(NAME gdn_rec_parity COMMAND gdn_rec_parity)
+    # the prompt path's BF16 products against cuBLAS's own BF16 GEMM, within fp32 rounding (sm_7x runs them on the
+    # FP16 tensor cores; GPU, synthetic, no model; --bench adds timings)
+    add_executable(gemm_bf16_parity src/prefill/gemm_bf16_parity.cu)
+    target_link_libraries(gemm_bf16_parity PRIVATE strata_prefill )
+    add_test(NAME gemm_bf16_parity COMMAND gemm_bf16_parity)
+  endif()
+  # native_expert_grouped's group stride and fused SwiGLU + q8_1, bitwise vs the launches before them (GPU, synthetic,
+  # no model; --bench adds v1/new timings of a window's 48 calls in a graph)
+  add_executable(native_grouped_parity src/kernels/native_grouped_parity.cpp)
+  target_include_directories(native_grouped_parity PRIVATE ${_strata_gpu_include_directories})
+  target_link_libraries(native_grouped_parity PRIVATE strata_kernels ${_strata_gpu_runtime_target})
+  add_test(NAME native_grouped_parity COMMAND native_grouped_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})
   add_test(NAME shared_expert_parity COMMAND shared_expert_parity --selftest)
+  # the split / batched verify-window kernels (from eddoursul's fork), bitwise vs the kernels they replace; CUDA only
+  if(STRATA_ENABLE_CUDA)
+    add_executable(verify_parity src/kernels/verify_parity.cpp)
+    target_include_directories(verify_parity PRIVATE ${_strata_gpu_include_directories})
+    target_link_libraries(verify_parity PRIVATE strata_kernels ${_strata_gpu_runtime_target})
+    add_test(NAME verify_parity COMMAND verify_parity --selftest)
+  endif()
 
+  add_executable(fused_gr_bench src/kernels/fused_gr_bench.cpp)
+  target_include_directories(fused_gr_bench PRIVATE ${_strata_gpu_include_directories})
+  target_link_libraries(fused_gr_bench PRIVATE strata_kernels ${_strata_gpu_runtime_target})
   add_executable(gr_parity src/kernels/gr_parity.cpp)
   target_include_directories(gr_parity PRIVATE ${_strata_gpu_include_directories})
   target_link_libraries(gr_parity PRIVATE strata_kernels ${_strata_gpu_runtime_target})
@@ -409,6 +770,20 @@ if(STRATA_ENABLE_CUDA OR STRATA_ENABLE_HIP)
   target_include_directories(sampler_parity PRIVATE ${_strata_gpu_include_directories})
   target_link_libraries(sampler_parity PRIVATE strata_kernels ${_strata_gpu_runtime_target})
   add_test(NAME sampler_parity COMMAND sampler_parity --selftest)
+  set_tests_properties(sampler_parity PROPERTIES ENVIRONMENT "STRATA_OLD_SAMPLER=0;STRATA_SAMPLER_ONE_BLOCK=0")
+  # the same fixtures on the other two sampled paths: the one-block kernel and the 0.1.20 kernel
+  add_test(NAME sampler_parity_one_block COMMAND sampler_parity --selftest)
+  set_tests_properties(sampler_parity_one_block PROPERTIES ENVIRONMENT "STRATA_OLD_SAMPLER=0;STRATA_SAMPLER_ONE_BLOCK=1")
+  add_test(NAME sampler_parity_old COMMAND sampler_parity --selftest)
+  set_tests_properties(sampler_parity_old PROPERTIES ENVIRONMENT "STRATA_OLD_SAMPLER=1;STRATA_SAMPLER_ONE_BLOCK=0")
+  # S19: the decode QSA top-k and the greedy argmax on thread-block clusters (sm_90+) vs the one-CTA kernels, bitwise,
+  # captured in a graph too (GPU, synthetic, no model; skips below sm_90); --bench: per-call times
+  if(STRATA_ENABLE_CUDA)
+    add_executable(decode_cluster_parity src/kernels/decode_cluster_parity.cpp)
+    target_include_directories(decode_cluster_parity PRIVATE ${_strata_gpu_include_directories})
+    target_link_libraries(decode_cluster_parity PRIVATE strata_kernels ${_strata_gpu_runtime_target})
+    add_test(NAME decode_cluster_parity COMMAND decode_cluster_parity --selftest)
+  endif()
 
   add_executable(rope_parity src/kernels/rope_parity.cpp)
   target_include_directories(rope_parity PRIVATE ${_strata_gpu_include_directories})
@@ -420,10 +795,42 @@ if(STRATA_ENABLE_CUDA OR STRATA_ENABLE_HIP)
   target_link_libraries(quantize_act_parity PRIVATE strata_kernels ${_strata_gpu_runtime_target})
   add_test(NAME quantize_act_parity COMMAND quantize_act_parity --selftest)
 
+  # the verify window's routing in one multi-column BF16 projection + one top-k, bitwise the per-token calls
+  add_executable(route_window_parity src/kernels/route_window_parity.cpp)
+  target_include_directories(route_window_parity PRIVATE ${_strata_gpu_include_directories})
+  target_link_libraries(route_window_parity PRIVATE strata_kernels ${_strata_gpu_runtime_target})
+  add_test(NAME route_window_parity COMMAND route_window_parity)
+
   add_executable(router_top10_parity src/kernels/router_top10_parity.cpp)
   target_include_directories(router_top10_parity PRIVATE ${_strata_gpu_include_directories})
   target_link_libraries(router_top10_parity PRIVATE strata_kernels ${_strata_gpu_runtime_target})
   add_test(NAME router_top10_parity COMMAND router_top10_parity --selftest)
+  # #783 PR-c: the multi-token router and the k = 10 float4 combine, bitwise against the single-token contracts
+  add_executable(native_multi_parity src/kernels/native_multi_parity.cpp)
+  target_include_directories(native_multi_parity PRIVATE ${_strata_gpu_include_directories})
+  target_link_libraries(native_multi_parity PRIVATE strata_kernels ${_strata_gpu_runtime_target})
+  add_test(NAME native_multi_parity COMMAND native_multi_parity)
+  # #783 PR-d: the batched K/V append (INT8, Q4_0, the hybrid fold) against the per-cell appends, memcmp
+  add_executable(kv_steps_parity src/kernels/kv_steps_parity.cpp)
+  target_include_directories(kv_steps_parity PRIVATE ${_strata_gpu_include_directories})
+  target_link_libraries(kv_steps_parity PRIVATE strata_kernels ${_strata_gpu_runtime_target})
+  add_test(NAME kv_steps_parity COMMAND kv_steps_parity)
+  # #783 PR-e: the device-built expert plan (parallel grouping + prefix scan) against a host replay of its contract
+  add_executable(resident_plan_parity src/kernels/resident_plan_parity.cpp)
+  target_include_directories(resident_plan_parity PRIVATE ${_strata_gpu_include_directories})
+  target_link_libraries(resident_plan_parity PRIVATE strata_kernels ${_strata_gpu_runtime_target})
+  add_test(NAME resident_plan_parity COMMAND resident_plan_parity)
+  # #783 PR-g: the multi-token hyper-connection read/write (exact-T projections under them) against the single-token calls
+  add_executable(gr_multi_parity src/kernels/gr_multi_parity.cpp)
+  target_include_directories(gr_multi_parity PRIVATE ${_strata_gpu_include_directories})
+  target_link_libraries(gr_multi_parity PRIVATE strata_kernels ${_strata_gpu_runtime_target})
+  add_test(NAME gr_multi_parity COMMAND gr_multi_parity)
+  if(STRATA_HIP_GFX906)
+    # the S6 router (STRATA_HIP_ROUTER_FAST=1 on gfx906) against the portable kernel, bitwise, on wave64
+    add_executable(hip_router_fast tests/hip/router_fast.cpp)
+    target_include_directories(hip_router_fast PRIVATE ${_strata_gpu_include_directories})
+    target_link_libraries(hip_router_fast PRIVATE strata_kernels ${_strata_gpu_runtime_target})
+  endif()
 
   add_executable(s_gemv_parity src/kernels/s_gemv_parity.cpp)
   target_include_directories(s_gemv_parity PRIVATE ${_strata_gpu_include_directories})
@@ -450,6 +857,13 @@ if(STRATA_ENABLE_CUDA OR STRATA_ENABLE_HIP)
   target_link_libraries(qsa_parity PRIVATE strata_kernels ${_strata_gpu_runtime_target})
   add_test(NAME qsa_parity COMMAND qsa_parity --selftest)
 
+  # the QSA top-k the engine dispatches against the original kernel: identical ids on both sides of the register
+  # kernel's reach, with tied and with equal scores (synthetic, no model)
+  add_executable(qsa_topk_parity src/kernels/qsa_topk_parity.cpp)
+  target_include_directories(qsa_topk_parity PRIVATE ${_strata_gpu_include_directories})
+  target_link_libraries(qsa_topk_parity PRIVATE strata_kernels ${_strata_gpu_runtime_target})
+  add_test(NAME qsa_topk_parity COMMAND qsa_topk_parity --selftest)
+
   # ---- P2.S4: the PLE n-gram hash, the IQ4_NL table read, and the PLE block ----
   # `ngram.cpp` is host-only but lives in `strata_kernels` because `ple.cu` shares its geometry constants, and
   # splitting them across two libraries would only add a layer.
@@ -494,12 +908,19 @@ 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)
-  add_executable(native_mmvq_multi bench/micro/native_mmvq_multi.cpp)
-  target_include_directories(native_mmvq_multi PRIVATE ${_strata_gpu_include_directories})
-  target_link_libraries(native_mmvq_multi PRIVATE strata_kernels ${_strata_gpu_runtime_target})
-  add_test(NAME native_mmvq_multi COMMAND native_mmvq_multi --selftest)
+  if(STRATA_BUILD_TESTS AND EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/bench/micro/native_mmvq_multi.cpp")
+    add_executable(native_mmvq_multi bench/micro/native_mmvq_multi.cpp)
+    target_include_directories(native_mmvq_multi PRIVATE ${_strata_gpu_include_directories})
+    target_link_libraries(native_mmvq_multi PRIVATE strata_kernels ${_strata_gpu_runtime_target})
+    add_test(NAME native_mmvq_multi COMMAND native_mmvq_multi --selftest)
   endif()
 
   # Plan v0.3 P0.1: working-set lock of a host region (CPU only).
@@ -507,6 +928,12 @@ if(STRATA_ENABLE_CUDA OR STRATA_ENABLE_HIP)
   target_link_libraries(platform_memory_test PRIVATE strata_core ${_strata_gpu_runtime_target})
   add_test(NAME platform_memory_test COMMAND platform_memory_test)
 
+  if(CMAKE_SYSTEM_NAME STREQUAL "Linux")
+    add_executable(pinned_shared_test src/core/pinned_shared_test.cpp)
+    target_link_libraries(pinned_shared_test PRIVATE strata_core ${_strata_gpu_runtime_target})
+    add_test(NAME pinned_shared_test COMMAND pinned_shared_test)
+  endif()
+
   # Plan v0.3 P2: the n-gram table read straight from the SSD. The selftest needs only the CPU and a temp file.
   add_executable(ple_reader_test src/ngram/ple_reader_test.cpp)
   target_include_directories(ple_reader_test PRIVATE ${_strata_gpu_include_directories})
@@ -563,9 +990,13 @@ endif()
 #     worse opt-out.  `cpu_require_expert_support()` therefore refuses at startup on a CPU without the
 #     features, and the fallback exists for tests, exactly as P2.S3 specifies.
 # Plan v0.3 P6: speculation components that need no GPU.
-add_library(strata_spec STATIC src/spec/suffix_drafter.cpp src/spec/controller.cpp src/spec/draft_policy.cpp)
+add_library(strata_spec STATIC src/spec/suffix_drafter.cpp src/spec/controller.cpp src/spec/draft_policy.cpp
+            src/spec/draft_source.cpp)
 target_include_directories(strata_spec PUBLIC include)
 if(STRATA_BUILD_TESTS)
+  add_executable(arch_defaults_test src/core/arch_defaults_test.cpp src/core/arch_defaults.cpp)
+  target_include_directories(arch_defaults_test PRIVATE include)
+  add_test(NAME arch_defaults_test COMMAND arch_defaults_test)
   add_executable(suffix_drafter_test src/spec/suffix_drafter_test.cpp)
   target_link_libraries(suffix_drafter_test PRIVATE strata_spec)
   add_test(NAME suffix_drafter_test COMMAND suffix_drafter_test)
@@ -575,10 +1006,23 @@ if(STRATA_BUILD_TESTS)
   add_executable(draft_policy_test src/spec/draft_policy_test.cpp)
   target_link_libraries(draft_policy_test PRIVATE strata_spec)
   add_test(NAME draft_policy_test COMMAND draft_policy_test)
+endif()
+if(STRATA_BUILD_TESTS OR STRATA_BUILD_CONVERSATION_TESTS)
+  add_executable(message_boundary_test src/program/message_boundary_test.cpp)
+  target_include_directories(message_boundary_test PRIVATE include)
+  add_test(NAME message_boundary_test COMMAND message_boundary_test)
   # the serve loop's conversation-cache retention policy (header-only, no library to link)
   add_executable(conv_cache_test src/program/conv_cache_test.cpp)
   target_include_directories(conv_cache_test PRIVATE include)
   add_test(NAME conv_cache_test COMMAND conv_cache_test)
+  # coupled draft sampling's counter and penalty-ring arithmetic (header-only, CPU)
+  add_executable(coupled_draft_test src/core/coupled_draft_test.cpp)
+  target_include_directories(coupled_draft_test PRIVATE include)
+  add_test(NAME coupled_draft_test COMMAND coupled_draft_test)
+  # the f32 -> bf16 rounding against ggml's, NaN included, over every f32 bit pattern (header-only, CPU)
+  add_executable(bf16_bits_test src/kernels/bf16_bits_test.cpp)
+  target_include_directories(bf16_bits_test PRIVATE include)
+  add_test(NAME bf16_bits_test COMMAND bf16_bits_test)
 endif()
 
 add_library(strata_kernels_cpu STATIC src/kernels/cpu/expert.cpp src/kernels/cpu/pool.cpp
@@ -595,6 +1039,34 @@ else()
                               PROPERTIES COMPILE_OPTIONS "-mavx512f;-mavx512bw;-mavx512vl;-mavx512dq;-mavx512vnni;-mavx512vbmi;-mf16c;-mfma")
   set_source_files_properties(src/kernels/cpu/q2_avx2.cpp PROPERTIES COMPILE_OPTIONS "-mavx2;-mfma;-mf16c")
 endif()
+# AVX-VNNI (Alder Lake, Sapphire Rapids and later): the AVX-2 expert kernels' vpdpwssd / vpdpbusd copies (q2_avx2.cpp,
+# iq_avx2.cpp; *_rows.inl), taken at run time where cpu_avxvnni_ok().  GCC and Clang compile only those functions with
+# target("avxvnni") - never -mavxvnni on a file, which would let the compiler use it anywhere - and MSVC takes the
+# intrinsics under /arch:AVX2.  A compiler without them (GCC < 11, Clang < 12) builds the files without the copies.
+include(CheckCXXSourceCompiles)
+if(MSVC)
+  set(CMAKE_REQUIRED_FLAGS "/arch:AVX2")
+else()
+  set(CMAKE_REQUIRED_FLAGS "-mavx2 -mfma -mf16c")
+endif()
+check_cxx_source_compiles([=[
+#include <immintrin.h>
+#if defined(__GNUC__) || defined(__clang__)
+__attribute__((target("avxvnni")))
+#endif
+static __m256i dots(__m256i a, __m256i b) { return _mm256_dpbusd_avx_epi32(_mm256_dpwssd_avx_epi32(a, a, b), a, b); }
+int main() {
+    volatile int x = 1;
+    return _mm256_extract_epi32(dots(_mm256_set1_epi32(x), _mm256_set1_epi32(x)), 0) == 0;
+}
+]=] STRATA_HAVE_AVXVNNI_INTRINSICS)
+unset(CMAKE_REQUIRED_FLAGS)
+if(STRATA_HAVE_AVXVNNI_INTRINSICS)
+  set(_strata_avxvnni STRATA_AVXVNNI=1)
+else()
+  set(_strata_avxvnni STRATA_AVXVNNI=0)
+endif()
+set_property(SOURCE src/kernels/cpu/q2_avx2.cpp APPEND PROPERTY COMPILE_DEFINITIONS ${_strata_avxvnni})
 find_package(Threads REQUIRED)
 target_link_libraries(strata_kernels_cpu PUBLIC Threads::Threads)
 
@@ -608,6 +1080,9 @@ 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)
@@ -622,6 +1097,22 @@ if(STRATA_NATIVE_EXPERTS)
     foreach(_f GGML_AVX GGML_AVX2 GGML_FMA GGML_F16C GGML_BMI2)
       set(${_f} ON CACHE BOOL "" FORCE)
     endforeach()
+  elseif(STRATA_ISA_FLOOR)
+    # the experimental older-CPU build (STRATA_ISA_FLOOR above): one ggml-cpu for the floor, ggml's "sandybridge"
+    # feature set (SSE4.2 + AVX) or SSE4.2 alone - not the build host's, which may be newer than the PC it runs on.
+    # From the Strata_Dirigo fork (rwkeyes).  ggml's defaults turn the rest on when GGML_NATIVE is off: all off here.
+    set(GGML_NATIVE OFF CACHE BOOL "" FORCE)
+    set(GGML_CPU_ALL_VARIANTS OFF CACHE BOOL "" FORCE)
+    set(GGML_SSE42 ON CACHE BOOL "" FORCE)
+    if(STRATA_ISA_FLOOR STREQUAL "avx")
+      set(GGML_AVX ON CACHE BOOL "" FORCE)
+    else()
+      set(GGML_AVX OFF CACHE BOOL "" FORCE)
+    endif()
+    foreach(_f GGML_AVX2 GGML_FMA GGML_F16C GGML_BMI2 GGML_AVX_VNNI GGML_AVX512 GGML_AVX512_VBMI GGML_AVX512_VNNI
+               GGML_AVX512_BF16 GGML_AMX_TILE GGML_AMX_INT8 GGML_AMX_BF16)
+      set(${_f} OFF CACHE BOOL "" FORCE)
+    endforeach()
   else()
     set(GGML_NATIVE ON CACHE BOOL "" FORCE)
   endif()
@@ -642,18 +1133,56 @@ 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
+                                           src/kernels/cpu/kq_avx1.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")
+    # the router lookahead's AVX1 dot (older CPUs; called behind cpu_avx1_ok): no FMA, no 256-bit integer ops
+    set_source_files_properties(src/kernels/cpu/kq_avx1.cpp PROPERTIES COMPILE_OPTIONS "/arch:AVX")
   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")
+    set_source_files_properties(src/kernels/cpu/kq_avx1.cpp PROPERTIES COMPILE_OPTIONS "-mavx")
+  endif()
+  set_property(SOURCE src/kernels/cpu/iq_avx2.cpp APPEND PROPERTY COMPILE_DEFINITIONS ${_strata_avxvnni})
+  if(STRATA_ISA_FLOOR STREQUAL "avx")
+    target_compile_definitions(strata_kernels_cpu PRIVATE STRATA_ISA_FLOOR_AVX=1)
+  elseif(STRATA_ISA_FLOOR STREQUAL "none")
+    target_compile_definitions(strata_kernels_cpu PRIVATE STRATA_ISA_FLOOR_NONE=1)
   endif()
   target_link_libraries(strata_kernels_cpu PUBLIC ggml-cpu ggml-base)
   target_compile_definitions(strata_kernels_cpu PUBLIC STRATA_NATIVE_EXPERTS=1)
+  if(STRATA_BUILD_TESTS)
+    # the Q5_1 block dequantizer (a Q5_1 PLE table) against ggml's, synthetic blocks: no model, no GPU
+    add_executable(dequant_q5_1_test tests/core/dequant_q5_1_test.cpp)
+    target_link_libraries(dequant_q5_1_test PRIVATE strata_artifact ggml-base)
+    add_test(NAME dequant_q5_1_test COMMAND dequant_q5_1_test)
+  endif()
+  # the router lookahead's dot on each rung this CPU has (AVX2, the older CPUs' AVX1) against a double reference
+  add_executable(router_dot_parity src/kernels/cpu/router_dot_parity.cpp)
+  target_link_libraries(router_dot_parity PRIVATE strata_kernels_cpu)
+  if(STRATA_BUILD_TESTS)
+    add_test(NAME router_dot_parity COMMAND router_dot_parity)
+    add_test(NAME router_dot_parity_avx COMMAND ${CMAKE_COMMAND} -E env STRATA_FORCE_ISA=avx $<TARGET_FILE:router_dot_parity>)
+  endif()
+  # the AVX-2 expert kernels' variants (iq_avx2.cpp, q2_avx2.cpp) bit for bit against each other at 1..8 tokens and
+  # against ggml-cpu's dot, on random blocks; --bench: ms per expert on one thread, the weights streamed from DRAM
+  add_executable(iq_avx2_parity src/kernels/cpu/iq_avx2_parity.cpp)
+  target_link_libraries(iq_avx2_parity PRIVATE strata_kernels_cpu)
+  if(STRATA_BUILD_TESTS)
+    add_test(NAME iq_avx2_parity COMMAND iq_avx2_parity)
+    add_test(NAME iq_avx2_parity_iq3s_mt1 COMMAND ${CMAKE_COMMAND} -E env STRATA_IQ3S_MT1=1 $<TARGET_FILE:iq_avx2_parity>)
+  endif()
+  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
@@ -664,8 +1193,21 @@ if(STRATA_NATIVE_EXPERTS)
       set(_strata_gg ${strata_llamacpp_SOURCE_DIR}/ggml)
     endif()
     set(_strata_mmq ${_strata_gg}/src/ggml-cuda)
-    add_library(strata_mmq STATIC src/prefill/moe_mmq.cu src/prefill/ggml_cuda_host.cu ${_strata_mmq}/quantize.cu)
-    foreach(_t q2_0 iq2_xxs iq2_xs iq2_s iq3_xxs iq3_s iq4_nl iq4_xs q8_0)   # q8_0: the drafter's matrices (E-9)
+    # #136: the Q2_0 pack's prompt experts on Strata's own int8 kernels (moe_fused.cu, opt-in STRATA_PF_FUSED=1; CUDA
+    # only: the HIP branch below keeps MMQ, and an sm_75 device or an sm_75-only build reports them unavailable)
+    add_library(strata_mmq STATIC src/prefill/moe_mmq.cu src/prefill/moe_fused.cu src/prefill/moe_fused_iq.cu
+                src/prefill/ggml_cuda_host.cu ${_strata_mmq}/quantize.cu)
+    # 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); q6_k is HIP only
+    set(_strata_mmq_cuda_types ${_strata_mmq_types})
+    if(STRATA_MMQ_KQUANTS)
+      list(APPEND _strata_mmq_cuda_types q4_k q5_k q5_1)
+      if(STRATA_Q6K_EXPERTS)   # UD-Q6_K_XL gate/up (docs/UNSLOTH_Q6.md)
+        list(APPEND _strata_mmq_cuda_types q6_k)
+      endif()
+    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}
@@ -675,8 +1217,33 @@ 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)
+    target_compile_definitions(strata_prefill PUBLIC STRATA_PREFILL_MMQ=1 STRATA_PREFILL_FUSED=1)
+    if(STRATA_BUILD_TESTS)
+      # #136: the fused int8 experts against a double-precision reference and the MMQ path (exit 77 below sm_80)
+      add_executable(prefill_fused_moe_test tests/cuda/prefill_fused_moe_test.cpp)
+      target_link_libraries(prefill_fused_moe_test PRIVATE strata_mmq ggml-base ${_strata_gpu_runtime_target})
+      add_test(NAME prefill_fused_moe_test COMMAND prefill_fused_moe_test)
+      set_tests_properties(prefill_fused_moe_test PROPERTIES SKIP_RETURN_CODE 77 TIMEOUT 300)
+      # #136: the same for the native packs' formats (IQ2_XXS IQ2_XS IQ2_S IQ3_XXS IQ3_S IQ4_XS, down Q2_0 IQ4_NL)
+      add_executable(prefill_fused_iq_test tests/cuda/prefill_fused_iq_test.cpp)
+      target_link_libraries(prefill_fused_iq_test PRIVATE strata_mmq ggml-base ${_strata_gpu_runtime_target})
+      add_test(NAME prefill_fused_iq_test COMMAND prefill_fused_iq_test)
+      set_tests_properties(prefill_fused_iq_test PROPERTIES SKIP_RETURN_CODE 77 TIMEOUT 600)
+    endif()
+    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 ${_strata_gpu_runtime_target})
+      add_test(NAME prefill_mmq_kquant_test COMMAND prefill_mmq_kquant_test)
+      set_tests_properties(prefill_mmq_kquant_test PROPERTIES TIMEOUT 120)
+    endif()
   elseif(STRATA_PREFILL_MMQ)
     # Compile only the pinned GGML MMQ and activation-quantizer sources used by prefill. This uses
     # common.cuh's native HIP path without enabling a second GGML HIP backend.
@@ -689,22 +1256,52 @@ if(STRATA_NATIVE_EXPERTS)
     if(NOT EXISTS "${_strata_mmq}/common.cuh" OR NOT EXISTS "${_strata_mmq}/quantize.cu")
       message(FATAL_ERROR "The selected GGML source is missing the ggml-cuda MMQ sources: ${_strata_mmq}")
     endif()
+    # Aurora: moe_fused.cu's and moe_fused_iq.cu's gfx11 WMMA kernels for the prompt experts (opt-in STRATA_PF_FUSED=1)
     set(_strata_mmq_sources
       src/prefill/moe_mmq.cu
+      src/prefill/moe_fused.cu
+      src/prefill/moe_fused_iq.cu
       src/prefill/ggml_cuda_host.cu
       "${_strata_mmq}/quantize.cu")
-    foreach(_t q2_0 iq2_xxs iq2_xs iq2_s iq3_xxs iq3_s iq4_nl iq4_xs q8_0)
+    if(STRATA_MMQ_KQUANTS)
+      # the same K-quant instances as the CUDA branch: on HIP they serve the dense GGUF projections
+      # of Gemm::native (STRATA_DENSE_MMQ=1), the mixed-quant packs' attention/PLE tensors
+      list(APPEND _strata_mmq_types q4_k q5_k q5_1 q6_k)
+    endif()
+    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(${_strata_mmq_sources} PROPERTIES LANGUAGE HIP)
+    # moe_fused.cu is Strata's own CUDA-shaped source: it takes the HIP compat headers, ggml's MMQ sources do not
+    set_source_files_properties(src/prefill/moe_fused.cu src/prefill/moe_fused_iq.cu
+                                PROPERTIES INCLUDE_DIRECTORIES "${STRATA_HIP_COMPAT_INCLUDE_DIR}")
     add_library(strata_mmq STATIC ${_strata_mmq_sources})
     target_include_directories(strata_mmq
       PRIVATE "${_strata_gg}/include" "${_strata_gg}/src" "${_strata_mmq}"
       PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/include)
     target_compile_definitions(strata_mmq PRIVATE GGML_USE_HIP=1)
+    if(STRATA_MMQ_KQUANTS)
+      target_compile_definitions(strata_mmq PRIVATE STRATA_MMQ_KQUANTS=1)
+    endif()
+    if(STRATA_ORCA_Q4KS_MMQ)
+      target_compile_definitions(strata_mmq PRIVATE STRATA_ORCA_Q4KS_MMQ=1)
+    endif()
     target_link_libraries(strata_mmq PUBLIC ggml-base hip::host roc::hipblas)
     target_link_libraries(strata_prefill PUBLIC strata_mmq)
-    target_compile_definitions(strata_prefill PUBLIC STRATA_PREFILL_MMQ=1)
+    target_compile_definitions(strata_prefill PUBLIC STRATA_PREFILL_MMQ=1 STRATA_PREFILL_FUSED=1)
+    # Aurora: the gfx11 fused experts against the FP64 reference and MMQ (tests/cuda's test; exit 77 off gfx11)
+    add_executable(hip_prefill_fused_moe tests/cuda/prefill_fused_moe_test.cpp)
+    target_include_directories(hip_prefill_fused_moe PRIVATE "${_strata_gg}/include")
+    target_link_libraries(hip_prefill_fused_moe PRIVATE strata_mmq ggml-base strata_hip_runtime)
+    add_test(NAME hip_prefill_fused_moe COMMAND hip_prefill_fused_moe)
+    set_tests_properties(hip_prefill_fused_moe PROPERTIES SKIP_RETURN_CODE 77 TIMEOUT 300)
+    list(APPEND _strata_hip_tests hip_prefill_fused_moe)
+    add_executable(hip_prefill_fused_iq tests/cuda/prefill_fused_iq_test.cpp)
+    target_include_directories(hip_prefill_fused_iq PRIVATE "${_strata_gg}/include")
+    target_link_libraries(hip_prefill_fused_iq PRIVATE strata_mmq ggml-base strata_hip_runtime)
+    add_test(NAME hip_prefill_fused_iq COMMAND hip_prefill_fused_iq)
+    set_tests_properties(hip_prefill_fused_iq PROPERTIES SKIP_RETURN_CODE 77 TIMEOUT 600)
+    list(APPEND _strata_hip_tests hip_prefill_fused_iq)
 
     # Numerical diagnostic: synthetic layouts by default, optional first-expert IQ3_XXS/IQ4_NL check with a pack.
     add_executable(hip_prefill_mmq_parity tests/hip/prefill_mmq_parity.cpp)
@@ -723,15 +1320,65 @@ 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})
+    add_executable(native_expert_bench src/kernels/native_expert_bench.cpp)
+    target_include_directories(native_expert_bench PRIVATE ${_strata_gpu_include_directories})
+    target_link_libraries(native_expert_bench 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.
+      # q4_K/q5_0 and q5_0/q8_0: a plain llama-quantize "_M" tier GGUF (not Unsloth's dynamic method) commonly
+      # puts Q5_0 on down-projections (sometimes gate/up too) where this model's native_experts.txt had no GPU
+      # kernel for it before; q5_0/q5_0 (both roles Q5_0) is left out of this list since stacking the same
+      # lower-precision format on both roles pushes the GPU path over this test's 3% threshold (quantization
+      # noise, not a kernel bug -- see the PR).
+      # q4_0/q4_0 and q4_0/q4_1: a plain llama-quantize Q4_0 file (bartowski's Qwen3.8-Flash-Next-Q4_0: gate/up Q4_0,
+      # down Q4_0 on 42 layers and Q4_1 on 6)
+      # q6_K/q8_0: Unsloth UD-Q6_K_XL gate/up Q6_K with Q8_0 down (docs/UNSLOTH_Q6.md). Layer 2 is q8_0/q8_0, already listed.
+      set(_strata_parity_pairs q4_K/q5_1 q4_K/q8_0 q5_K/q8_0 q8_0/q8_0 q4_K/q5_0 q5_0/q8_0 q4_0/q4_0 q4_0/q4_1)
+      if(STRATA_Q6K_EXPERTS)
+        list(APPEND _strata_parity_pairs q6_K/q8_0)
+      else()   # 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()
+      foreach(_pair ${_strata_parity_pairs})
+        string(REPLACE "/" "_" _name "${_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)
+    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()
 
-if(STRATA_ENABLE_CUDA AND STRATA_BUILD_TESTS)
+if(STRATA_ENABLE_CUDA AND STRATA_BUILD_TESTS AND EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/bench/micro/hit_cpu_order_parity.cu")
   add_executable(hit_cpu_order_parity bench/micro/hit_cpu_order_parity.cu)
   target_link_libraries(hit_cpu_order_parity PRIVATE strata_kernels strata_kernels_cpu ${_strata_gpu_runtime_target})
 endif()
 
 if(STRATA_BUILD_TESTS)
+  if(WIN32)
+    add_executable(pool_affinity_test src/kernels/cpu/pool_affinity_test.cpp)
+    target_link_libraries(pool_affinity_test PRIVATE strata_kernels_cpu)
+    add_test(NAME pool_affinity_test COMMAND pool_affinity_test)
+  elseif(CMAKE_SYSTEM_NAME STREQUAL "Linux")
+    add_executable(pool_affinity_test src/kernels/cpu/pool_affinity_linux_test.cpp)
+    target_link_libraries(pool_affinity_test PRIVATE strata_kernels_cpu)
+    add_test(NAME pool_affinity_test COMMAND pool_affinity_test)
+  endif()
   # Plan v0.3 P6: one expert applied to several tokens, bitwise vs the single-token kernel (synthetic, CPU only).
   # plan v0.3 P5: the device dequantizers vs the artifact's CPU dequantizers on the real shards (not a ctest: needs
   # the 38 GB model; run it by hand or from tools/gpu_session.py)
@@ -744,6 +1391,14 @@ if(STRATA_BUILD_TESTS)
   target_link_libraries(expert_multi_test PRIVATE strata_kernels_cpu)
   add_test(NAME expert_multi_test COMMAND expert_multi_test)
 
+  # PR #706: the opt-in AVX-2 Q2_0 bit-plane kernel (STRATA_Q2_BITPLANE=1) against the legacy one, bitwise alone/window
+  if(STRATA_BUILD_TESTS AND CMAKE_SYSTEM_PROCESSOR MATCHES "AMD64|x86_64|x64|X86")
+    add_executable(q2_bitplane_parity src/kernels/cpu/q2_bitplane_parity.cpp)
+    target_link_libraries(q2_bitplane_parity PRIVATE strata_kernels_cpu)
+    add_test(NAME q2_bitplane_parity COMMAND q2_bitplane_parity)
+    set_tests_properties(q2_bitplane_parity PROPERTIES ENVIRONMENT "STRATA_Q2_BITPLANE=1")
+  endif()
+
   add_executable(expert_parity src/kernels/cpu/expert_parity.cpp)
   target_link_libraries(expert_parity PRIVATE strata_kernels_cpu)
   # The pack is a REQUIRED input, not an optional one: this test compares against a real expert blob, and a
@@ -774,3 +1429,15 @@ if(STRATA_BUILD_TESTS)
     add_subdirectory(tests)
   endif()
 endif()
+
+# ---- HIP gfx906: every CUDA source of the tree (and the ggml-cuda MMQ sources) is compiled as HIP ----
+if(STRATA_HIP_GFX906)
+  file(GLOB_RECURSE _strata_cu CONFIGURE_DEPENDS ${CMAKE_CURRENT_SOURCE_DIR}/src/*.cu ${CMAKE_CURRENT_SOURCE_DIR}/bench/micro/*.cu
+       ${CMAKE_CURRENT_SOURCE_DIR}/tools/vision/*.cu)
+  if(_strata_mmq)
+    file(GLOB _strata_mmq_cu ${_strata_mmq}/*.cu ${_strata_mmq}/template-instances/*.cu)
+    list(APPEND _strata_cu ${_strata_mmq_cu})
+    target_compile_definitions(strata_mmq PRIVATE GGML_USE_HIP)
+  endif()
+  set_source_files_properties(${_strata_cu} PROPERTIES LANGUAGE HIP)
+endif()

Dockerfile

@@ -0,0 +1,115 @@
+# syntax=docker/dockerfile:1
+#
+# Strata: Qwen3.8-Flash-Next on NVIDIA GPUs (RTX 30/40/50, 12+ GB VRAM; two or
+# three cards can share one model, 8 GB each - docs/MULTI_GPU.md).
+#
+# The engine is compiled during docker build, so the first container start only
+# downloads the model (~70 GB) and starts the server. docker build has no GPU,
+# so the CUDA architectures are fixed here instead of read from nvidia-smi: the
+# engine is a fat binary with a cubin per listed arch, and the runtime picks the
+# one matching your card. Narrow CUDA_ARCHITECTURES to your card for a faster
+# build; a card outside the set needs a rebuild with its own arch.
+#
+# Build:
+#   docker build -t strata .
+#   docker build -t strata --build-arg CUDA_ARCHITECTURES=89 .        # RTX 40 only
+#
+# Run (host needs an NVIDIA driver >= 580 and nvidia-container-toolkit):
+#   docker run --rm --gpus all \
+#     -p 8080:8080 \
+#     --ulimit memlock=-1 \
+#     -v strata-data:/data \
+#     -e MODEL=IQ2_XS \
+#     strata
+#
+# Setup choices are env vars, read by docker-entrypoint.sh: FAMILY, MODEL, CONTEXT,
+# VISION (no | yes | cpu), KV (int8 | q4_0 | k8v4), GPU (one card) or GPUS ("0,2"
+# or "all", with LAYER_SPLIT), LOW_RAM (auto | on | off), HOST, PORT, API_KEY.
+#
+# Only the model files, the prepared pack, the MTP layer and the install config
+# live in the /data volume; the engine is part of the image. Strata loads 32-62 GB
+# into RAM, so a capped container needs -e LOW_RAM=on: setup.py reads the RAM from
+# /proc/meminfo, which here is the host's total, not the container's limit. Add an
+# API key before exposing the port to a network: -e API_KEY=<secret>. Pass
+# -e REINSTALL=1 to change the model settings later.
+#
+# --gpus all on a host with two usable cards: setup takes both (the layer split is
+# its recommended default). Pin one card with -e GPU=0, or name them with
+# -e GPUS=0,2. A volume set up for one card switches to the pair on its first start
+# on a two-card host unless GPU or GPUS pins it. LOW_RAM=on runs on one card.
+
+FROM nvidia/cuda:13.0.0-devel-ubuntu24.04
+
+# STRATA_EXECV=1: setup.py replaces itself with the server, so the server is PID 1
+# and docker stop's SIGTERM reaches it (see setup.start). Normal Linux starts, which
+# don't set it, keep spawning the server as a child.
+ENV DEBIAN_FRONTEND=noninteractive PYTHONUNBUFFERED=1 LANG=C.UTF-8 STRATA_EXECV=1
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        build-essential ca-certificates curl git libatomic1 libgomp1 \
+        python3 python3-pip python3-venv unzip \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /opt/strata
+COPY . .
+
+# RTX 20 (75), RTX 30 (86), RTX 40 (89), RTX 50 (120), plus 80 for A-series. CMakeLists
+# refuses anything below 75. BUILD_VISION=0 skips the image encoder build.
+ARG CUDA_ARCHITECTURES=75;80;86;89;120
+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 -r requirements.txt \
+    && chmod +x setup.sh docker-entrypoint.sh
+
+# llama.cpp at the pinned commit, then the engine and the image encoder, built
+# exactly the way setup.py builds them. BUILD.json is what setup.py reads to
+# decide whether an engine is current: source=local with a matching src hash
+# means the first start reuses it instead of recompiling.
+RUN .venv/bin/python - <<'PYEOF'
+import json, os, pathlib, shutil
+import setup
+
+llama = setup.get_llama_cpp()
+nvcc, _ = setup.find_nvcc()
+arch = os.environ.get("CUDA_ARCHITECTURES", "75;80;86;89;120").strip().strip('"').replace(",", ";")
+vision = "gpu" if os.environ.get("BUILD_VISION", "1") == "1" else "none"
+
+setup.cmake_build(setup.ROOT, setup.ROOT / "build", "strata",
+    ["-DSTRATA_ENABLE_CUDA=ON", "-DSTRATA_BUILD_TESTS=OFF",
+     f"-DCMAKE_CUDA_ARCHITECTURES={arch}", f"-DCMAKE_CUDA_COMPILER={nvcc}",
+     f"-DSTRATA_GGML_DIR={llama}"], None, "build-strata.bat")
+if vision != "none":
+    setup.cmake_build(setup.ROOT / "tools" / "vision", setup.ROOT / "build-vision", "strata-vision",
+        [f"-DLLAMA_DIR={llama}", "-DSTRATA_VISION_CUDA=ON",
+         f"-DCMAKE_CUDA_ARCHITECTURES={arch}", f"-DCMAKE_CUDA_COMPILER={nvcc}"], None, "build-vision.bat")
+
+eng = setup.ROOT / "engine"
+eng.mkdir(exist_ok=True)
+shutil.copy2(setup.ROOT / "build" / setup.EXE, eng / setup.EXE)
+if vision != "none":
+    shutil.copy2(setup.ROOT / "build-vision" / "bin" / setup.VEXE, eng / setup.VEXE)
+bindir = pathlib.Path(nvcc).parent
+meta = {"source": "local", "version": setup.source_version(),
+        "archs": [int(a.split("-")[0]) for a in arch.split(";") if a.split("-")[0].isdigit()], "vision": vision,
+        "cuda_dirs": [str(d) for d in (bindir, bindir / "x64", bindir.parent / "lib64") if d.is_dir()],
+        "src": setup.source_hash(setup.ENGINE_SOURCES),
+        "vision_src": setup.source_hash(setup.VISION_SOURCES) if vision != "none" else None}
+(eng / "BUILD.json").write_text(json.dumps(meta, indent=1))
+PYEOF
+
+# the cmake trees are build-time only; the engine itself is what the container needs
+RUN rm -rf build build-vision
+
+VOLUME ["/data"]
+EXPOSE 8080
+
+# /health is answered before the API key gate, so it works with or without one.
+# The port only opens after the model loads (1-3 minutes, longer on a first run),
+# so the start period is generous: a too short one marks a still-loading container
+# unhealthy and a restart policy would kill it mid-download.
+HEALTHCHECK --interval=30s --timeout=5s --start-period=600s --retries=3 \
+  CMD curl -fs "http://127.0.0.1:${PORT:-8080}/health" || exit 1
+
+ENTRYPOINT ["./docker-entrypoint.sh"]

README.de.md

@@ -0,0 +1,222 @@
+<h1 align="center">Strata</h1>
+
+[English](README.md) · [简体中文](README.zh-CN.md) · [日本語](README.ja.md) · **Deutsch** · [Français](README.fr.md) · [Español](README.es.md) · [Português](README.pt-BR.md)
+
+<p align="center"><b>Ein KI-Modell mit 125 Milliarden Parametern auf deinem eigenen Gaming-PC</b><br>
+NVIDIA- oder AMD-Grafikkarte (ab 12 GB) · Windows oder Linux · kostenlos und Open Source</p>
+
+<p align="center"><a href="https://github.com/Niko1221/Strata/releases/download/v0.1.10/Pagoda.mp4"><img src="docs/media/pagoda-preview.webp" width="720" alt="Ein Voxel-Pagodengarten, den das Modell in Strata geschrieben hat, läuft im Browser"></a><br>
+<sub>Ein Voxel-Pagodengarten aus einem einzigen Prompt, auf einer RTX 5070 mit Strata (IQ3_S, 128K Kontext) ·
+<a href="https://github.com/Niko1221/Strata/releases/download/v0.1.10/Pagoda.mp4">ganzes Video (49 s)</a></sub></p>
+
+Strata lässt **[Qwen3.8-Flash-Next](https://huggingface.co/Qwen/Qwen3.8-Flash-Next)** auf einem normalen PC laufen.
+Das ist ein großes, kluges KI-Modell, das sonst meist einen Server braucht. Es chattet, schreibt Code, versteht
+Bilder und arbeitet mit deinen Apps und Coding-Agents zusammen. Nichts verlässt deinen PC.
+
+## Wie schnell ist es?
+
+Wir haben es auf zwei ganz normalen Gaming-PCs gemessen. Ein Token ist etwa ¾ eines Wortes.
+
+- **Schreibt Antworten:** wie schnell die Antwort in einem kurzen Chat erscheint. 60 Tokens pro Sekunde sind
+  schneller, als du lesen kannst.
+- **Liest deinen Prompt:** wie schnell es aufnimmt, was du schickst (hier ein Dokument, Code oder Chatverlauf mit
+  32K Tokens).
+
+<table>
+<tr><th>NVIDIA: RTX 5070 (12 GB), Ryzen 5 7600, 64 GB RAM</th><th>AMD: RX 9070 XT (16 GB), Ryzen 9 3900X, 47 GB RAM</th></tr>
+<tr><td>
+
+| Größe | Schreibt Antworten | Liest deinen Prompt |
+| --- | ---: | ---: |
+| **Q2_0** | 94 Tokens/s | 2,650 Tokens/s |
+| **IQ2_XS** | 79 Tokens/s | 2,090 Tokens/s |
+| **IQ3_XXS** | 62 Tokens/s | 1,750 Tokens/s |
+| **IQ3_S** | 53 Tokens/s | 1,620 Tokens/s |
+| **Coder** | 55 Tokens/s | 2,180 Tokens/s |
+
+</td><td>
+
+| Größe | Schreibt Antworten | Liest deinen Prompt |
+| --- | ---: | ---: |
+| **Q2_0** | 60 Tokens/s | 1,160 Tokens/s |
+| **IQ2_XS** | 52 Tokens/s | 1,110 Tokens/s |
+| **Coder** | 44 Tokens/s | 1,420 Tokens/s |
+
+</td></tr>
+</table>
+
+NVIDIA: Q2_0 mit Engine 0.1.36, die anderen Zeilen mit 0.1.26 (4K Antworten, 32K Prompts). Die vollständigen
+Tabellen stehen in [DETAILS.md](docs/DETAILS.md#speed-measured). Eine Karte mit mehr VRAM ist schneller: Eine
+RTX 3090 (24 GB) sollte etwa 100-140 Tokens pro Sekunde schreiben. Lange Chats und andere Karten:
+[Tempo jedes Modells](docs/MODELS.md#how-fast-is-each-size), [Ergebnisse aus der Community](docs/COMMUNITY_BENCHMARKS.md).
+
+<p align="center"><a href="https://buymeacoffee.com/strataengine"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" height="50"></a><br>
+<sub>Strata ist kostenlos. Wenn es auf deinem PC gut läuft, hält ein Kaffee die Arbeit daran am Laufen.</sub></p>
+
+## Was du brauchst
+
+| | |
+| --- | --- |
+| **Grafikkarte** | **NVIDIA** GeForce RTX 20, 30, 40 oder 50 Serie, oder **AMD** Radeon RX 7900 XT / XTX, RX 7800 XT / 7700 XT, RX 9060 XT, RX 9070 / 9070 XT, Radeon AI PRO R9700 oder RX 6800 / 6900 Serie. Sie braucht **12 GB VRAM oder mehr**. |
+| **RAM** | 32 GB oder mehr. Dein RAM entscheidet, [welches Modell](#welches-modell-soll-ich-nehmen) passt. Mit 64 GB laufen alle Größen. |
+| **Festplatte** | Etwa 80 GB frei. Nimm wenn möglich eine SSD: Der erste Start geht dann viel schneller. |
+| **System** | Windows 10 / 11 oder Linux und ein aktueller Grafiktreiber von NVIDIA oder AMD. |
+
+Alles andere richtet der Installer ein. Zwei oder drei Karten können sich das Modell teilen ([Multi-GPU](docs/MULTI_GPU.md)).
+
+Experimentell, von Community-Mitgliedern auf ihren eigenen Rechnern geschrieben und getestet:
+
+- **Ältere Grafikkarten** (Tesla P40 / V100, GTX 10, Radeon VII / MI50, RX 6700 XT, RX 5500 XT): [Ältere GPUs](docs/OLDER_GPUS.md).
+- **Intel Arc**, unter Linux aus dem Quellcode gebaut: [Intel Arc](docs/INTEL_ARC.md).
+- **AMD Ryzen AI Max (Strix Halo)**, unter Linux aus dem Quellcode gebaut: [Strix Halo](docs/STRIX_HALO.md).
+- **Ältere Prozessoren ohne AVX2**: Sie funktionieren, aber langsam. [Ältere CPUs](docs/INSTALL.md#older-cpus-experimental).
+
+Die vollständige Liste: [docs/INSTALL.md](docs/INSTALL.md#what-you-need).
+
+## Installieren
+
+### Lass deine KI es einrichten
+
+Nutzt du einen KI-Coding-Assistenten (Claude Code, Cursor, Codex, GitHub Copilot, ...)? Füge dort diesen Text ein:
+
+```text
+Set up Strata on this PC for me: https://github.com/Niko1221/Strata - follow docs/AI_SETUP.md in that repository.
+```
+
+Er prüft deine Grafikkarte, deinen RAM und deine Festplatte und wählt das passende Modell. Dann installiert und
+startet er es und sagt dir, wie du deine Apps verbindest. KI-Tools können Strata auch über seinen
+[MCP-Server](docs/MCP_SERVER.md) installieren, starten und stoppen.
+
+### Oder mach es selbst
+
+[Lade Strata herunter](https://github.com/Niko1221/Strata/archive/refs/heads/main.zip) und entpacke es (oder nutze
+`git clone`). **Windows:** Doppelklick auf **`START-HERE.bat`**. **Linux:** Führe im Strata-Ordner **`./setup.sh`** aus.
+
+Die Schritte sind für NVIDIA und AMD gleich. Der Installer erkennt deine Karte und richtet die passende Engine
+dafür ein. Er stellt dir ein paar Fragen:
+
+- welches Modell und welche Größe,
+- wie viel Kontext (wie viel Text sich das Modell merkt),
+- ob es Bilder lesen soll.
+
+Drück jedes Mal Enter, um die empfohlene Antwort zu nehmen. Dann lädt er das Modell herunter (etwa 70 GB) und
+startet es. Bricht der Download ab, starte ihn einfach neu: Er macht dort weiter, wo er aufgehört hat. Dein Browser
+öffnet die Strata-App unter `http://127.0.0.1:8080`.
+
+> **Während das Modell startet, kann dein PC 1-3 Minuten langsam sein oder nicht reagieren** (beim ersten Mal am
+> längsten). Strata lädt 35-55 GB in deinen RAM und reserviert einen Teil davon für die Grafikkarte. Das ist normal.
+> Warte und schließ das Fenster nicht. Im Fenster siehst du, was Strata gerade tut.
+
+**Beim nächsten Mal** startest du wieder `START-HERE.bat` (oder `./setup.sh`). Es startet sofort und lädt nichts
+doppelt herunter. Schließ das Fenster, um das Modell zu stoppen. `UPDATE.bat` (`./update.sh`) aktualisiert Strata,
+ohne es zu starten. Updates, Docker, mehrere Karten, wo die Dateien landen und alle Optionen:
+[docs/INSTALL.md](docs/INSTALL.md).
+
+## Welches Modell soll ich nehmen?
+
+Der Installer empfiehlt eins passend zu deinem RAM. Dasselbe Modell gibt es in mehreren Größen, mehr oder weniger
+stark komprimiert. Kleinere Größen sind schneller. Größere sind etwas klüger.
+
+| Dein RAM | Nimm | Warum |
+| --- | --- | --- |
+| **32 GB** | **Coder** | passt in 32 GB und ist für Code gemacht (mit einer 24-GB-Karte laufen auch Q2_0 und IQ2_XS) |
+| **48 GB** | **IQ2_XS** (oder Q2_0, das schnellste) | die größeren Größen passen nicht |
+| **64 GB** | **IQ2_XS** (empfohlen) oder IQ3_XXS / IQ3_S | alle Größen passen; IQ3_S ist das beste und das langsamste |
+| **96 GB oder mehr** | **IQ3_S** oder Unsloths UD-IQ4_XS (~4 Bit) | Platz für die größten Größen, auch wenn alles andere offen ist |
+
+- **[Coder](docs/MODELS.md#coder):** eine Version für Code, bei der die Hälfte der Experten entfernt wurde. Sie
+  erreicht 91 % des SWE-bench-Verified-Werts des vollen Modells (von ihren Autoren gemessen) und passt in 32 GB RAM.
+  Außerhalb von Code ist sie schwächer, auch bei chinesischem und anderem CJK-Text (#438). Nimm dafür Q2_0, IQ2_XS
+  oder IQ3_S, die alle Experten behalten.
+- **[Swift 1.5](docs/MODELS.md#swift-15):** ein Fine-Tune, das viel kürzer nachdenkt, bevor es antwortet. Du
+  bekommst die Antwort früher, bei etwa gleicher Qualität.
+- **[Unsloth UD-IQ4_XS](docs/MODELS.md#unsloth-ud-iq4_xs):** die ~4-Bit-Version von Unsloth, in der Qualität
+  zwischen IQ3_S und UD-Q4_K_XL. 94 GB Download. Mit weniger als ~80 GB RAM liest Strata einen Teil davon während
+  der Antwort von der SSD, dort ist es also langsamer (eine NVMe-SSD hilft).
+- **[Unsloth UD-Q4_K_XL](docs/MODELS.md#unsloth-ud-q4_k_xl-experimental)** (experimentell): am nächsten am vollen
+  Modell. Aber Strata liest das meiste davon während der Antwort von der SSD, deshalb schreibt es auf einem PC mit
+  64 GB nur 7-8.5 Tokens/s.
+- **[OrcaRouter's Uncensored IQ3_XXS](docs/MODELS.md#orcarouter-uncensored-iq3_xxs):** Das richtest du von Hand
+  ein. Es steht nicht im Menü des Installers.
+
+Größen, Downloads und was wohin passt: [docs/MODELS.md](docs/MODELS.md). Um später ein weiteres Modell
+hinzuzufügen, starte `SETUP.bat` (Linux: `./setup.sh --setup`).
+
+## So benutzt du es
+
+<p align="center"><img src="docs/media/runpagoda.png" width="900" alt="Der Monitor-Tab der Strata-App neben einem Coding-Agent"><br>
+<sub>Der <b>Monitor</b> der Strata-App (links), während ein Coding-Agent den Pagodengarten aus dem Video schreibt (rechts)</sub></p>
+
+- **Im Browser:** Öffne `http://127.0.0.1:8080`. Dort gibt es **Chat**, einen Live-**Monitor** für das Modell und
+  deine GPU/CPU/RAM und **About** mit den Einstellungen und Adressen.
+- **Deine Apps und Coding-Agents:** Füge einen „OpenAI-kompatiblen“ Anbieter mit der Basis-URL
+  **`http://127.0.0.1:8080/v1`** hinzu. Jeder API-Key und jeder Modellname funktioniert.
+  - Apps, die die API von Anthropic nutzen: `http://127.0.0.1:8080/v1/messages` (Claude Code:
+    `ANTHROPIC_BASE_URL=http://127.0.0.1:8080`).
+  - Codex CLI und andere Apps, die die OpenAI Responses API nutzen: `/v1/responses`
+    ([Einrichtung](docs/DETAILS.md#the-responses-api-and-codex-cli)).
+- **Nachdenken:** Wähle **aus, niedrig, mittel oder hoch** im Chat-Menü oder unter „Reasoning Effort“ in deiner
+  App. Aus ist am schnellsten. Hoch ist am besten für schwierige Fragen.
+- **Bilder:** Antworte im Setup mit Ja auf „Images?“. Klick dann im Chat auf **Picture** oder hänge Bilder in deiner
+  App an. AMD-Karten lesen Bilder unter Linux über den Prozessor; unter Windows geht das noch nicht.
+- **Vom Handy oder einem anderen PC:** `START-HERE.bat --setup --host 0.0.0.0 --api-key <secret>`. Setz immer einen
+  Key.
+- **Eine Anfrage nach der anderen:** Standardmäßig beantwortet Strata eine Anfrage, die anderen warten. Um mehrere
+  gleichzeitig zu beantworten, setz `"parallel": 2` ([BATCHING.md](docs/BATCHING.md)). Auf einer 12-GB-Karte wird
+  dadurch jede Antwort langsamer.
+- **Lange Prompts:** Strata liest die erste Nachricht eines Chats komplett, etwa 1 Minute pro 30,000 Tokens.
+  Folgenachrichten starten in Sekunden.
+
+Mehr: [wo deine Chats gespeichert werden](docs/INSTALL.md#where-things-are-stored), [die API](docs/DETAILS.md#using-it).
+
+## Etwas ist schiefgelaufen?
+
+- **Mein PC ist beim ersten Start von Strata eingefroren.** Das ist normal, während das Modell lädt. Warte und
+  schließ das Fenster nicht. Nach 10 Minuten immer noch eingefroren? Starte den PC neu, schließ andere Programme und
+  versuch es noch einmal, oder nimm eine kleinere Größe.
+- **Es hat beim Herunterladen oder Installieren aufgehört.** Starte `START-HERE.bat` (oder `./setup.sh`) noch
+  einmal. Es macht dort weiter, wo es aufgehört hat.
+- **Es ist sehr langsam und die Festplattenlampe blinkt ständig, oder es meldet „the engine stopped
+  unexpectedly“.** Dein PC hat nicht genug freien RAM. Schließ andere Programme (Browser brauchen viel) oder nimm
+  eine kleinere Größe (Q2_0 oder IQ2_XS).
+- **Es meldet, dass Port 8080 schon belegt ist.** Strata läuft bereits. Such nach seinem Fenster.
+
+Weitere Probleme und ihre Lösungen: [docs/TROUBLESHOOTING.md](docs/TROUBLESHOOTING.md). Kommst du nicht weiter?
+Öffne ein [Issue](https://github.com/Niko1221/Strata/issues) und hänge `strata-<model>.log` aus dem Strata-Ordner
+an. Du hast ein Sicherheitsproblem gefunden? Melde es vertraulich: [SECURITY.md](SECURITY.md).
+
+## Wie funktioniert es?
+
+Modelle wie dieses laufen normalerweise auf Servern mit Hunderten Gigabyte Grafikspeicher. Deine Grafikkarte hat
+12-24 GB. Strata bringt das Modell trotzdem unter, indem es **die Arbeit auf deinen ganzen PC verteilt**. Stell dir
+eine Küche vor: Was du ständig brauchst, bleibt auf der Arbeitsfläche, der Rest wartet in der Vorratskammer.
+
+<p align="center"><img src="docs/media/how-it-works.svg" width="860" alt="Die 24,576 Experten des Modells: die meistgenutzten auf der Grafikkarte, alle im RAM, eine Nachschlagetabelle auf der SSD"></p>
+
+- **Das Modell ist ein Team aus 24,576 kleinen Spezialisten („Experten“).** Jedes Wort braucht nur 10 davon.
+- **Deine Grafikkarte** hält die paar tausend Experten, die am häufigsten gebraucht werden. **Dein RAM** hält alle,
+  und **dein Prozessor** rechnet gleichzeitig am Rest. **Deine SSD** hält eine große Nachschlagetabelle.
+
+<p align="center"><img src="docs/media/guess-and-check.svg" width="860" alt="Ein kleiner Helfer rät die nächsten Wörter; das große Modell prüft alle auf einmal und behält die richtigen"></p>
+
+- **Raten, dann prüfen:** Ein kleiner Helfer rät die nächsten paar Wörter. Das große Modell prüft sie alle auf
+  einmal. Du bekommst dieselbe Antwort, nur 1.6-1.8x schneller.
+- **Lange Texte werden in großen Stücken gelesen** (bis zu 8,192 Tokens auf einmal), mit über 1,000 Tokens pro
+  Sekunde.
+
+Die längere Erklärung: [docs/HOW_IT_WORKS.md](docs/HOW_IT_WORKS.md). Jeder Teil und seine Zahlen:
+[die Details](docs/DETAILS.md#how-it-works) und das [Paper](docs/paper/Strata-Paper.pdf).
+
+## Danksagung und Lizenz
+
+Das Modell ist [Qwen3.8-Flash-Next](https://huggingface.co/Qwen/Qwen3.8-Flash-Next) vom Qwen-Team. Komprimiert
+wurde es von [ISTA-DASLab](https://huggingface.co/ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF), UkisAI (Swift 1.5)
+und Unsloth. Strata nutzt Teile von [llama.cpp / ggml](https://github.com/ggml-org/llama.cpp). Alle Danksagungen:
+[docs/HOW_IT_WORKS.md](docs/HOW_IT_WORKS.md#credits). Strata ist Open Source unter der [MIT-Lizenz](LICENSE). Ein
+paar Teile und alle Modelle haben eigene Lizenzen ([welche](docs/HOW_IT_WORKS.md#license)).
+
+## Strata unterstützen
+
+Strata ist kostenlos und Open Source. Wenn es dir nützt, kannst du die Entwicklung unterstützen:
+
+<p align="center"><a href="https://buymeacoffee.com/strataengine"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" height="50"></a></p>

README.es.md

@@ -0,0 +1,216 @@
+<h1 align="center">Strata</h1>
+
+[English](README.md) · [简体中文](README.zh-CN.md) · [日本語](README.ja.md) · [Deutsch](README.de.md) · [Français](README.fr.md) · **Español** · [Português](README.pt-BR.md)
+
+<p align="center"><b>Ejecuta un modelo de IA de 125 mil millones de parámetros en tu propio PC gaming</b><br>
+Tarjeta gráfica NVIDIA o AMD (12 GB o más) · Windows o Linux · libre y de código abierto</p>
+
+<p align="center"><a href="https://github.com/Niko1221/Strata/releases/download/v0.1.10/Pagoda.mp4"><img src="docs/media/pagoda-preview.webp" width="720" alt="Un jardín con una pagoda de vóxeles que escribió el modelo de Strata, funcionando en el navegador"></a><br>
+<sub>Un jardín con una pagoda de vóxeles, hecho con un solo prompt en una RTX 5070 con Strata (IQ3_S, contexto de 128K) ·
+<a href="https://github.com/Niko1221/Strata/releases/download/v0.1.10/Pagoda.mp4">vídeo completo (49 s)</a></sub></p>
+
+Strata ejecuta **[Qwen3.8-Flash-Next](https://huggingface.co/Qwen/Qwen3.8-Flash-Next)** en un PC normal. Es un
+modelo de IA grande e inteligente que normalmente necesita un servidor. Conversa, escribe código, entiende imágenes
+y funciona con tus aplicaciones y agentes de programación. Nada sale de tu PC.
+
+## ¿Qué velocidad tiene?
+
+Lo medimos en dos PC gaming normales. Un token equivale más o menos a ¾ de una palabra.
+
+- **Escribe respuestas:** lo rápido que aparece la respuesta en un chat corto. 60 tokens por segundo es más rápido de lo que puedes leer.
+- **Lee tu prompt:** lo rápido que procesa lo que le envías (aquí, un documento, código o historial de chat de 32K tokens).
+
+<table>
+<tr><th>NVIDIA: RTX 5070 (12 GB), Ryzen 5 7600, 64 GB RAM</th><th>AMD: RX 9070 XT (16 GB), Ryzen 9 3900X, 47 GB RAM</th></tr>
+<tr><td>
+
+| Tamaño | Escribe respuestas | Lee tu prompt |
+| --- | ---: | ---: |
+| **Q2_0** | 94 tokens/s | 2,650 tokens/s |
+| **IQ2_XS** | 79 tokens/s | 2,090 tokens/s |
+| **IQ3_XXS** | 62 tokens/s | 1,750 tokens/s |
+| **IQ3_S** | 53 tokens/s | 1,620 tokens/s |
+| **Coder** | 55 tokens/s | 2,180 tokens/s |
+
+</td><td>
+
+| Tamaño | Escribe respuestas | Lee tu prompt |
+| --- | ---: | ---: |
+| **Q2_0** | 60 tokens/s | 1,160 tokens/s |
+| **IQ2_XS** | 52 tokens/s | 1,110 tokens/s |
+| **Coder** | 44 tokens/s | 1,420 tokens/s |
+
+</td></tr>
+</table>
+
+NVIDIA: Q2_0 con el motor 0.1.36, las demás filas con 0.1.26 (respuestas de 4K, prompts de 32K). Las tablas
+completas están en [DETAILS.md](docs/DETAILS.md#speed-measured). Una tarjeta con más VRAM es más rápida: una
+RTX 3090 (24 GB) debería escribir unos 100-140 tokens por segundo. Chats largos y otras tarjetas:
+[velocidad de cada modelo](docs/MODELS.md#how-fast-is-each-size), [resultados de la comunidad](docs/COMMUNITY_BENCHMARKS.md).
+
+<p align="center"><a href="https://buymeacoffee.com/strataengine"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" height="50"></a><br>
+<sub>Strata es gratis. Si funciona bien en tu PC, un café ayuda a seguir trabajando en él.</sub></p>
+
+## Qué necesitas
+
+| | |
+| --- | --- |
+| **Tarjeta gráfica** | **NVIDIA** GeForce serie RTX 20, 30, 40 o 50, o **AMD** Radeon RX 7900 XT / XTX, RX 7800 XT / 7700 XT, RX 9060 XT, RX 9070 / 9070 XT, Radeon AI PRO R9700 o serie RX 6800 / 6900. Necesita **12 GB de VRAM o más**. |
+| **RAM** | 32 GB o más. Tu RAM decide [qué modelo](#qué-modelo-elijo) cabe. Con 64 GB funcionan todos los tamaños. |
+| **Disco** | Unos 80 GB libres. Usa un SSD si puedes: el primer arranque es mucho más rápido. |
+| **Sistema** | Windows 10 / 11 o Linux, y un controlador gráfico actual de NVIDIA o AMD. |
+
+El instalador prepara todo lo demás. Dos o tres tarjetas pueden repartirse el modelo ([multi-GPU](docs/MULTI_GPU.md)).
+
+Experimental, escrito y probado por miembros de la comunidad en sus propios equipos:
+
+- **Tarjetas gráficas antiguas** (Tesla P40 / V100, GTX 10, Radeon VII / MI50, RX 6700 XT, RX 5500 XT): [Older GPUs](docs/OLDER_GPUS.md).
+- **Intel Arc**, compilado desde el código fuente en Linux: [Intel Arc](docs/INTEL_ARC.md).
+- **AMD Ryzen AI Max (Strix Halo)**, compilado desde el código fuente en Linux: [Strix Halo](docs/STRIX_HALO.md).
+- **Procesadores antiguos sin AVX2**: funcionan, pero despacio. [Older CPUs](docs/INSTALL.md#older-cpus-experimental).
+
+La lista completa: [docs/INSTALL.md](docs/INSTALL.md#what-you-need).
+
+## Instalación
+
+### Deja que tu IA lo instale
+
+¿Usas un asistente de programación con IA (Claude Code, Cursor, Codex, GitHub Copilot, ...)? Pega esto en él:
+
+```text
+Set up Strata on this PC for me: https://github.com/Niko1221/Strata - follow docs/AI_SETUP.md in that repository.
+```
+
+Revisa tu tarjeta gráfica, tu RAM y tu disco, y elige el modelo que cabe. Luego lo instala, lo arranca y te explica
+cómo conectar tus aplicaciones. Las herramientas de IA también pueden instalar, arrancar y detener Strata con su
+[servidor MCP](docs/MCP_SERVER.md).
+
+### O hazlo tú mismo
+
+[Descarga Strata](https://github.com/Niko1221/Strata/archive/refs/heads/main.zip) y descomprímelo (o usa `git clone`).
+**Windows:** haz doble clic en **`START-HERE.bat`**. **Linux:** ejecuta **`./setup.sh`** en la carpeta de Strata.
+
+Los pasos son los mismos para NVIDIA y AMD. El instalador detecta tu tarjeta y prepara el motor adecuado para ella.
+Te hace unas pocas preguntas:
+
+- qué modelo y qué tamaño,
+- cuánto contexto (cuánto texto tiene presente el modelo),
+- si debe entender imágenes.
+
+Pulsa Enter cada vez para usar la respuesta recomendada. Después descarga el modelo (unos 70 GB) y lo arranca. Si la
+descarga se detiene, vuelve a ejecutarlo: sigue donde lo dejó. Tu navegador abre la aplicación de Strata en
+`http://127.0.0.1:8080`.
+
+> **Mientras el modelo arranca, tu PC puede ir lento o dejar de responder durante 1-3 minutos** (más la primera vez).
+> Strata carga 35-55 GB en tu RAM y reserva una parte para la tarjeta gráfica. Es normal. Espera y no cierres la
+> ventana. La ventana muestra lo que está haciendo Strata.
+
+**La próxima vez**, vuelve a ejecutar `START-HERE.bat` (o `./setup.sh`). Arranca enseguida y no descarga nada dos
+veces. Cierra su ventana para detener el modelo. `UPDATE.bat` (`./update.sh`) actualiza Strata sin arrancarlo.
+Actualizaciones, Docker, varias tarjetas, dónde se guardan los archivos y todas las opciones: [docs/INSTALL.md](docs/INSTALL.md).
+
+## ¿Qué modelo elijo?
+
+El instalador te recomienda uno según tu RAM. El mismo modelo viene en varios tamaños, más o menos comprimidos. Los
+tamaños pequeños son más rápidos. Los grandes son algo más inteligentes.
+
+| Tu RAM | Elige | Por qué |
+| --- | --- | --- |
+| **32 GB** | **Coder** | cabe en 32 GB y está hecho para programar (con una tarjeta de 24 GB también funcionan Q2_0 e IQ2_XS) |
+| **48 GB** | **IQ2_XS** (o Q2_0, el más rápido) | los tamaños más grandes no caben |
+| **64 GB** | **IQ2_XS** (recomendado), o IQ3_XXS / IQ3_S | caben todos los tamaños; IQ3_S es el mejor y el más lento |
+| **96 GB o más** | **IQ3_S**, o el UD-IQ4_XS de Unsloth (~4 bits) | espacio para los tamaños más grandes con todo lo demás abierto |
+
+- **[Coder](docs/MODELS.md#coder):** una versión para programar a la que se le quitó la mitad de los expertos. Alcanza
+  el 91% de la puntuación SWE-bench Verified del modelo completo (medido por sus autores) y cabe en 32 GB de RAM. Es
+  más flojo fuera del código, también con chino y otros textos CJK (#438). Para eso, elige Q2_0, IQ2_XS o IQ3_S, que
+  conservan todos los expertos.
+- **[Swift 1.5](docs/MODELS.md#swift-15):** un ajuste fino que piensa mucho menos tiempo antes de responder. Recibes
+  la respuesta antes, con más o menos la misma calidad.
+- **[Unsloth UD-IQ4_XS](docs/MODELS.md#unsloth-ud-iq4_xs):** la versión de ~4 bits de Unsloth, entre IQ3_S y
+  UD-Q4_K_XL en calidad. Una descarga de 94 GB. Con menos de ~80 GB de RAM, Strata lee una parte desde el SSD
+  mientras responde, así que ahí es más lento (un SSD NVMe ayuda).
+- **[Unsloth UD-Q4_K_XL](docs/MODELS.md#unsloth-ud-q4_k_xl-experimental)** (experimental): el más parecido al modelo
+  completo. Pero Strata lee la mayor parte desde el SSD mientras responde, así que solo escribe 7-8.5 tokens/s en un
+  PC con 64 GB.
+- **[OrcaRouter's Uncensored IQ3_XXS](docs/MODELS.md#orcarouter-uncensored-iq3_xxs):** se instala a mano. No está en
+  el menú del instalador.
+
+Tamaños, descargas y qué cabe dónde: [docs/MODELS.md](docs/MODELS.md). Para añadir otro modelo más adelante, ejecuta
+`SETUP.bat` (Linux: `./setup.sh --setup`).
+
+## Cómo usarlo
+
+<p align="center"><img src="docs/media/runpagoda.png" width="900" alt="La pestaña Monitor de la aplicación de Strata junto a un agente de programación"><br>
+<sub>El <b>Monitor</b> de la aplicación de Strata (izquierda) mientras un agente de programación escribe el jardín de la pagoda del vídeo (derecha)</sub></p>
+
+- **En el navegador:** abre `http://127.0.0.1:8080`. Tiene **Chat**, un **Monitor** en vivo del modelo y de tu
+  GPU/CPU/RAM, y **About** con los ajustes y las direcciones.
+- **Tus aplicaciones y agentes de programación:** añade un proveedor "compatible con OpenAI" con la URL base
+  **`http://127.0.0.1:8080/v1`**. Sirve cualquier clave de API y cualquier nombre de modelo.
+  - Aplicaciones que usan la API de Anthropic: `http://127.0.0.1:8080/v1/messages` (Claude Code:
+    `ANTHROPIC_BASE_URL=http://127.0.0.1:8080`).
+  - Codex CLI y otras aplicaciones que usan la API Responses de OpenAI: `/v1/responses`
+    ([configuración](docs/DETAILS.md#the-responses-api-and-codex-cli)).
+- **Razonamiento:** elige **off, low, medium o high** en el menú del chat o en el "reasoning effort" de tu aplicación.
+  Off es lo más rápido. High es lo mejor para preguntas difíciles.
+- **Imágenes:** responde que sí a "Images?" en la instalación. Luego haz clic en **Picture** en el chat, o adjunta
+  imágenes en tu aplicación. Las tarjetas AMD leen imágenes en Linux a través del procesador; en Windows todavía no.
+- **Desde tu móvil u otro PC:** `START-HERE.bat --setup --host 0.0.0.0 --api-key <secret>`. Pon siempre una clave.
+- **Una petición cada vez:** por defecto Strata responde una petición y las demás esperan. Para responder varias a la
+  vez, pon `"parallel": 2` ([BATCHING.md](docs/BATCHING.md)). En una tarjeta de 12 GB, cada respuesta va más lenta.
+- **Prompts largos:** Strata lee entero el primer mensaje de un chat, más o menos 1 minuto por cada 30,000 tokens. Los
+  mensajes siguientes empiezan en segundos.
+
+Más: [dónde se guardan tus chats](docs/INSTALL.md#where-things-are-stored), [la API](docs/DETAILS.md#using-it).
+
+## ¿Algo salió mal?
+
+- **Mi PC se congeló la primera vez que arrancó Strata.** Es normal mientras carga el modelo. Espera y no cierres la
+  ventana. ¿Sigue congelado después de 10 minutos? Reinicia el PC, cierra otros programas y vuelve a intentarlo, o
+  elige un tamaño más pequeño.
+- **Se detuvo durante la descarga o la instalación.** Vuelve a ejecutar `START-HERE.bat` (o `./setup.sh`). Sigue
+  donde se detuvo.
+- **Va muy lento y la luz del disco no deja de parpadear, o dice "the engine stopped unexpectedly".** Tu PC no tiene
+  suficiente RAM libre. Cierra otros programas (los navegadores usan mucha), o elige un tamaño más pequeño (Q2_0 o
+  IQ2_XS).
+- **Dice que el puerto 8080 ya está en uso.** Strata ya está en marcha. Busca su ventana.
+
+Más problemas y sus soluciones: [docs/TROUBLESHOOTING.md](docs/TROUBLESHOOTING.md). ¿Sigues atascado? Abre un
+[issue](https://github.com/Niko1221/Strata/issues) y adjunta `strata-<model>.log` de la carpeta de Strata. ¿Has
+encontrado un problema de seguridad? Infórmalo en privado: [SECURITY.md](SECURITY.md).
+
+## ¿Cómo funciona?
+
+Los modelos como este suelen funcionar en servidores con cientos de gigabytes de memoria gráfica. Tu tarjeta gráfica
+tiene 12-24 GB. Strata hace que el modelo quepa **repartiendo el trabajo por todo tu PC**. Piensa en una cocina: lo
+que usas todo el rato se queda en la encimera, y el resto espera en la despensa.
+
+<p align="center"><img src="docs/media/how-it-works.svg" width="860" alt="Los 24,576 expertos del modelo: los más usados en la tarjeta gráfica, todos en la RAM y una tabla de consulta en el SSD"></p>
+
+- **El modelo es un equipo de 24,576 pequeños especialistas ("expertos").** Cada palabra solo necesita 10 de ellos.
+- **Tu tarjeta gráfica** guarda los pocos miles de expertos que más se usan. **Tu RAM** los tiene todos, y **tu
+  procesador** trabaja con el resto al mismo tiempo. **Tu SSD** guarda una gran tabla de consulta.
+
+<p align="center"><img src="docs/media/guess-and-check.svg" width="860" alt="Un pequeño ayudante adivina las siguientes palabras; el modelo grande las comprueba todas a la vez y se queda con las correctas"></p>
+
+- **Adivinar y comprobar:** un pequeño ayudante adivina las siguientes palabras. El modelo grande las comprueba todas
+  a la vez. Recibes la misma respuesta, 1.6-1.8x antes.
+- **Los textos largos se leen en trozos grandes** (hasta 8,192 tokens cada vez), a más de 1,000 tokens por segundo.
+
+La explicación larga: [docs/HOW_IT_WORKS.md](docs/HOW_IT_WORKS.md). Cada parte y sus números:
+[los detalles](docs/DETAILS.md#how-it-works) y el [artículo](docs/paper/Strata-Paper.pdf).
+
+## Créditos y licencia
+
+El modelo es [Qwen3.8-Flash-Next](https://huggingface.co/Qwen/Qwen3.8-Flash-Next), del equipo de Qwen. Lo
+comprimieron [ISTA-DASLab](https://huggingface.co/ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF), UkisAI (Swift 1.5)
+y Unsloth. Strata usa partes de [llama.cpp / ggml](https://github.com/ggml-org/llama.cpp). Todos los créditos:
+[docs/HOW_IT_WORKS.md](docs/HOW_IT_WORKS.md#credits). Strata es de código abierto con la [licencia MIT](LICENSE).
+Algunas partes y cada modelo tienen sus propias licencias ([cuáles](docs/HOW_IT_WORKS.md#license)).
+
+## Apoya a Strata
+
+Strata es libre y de código abierto. Si te resulta útil, puedes apoyar su desarrollo:
+
+<p align="center"><a href="https://buymeacoffee.com/strataengine"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" height="50"></a></p>

README.fr.md

@@ -0,0 +1,221 @@
+<h1 align="center">Strata</h1>
+
+[English](README.md) · [简体中文](README.zh-CN.md) · [日本語](README.ja.md) · [Deutsch](README.de.md) · **Français** · [Español](README.es.md) · [Português](README.pt-BR.md)
+
+<p align="center"><b>Faites tourner un modèle d'IA de 125 milliards de paramètres sur votre propre PC de jeu</b><br>
+Carte graphique NVIDIA ou AMD (12 Go ou plus) · Windows ou Linux · gratuit et open source</p>
+
+<p align="center"><a href="https://github.com/Niko1221/Strata/releases/download/v0.1.10/Pagoda.mp4"><img src="docs/media/pagoda-preview.webp" width="720" alt="Un jardin de pagode en voxels écrit par le modèle de Strata, qui tourne dans le navigateur"></a><br>
+<sub>Un jardin de pagode en voxels, généré en une seule requête sur une RTX 5070 avec Strata (IQ3_S, contexte de 128K) ·
+<a href="https://github.com/Niko1221/Strata/releases/download/v0.1.10/Pagoda.mp4">vidéo complète (49 s)</a></sub></p>
+
+Strata fait tourner **[Qwen3.8-Flash-Next](https://huggingface.co/Qwen/Qwen3.8-Flash-Next)** sur un PC normal. C'est
+un grand modèle d'IA, très capable, qui demande d'habitude un serveur. Il discute, écrit du code, lit des images et
+fonctionne avec vos applications et vos agents de code. Rien ne quitte votre PC.
+
+## Quelle est sa vitesse ?
+
+Nous l'avons mesurée sur deux PC de jeu ordinaires. Un token correspond à environ ¾ d'un mot.
+
+- **Écrit les réponses :** la vitesse à laquelle la réponse s'affiche dans une courte discussion. 60 tokens par seconde, c'est plus rapide que votre lecture.
+- **Lit votre prompt :** la vitesse à laquelle il absorbe ce que vous envoyez (ici un document, du code ou un historique de discussion de 32K tokens).
+
+<table>
+<tr><th>NVIDIA : RTX 5070 (12 Go), Ryzen 5 7600, 64 Go de RAM</th><th>AMD : RX 9070 XT (16 Go), Ryzen 9 3900X, 47 Go de RAM</th></tr>
+<tr><td>
+
+| Taille | Écrit les réponses | Lit votre prompt |
+| --- | ---: | ---: |
+| **Q2_0** | 94 tokens/s | 2,650 tokens/s |
+| **IQ2_XS** | 79 tokens/s | 2,090 tokens/s |
+| **IQ3_XXS** | 62 tokens/s | 1,750 tokens/s |
+| **IQ3_S** | 53 tokens/s | 1,620 tokens/s |
+| **Coder** | 55 tokens/s | 2,180 tokens/s |
+
+</td><td>
+
+| Taille | Écrit les réponses | Lit votre prompt |
+| --- | ---: | ---: |
+| **Q2_0** | 60 tokens/s | 1,160 tokens/s |
+| **IQ2_XS** | 52 tokens/s | 1,110 tokens/s |
+| **Coder** | 44 tokens/s | 1,420 tokens/s |
+
+</td></tr>
+</table>
+
+NVIDIA : Q2_0 avec le moteur 0.1.36, les autres lignes avec 0.1.26 (réponses de 4K, prompts de 32K). Les tableaux
+complets sont dans [DETAILS.md](docs/DETAILS.md#speed-measured). Une carte avec plus de VRAM va plus vite : une
+RTX 3090 (24 Go) devrait écrire environ 100-140 tokens par seconde. Longues discussions et autres cartes :
+[vitesse de chaque modèle](docs/MODELS.md#how-fast-is-each-size), [résultats de la communauté](docs/COMMUNITY_BENCHMARKS.md).
+
+<p align="center"><a href="https://buymeacoffee.com/strataengine"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" height="50"></a><br>
+<sub>Strata est gratuit. S'il tourne bien sur votre PC, un café aide à poursuivre le travail.</sub></p>
+
+## Ce qu'il vous faut
+
+| | |
+| --- | --- |
+| **Carte graphique** | **NVIDIA** GeForce RTX série 20, 30, 40 ou 50, ou **AMD** Radeon RX 7900 XT / XTX, RX 7800 XT / 7700 XT, RX 9060 XT, RX 9070 / 9070 XT, Radeon AI PRO R9700 ou série RX 6800 / 6900. Il faut **12 Go de VRAM ou plus**. |
+| **RAM** | 32 Go ou plus. Votre RAM décide [quel modèle](#quel-modèle-choisir-) tient. Avec 64 Go, toutes les tailles tournent. |
+| **Disque** | Environ 80 Go libres. Prenez un SSD si possible : le premier démarrage est bien plus rapide. |
+| **Système** | Windows 10 / 11 ou Linux, avec un pilote graphique NVIDIA ou AMD à jour. |
+
+L'installateur s'occupe de tout le reste. Deux ou trois cartes peuvent se partager le modèle ([multi-GPU](docs/MULTI_GPU.md)).
+
+Expérimental, écrit et testé par des membres de la communauté sur leurs propres machines :
+
+- **Cartes graphiques plus anciennes** (Tesla P40 / V100, GTX 10, Radeon VII / MI50, RX 6700 XT, RX 5500 XT) : [Older GPUs](docs/OLDER_GPUS.md).
+- **Intel Arc**, compilé depuis les sources sous Linux : [Intel Arc](docs/INTEL_ARC.md).
+- **AMD Ryzen AI Max (Strix Halo)**, compilé depuis les sources sous Linux : [Strix Halo](docs/STRIX_HALO.md).
+- **Processeurs plus anciens sans AVX2** : ça marche, mais lentement. [Older CPUs](docs/INSTALL.md#older-cpus-experimental).
+
+La liste complète : [docs/INSTALL.md](docs/INSTALL.md#what-you-need).
+
+## Installation
+
+### Laissez votre IA l'installer
+
+Vous utilisez un assistant de code IA (Claude Code, Cursor, Codex, GitHub Copilot, ...) ? Collez-lui ceci :
+
+```text
+Set up Strata on this PC for me: https://github.com/Niko1221/Strata - follow docs/AI_SETUP.md in that repository.
+```
+
+Il vérifie votre carte graphique, votre RAM et votre disque, et choisit le modèle qui convient. Ensuite il l'installe,
+le démarre et vous explique comment connecter vos applications. Les outils d'IA peuvent aussi installer, démarrer et
+arrêter Strata grâce à son [serveur MCP](docs/MCP_SERVER.md).
+
+### Ou faites-le vous-même
+
+[Téléchargez Strata](https://github.com/Niko1221/Strata/archive/refs/heads/main.zip) et décompressez-le (ou faites un `git clone`).
+**Windows :** double-cliquez sur **`START-HERE.bat`**. **Linux :** lancez **`./setup.sh`** dans le dossier Strata.
+
+Les étapes sont les mêmes pour NVIDIA et AMD. L'installateur détecte votre carte et installe le bon moteur pour elle.
+Il vous pose quelques questions :
+
+- quel modèle et quelle taille,
+- combien de contexte (la quantité de texte que le modèle garde en tête),
+- s'il doit lire les images.
+
+Appuyez sur Entrée à chaque fois pour garder la réponse recommandée. Ensuite il télécharge le modèle (environ 70 Go)
+et le démarre. Si le téléchargement s'arrête, relancez-le : il reprend là où il s'était arrêté. Votre navigateur
+ouvre l'application Strata à l'adresse `http://127.0.0.1:8080`.
+
+> **Pendant le démarrage du modèle, votre PC peut ralentir ou ne plus répondre pendant 1-3 minutes** (plus longtemps la première fois).
+> Strata charge 35-55 Go dans votre RAM et en réserve une partie pour la carte graphique. C'est normal. Attendez, et
+> ne fermez pas la fenêtre. La fenêtre montre ce que fait Strata.
+
+**La fois suivante**, relancez `START-HERE.bat` (ou `./setup.sh`). Il démarre tout de suite et ne télécharge rien
+deux fois. Fermez sa fenêtre pour arrêter le modèle. `UPDATE.bat` (`./update.sh`) met Strata à jour sans le démarrer.
+Mises à jour, Docker, plusieurs cartes, emplacement des fichiers et toutes les options : [docs/INSTALL.md](docs/INSTALL.md).
+
+## Quel modèle choisir ?
+
+L'installateur en recommande un selon votre RAM. Le même modèle existe en plusieurs tailles, plus ou moins
+compressées. Les petites tailles sont plus rapides. Les grandes sont un peu plus intelligentes.
+
+| Votre RAM | Prenez | Pourquoi |
+| --- | --- | --- |
+| **32 Go** | **Coder** | il tient dans 32 Go et il est fait pour le code (avec une carte de 24 Go, Q2_0 et IQ2_XS tournent aussi) |
+| **48 Go** | **IQ2_XS** (ou Q2_0, le plus rapide) | les plus grandes tailles ne tiennent pas |
+| **64 Go** | **IQ2_XS** (recommandé), ou IQ3_XXS / IQ3_S | toutes les tailles tiennent ; IQ3_S est le meilleur et le plus lent |
+| **96 Go ou plus** | **IQ3_S**, ou l'UD-IQ4_XS d'Unsloth (~4 bits) | de la place pour les plus grandes tailles, avec tout le reste ouvert |
+
+- **[Coder](docs/MODELS.md#coder) :** une version pour le code, avec la moitié des experts en moins. Elle atteint 91 %
+  du score SWE-bench Verified du modèle complet (mesuré par ses auteurs) et tient dans 32 Go de RAM. Elle est moins
+  bonne en dehors du code, y compris pour le chinois et les autres textes CJK (#438). Pour ces cas, prenez Q2_0,
+  IQ2_XS ou IQ3_S, qui gardent tous les experts.
+- **[Swift 1.5](docs/MODELS.md#swift-15) :** un fine-tune qui réfléchit bien moins longtemps avant de répondre. Vous
+  avez la réponse plus tôt, avec à peu près la même qualité.
+- **[Unsloth UD-IQ4_XS](docs/MODELS.md#unsloth-ud-iq4_xs)** : la version ~4 bits d'Unsloth, entre IQ3_S et
+  UD-Q4_K_XL en qualité. Un téléchargement de 94 Go. Avec moins de ~80 Go de RAM, Strata en lit une partie depuis
+  le SSD pendant qu'il répond, il y est donc plus lent (un SSD NVMe aide).
+- **[Unsloth UD-Q4_K_XL](docs/MODELS.md#unsloth-ud-q4_k_xl-experimental)** (expérimental) : le plus proche du modèle
+  complet. Mais Strata en lit la plus grande partie depuis le SSD pendant qu'il répond, donc il n'écrit que
+  7-8.5 tokens/s sur un PC avec 64 Go.
+- **[OrcaRouter's Uncensored IQ3_XXS](docs/MODELS.md#orcarouter-uncensored-iq3_xxs) :** vous l'installez à la main.
+  Il n'est pas dans le menu de l'installateur.
+
+Tailles, téléchargements et ce qui tient où : [docs/MODELS.md](docs/MODELS.md). Pour ajouter un autre modèle plus
+tard, lancez `SETUP.bat` (Linux : `./setup.sh --setup`).
+
+## Utilisation
+
+<p align="center"><img src="docs/media/runpagoda.png" width="900" alt="L'onglet Monitor de l'application Strata à côté d'un agent de code"><br>
+<sub>Le <b>Monitor</b> de l'application Strata (à gauche) pendant qu'un agent de code écrit le jardin de pagode de la vidéo (à droite)</sub></p>
+
+- **Dans le navigateur :** ouvrez `http://127.0.0.1:8080`. Vous y trouvez **Chat**, un **Monitor** en direct du
+  modèle et de votre GPU/CPU/RAM, et **About** avec les réglages et les adresses.
+- **Vos applications et agents de code :** ajoutez un fournisseur « OpenAI-compatible » avec l'URL de base
+  **`http://127.0.0.1:8080/v1`**. N'importe quelle clé API et n'importe quel nom de modèle fonctionnent.
+  - Applications qui utilisent l'API d'Anthropic : `http://127.0.0.1:8080/v1/messages` (Claude Code :
+    `ANTHROPIC_BASE_URL=http://127.0.0.1:8080`).
+  - Codex CLI et les autres applications qui utilisent l'API OpenAI Responses : `/v1/responses`
+    ([configuration](docs/DETAILS.md#the-responses-api-and-codex-cli)).
+- **Réflexion :** choisissez **off, low, medium ou high** dans le menu du chat ou dans le « reasoning effort » de
+  votre application. Off est le plus rapide. High est le meilleur pour les questions difficiles.
+- **Images :** répondez oui à « Images? » pendant l'installation. Ensuite cliquez sur **Picture** dans le chat, ou
+  joignez des images dans votre application. Les cartes AMD lisent les images sous Linux via le processeur ; sous
+  Windows, pas encore.
+- **Depuis votre téléphone ou un autre PC :** `START-HERE.bat --setup --host 0.0.0.0 --api-key <secret>`. Définissez
+  toujours une clé.
+- **Une requête à la fois :** par défaut, Strata répond à une requête et les autres attendent. Pour répondre à
+  plusieurs en même temps, mettez `"parallel": 2` ([BATCHING.md](docs/BATCHING.md)). Sur une carte de 12 Go, chaque
+  réponse devient alors plus lente.
+- **Longs prompts :** Strata lit en entier le premier message d'une discussion, environ 1 minute pour 30,000 tokens.
+  Les messages suivants démarrent en quelques secondes.
+
+Plus d'infos : [où sont stockées vos discussions](docs/INSTALL.md#where-things-are-stored), [l'API](docs/DETAILS.md#using-it).
+
+## Un problème ?
+
+- **Mon PC s'est figé au premier démarrage de Strata.** C'est normal pendant le chargement du modèle. Attendez, et ne
+  fermez pas la fenêtre. Toujours figé après 10 minutes ? Redémarrez le PC, fermez les autres programmes et
+  réessayez, ou choisissez une taille plus petite.
+- **Il s'est arrêté pendant le téléchargement ou l'installation.** Relancez `START-HERE.bat` (ou `./setup.sh`). Il
+  reprend là où il s'était arrêté.
+- **C'est très lent et le voyant du disque clignote sans arrêt, ou il affiche « the engine stopped unexpectedly ».**
+  Votre PC n'a pas assez de RAM libre. Fermez les autres programmes (les navigateurs en utilisent beaucoup), ou
+  choisissez une taille plus petite (Q2_0 ou IQ2_XS).
+- **Il dit que le port 8080 est déjà utilisé.** Strata tourne déjà. Cherchez sa fenêtre.
+
+Autres problèmes et leurs solutions : [docs/TROUBLESHOOTING.md](docs/TROUBLESHOOTING.md). Toujours bloqué ? Ouvrez une
+[issue](https://github.com/Niko1221/Strata/issues) et joignez `strata-<model>.log` depuis le dossier Strata. Vous
+avez trouvé un problème de sécurité ? Signalez-le en privé : [SECURITY.md](SECURITY.md).
+
+## Comment ça marche ?
+
+Les modèles comme celui-ci tournent d'habitude sur des serveurs avec des centaines de gigaoctets de mémoire
+graphique. Votre carte graphique a 12-24 Go. Strata fait tenir le modèle en **répartissant le travail sur tout votre
+PC**. Pensez à une cuisine : ce que vous utilisez tout le temps reste sur le plan de travail, et le reste attend dans
+le placard.
+
+<p align="center"><img src="docs/media/how-it-works.svg" width="860" alt="Les 24,576 experts du modèle : les plus sollicités sur la carte graphique, tous en RAM, une table de correspondance sur le SSD"></p>
+
+- **Le modèle est une équipe de 24,576 petits spécialistes (les « experts »).** Chaque mot n'en demande que 10.
+- **Votre carte graphique** garde les quelques milliers d'experts les plus utilisés. **Votre RAM** les contient tous,
+  et **votre processeur** travaille sur les autres en même temps. **Votre SSD** contient une grande table de
+  correspondance.
+
+<p align="center"><img src="docs/media/guess-and-check.svg" width="860" alt="Un petit assistant devine les mots suivants ; le grand modèle les vérifie tous d'un coup et garde les bons"></p>
+
+- **Deviner, puis vérifier :** un petit assistant devine les quelques mots suivants. Le grand modèle les vérifie
+  tous d'un coup. Vous obtenez la même réponse, 1.6-1.8x plus tôt.
+- **Les longs textes sont lus par gros morceaux** (jusqu'à 8,192 tokens à la fois), à plus de 1,000 tokens par seconde.
+
+L'explication plus longue : [docs/HOW_IT_WORKS.md](docs/HOW_IT_WORKS.md). Chaque partie et ses chiffres :
+[les détails](docs/DETAILS.md#how-it-works) et l'[article](docs/paper/Strata-Paper.pdf).
+
+## Crédits et licence
+
+Le modèle est [Qwen3.8-Flash-Next](https://huggingface.co/Qwen/Qwen3.8-Flash-Next), de l'équipe Qwen. Il a été
+compressé par [ISTA-DASLab](https://huggingface.co/ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF), UkisAI (Swift 1.5)
+et Unsloth. Strata utilise des parties de [llama.cpp / ggml](https://github.com/ggml-org/llama.cpp). Tous les
+crédits : [docs/HOW_IT_WORKS.md](docs/HOW_IT_WORKS.md#credits). Strata est open source sous [licence MIT](LICENSE).
+Quelques parties et chaque modèle ont leur propre licence ([lesquelles](docs/HOW_IT_WORKS.md#license)).
+
+## Soutenir Strata
+
+Strata est gratuit et open source. S'il vous est utile, vous pouvez soutenir son développement :
+
+<p align="center"><a href="https://buymeacoffee.com/strataengine"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" height="50"></a></p>

README.ja.md

@@ -0,0 +1,211 @@
+<h1 align="center">Strata</h1>
+
+[English](README.md) · [简体中文](README.zh-CN.md) · **日本語** · [Deutsch](README.de.md) · [Français](README.fr.md) · [Español](README.es.md) · [Português](README.pt-BR.md)
+
+<p align="center"><b>1,250 億パラメータの AI モデルを、手元のゲーミング PC で動かす</b><br>
+NVIDIA または AMD のグラフィックカード（12 GB 以上） · Windows または Linux · 無料のオープンソース</p>
+
+<p align="center"><a href="https://github.com/Niko1221/Strata/releases/download/v0.1.10/Pagoda.mp4"><img src="docs/media/pagoda-preview.webp" width="720" alt="Strata のモデルが書いたボクセルの五重塔の庭。ブラウザで動いている様子"></a><br>
+<sub>ボクセルの五重塔の庭。1 回のプロンプトで作成し、RTX 5070 上の Strata（IQ3_S、128K コンテキスト）で実行 ·
+<a href="https://github.com/Niko1221/Strata/releases/download/v0.1.10/Pagoda.mp4">動画全体（49 秒）</a></sub></p>
+
+Strata は **[Qwen3.8-Flash-Next](https://huggingface.co/Qwen/Qwen3.8-Flash-Next)** を普通の PC で動かします。
+これは大きくて賢い AI モデルで、ふつうはサーバーが必要です。チャットをしたり、コードを書いたり、画像を読んだりできます。
+アプリやコーディングエージェントとも連携します。データが PC の外に出ることはありません。
+
+## どのくらい速い？
+
+ごく普通のゲーミング PC 2 台で測りました。1 トークンは英単語のおよそ ¾ です。
+
+- **回答の書き出し：** 短いチャットで返答が表示される速さです。毎秒 60 トークンあれば、読むより速く表示されます。
+- **プロンプトの読み込み：** 送った内容を取り込む速さです（ここでは 32K トークンの文書、コード、またはチャット履歴）。
+
+<table>
+<tr><th>NVIDIA: RTX 5070 (12 GB), Ryzen 5 7600, 64 GB RAM</th><th>AMD: RX 9070 XT (16 GB), Ryzen 9 3900X, 47 GB RAM</th></tr>
+<tr><td>
+
+| サイズ | 回答の書き出し | プロンプトの読み込み |
+| --- | ---: | ---: |
+| **Q2_0** | 94 tokens/s | 2,650 tokens/s |
+| **IQ2_XS** | 79 tokens/s | 2,090 tokens/s |
+| **IQ3_XXS** | 62 tokens/s | 1,750 tokens/s |
+| **IQ3_S** | 53 tokens/s | 1,620 tokens/s |
+| **Coder** | 55 tokens/s | 2,180 tokens/s |
+
+</td><td>
+
+| サイズ | 回答の書き出し | プロンプトの読み込み |
+| --- | ---: | ---: |
+| **Q2_0** | 60 tokens/s | 1,160 tokens/s |
+| **IQ2_XS** | 52 tokens/s | 1,110 tokens/s |
+| **Coder** | 44 tokens/s | 1,420 tokens/s |
+
+</td></tr>
+</table>
+
+NVIDIA：Q2_0 はエンジン 0.1.36、ほかの行は 0.1.26 で測定（回答 4K、プロンプト 32K）。表の全体は
+[DETAILS.md](docs/DETAILS.md#speed-measured) にあります。VRAM が多いカードほど速くなります。RTX 3090（24 GB）なら
+毎秒およそ 100-140 トークンで書き出せるはずです。長いチャットやほかのカードについて：[モデルごとの速度](docs/MODELS.md#how-fast-is-each-size)、
+[コミュニティの測定結果](docs/COMMUNITY_BENCHMARKS.md)。
+
+<p align="center"><a href="https://buymeacoffee.com/strataengine"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" height="50"></a><br>
+<sub>Strata は無料です。あなたの PC でうまく動いたら、コーヒー 1 杯の支援が開発を続ける力になります。</sub></p>
+
+## 必要なもの
+
+| | |
+| --- | --- |
+| **グラフィックカード** | **NVIDIA** GeForce RTX 20、30、40、50 シリーズ、または **AMD** Radeon RX 7900 XT / XTX、RX 7800 XT / 7700 XT、RX 9060 XT、RX 9070 / 9070 XT、Radeon AI PRO R9700、RX 6800 / 6900 シリーズ。**VRAM 12 GB 以上**が必要です。 |
+| **RAM** | 32 GB 以上。RAM の量で[使えるモデル](#どのモデルを選べばいい)が決まります。64 GB あればすべてのサイズが動きます。 |
+| **ディスク** | 空き容量が約 80 GB。できれば SSD を使ってください。初回の起動がずっと速くなります。 |
+| **OS** | Windows 10 / 11 または Linux。NVIDIA または AMD の最新のグラフィックドライバー。 |
+
+ほかに必要なものはすべてインストーラーが用意します。2 枚や 3 枚のカードでモデルを分担することもできます（[マルチ GPU](docs/MULTI_GPU.md)）。
+
+試験的なサポート。コミュニティのメンバーが自分のマシンで書き、テストしたものです：
+
+- **古いグラフィックカード**（Tesla P40 / V100、GTX 10、Radeon VII / MI50、RX 6700 XT、RX 5500 XT）：[Older GPUs](docs/OLDER_GPUS.md)。
+- **Intel Arc**（Linux でソースからビルド）：[Intel Arc](docs/INTEL_ARC.md)。
+- **AMD Ryzen AI Max（Strix Halo）**（Linux でソースからビルド）：[Strix Halo](docs/STRIX_HALO.md)。
+- **AVX2 のない古いプロセッサー**：動きますが、遅いです。[Older CPUs](docs/INSTALL.md#older-cpus-experimental)。
+
+全リスト：[docs/INSTALL.md](docs/INSTALL.md#what-you-need)。
+
+## インストール
+
+### AI にセットアップしてもらう
+
+AI コーディングアシスタント（Claude Code、Cursor、Codex、GitHub Copilot など）を使っていますか？ これを貼り付けてください：
+
+```text
+Set up Strata on this PC for me: https://github.com/Niko1221/Strata - follow docs/AI_SETUP.md in that repository.
+```
+
+AI がグラフィックカード、RAM、ディスクを調べて、合うモデルを選びます。それからインストールして起動し、
+アプリのつなぎ方を教えてくれます。AI ツールは Strata の [MCP サーバー](docs/MCP_SERVER.md) を通じて、
+Strata のインストール、起動、停止もできます。
+
+### 自分でやる
+
+[Strata をダウンロード](https://github.com/Niko1221/Strata/archive/refs/heads/main.zip)して展開します（または `git clone` します）。
+**Windows：** **`START-HERE.bat`** をダブルクリック。**Linux：** Strata フォルダーで **`./setup.sh`** を実行します。
+
+手順は NVIDIA でも AMD でも同じです。インストーラーがカードを見つけて、それに合うエンジンを用意します。
+いくつか質問があります：
+
+- どのモデルを、どのサイズで使うか
+- コンテキストをどのくらいにするか（モデルが覚えておけるテキストの量）
+- 画像を読ませるかどうか
+
+毎回 Enter を押せば、おすすめの答えになります。そのあとモデル（約 70 GB）をダウンロードして起動します。
+ダウンロードが止まったら、もう一度実行してください。続きから再開します。ブラウザーで Strata アプリが
+`http://127.0.0.1:8080` に開きます。
+
+> **モデルの起動中、1-3 分ほど PC が重くなったり、反応しなくなったりすることがあります**（初回がいちばん長いです）。
+> Strata は 35-55 GB を RAM に読み込み、その一部をグラフィックカード用に固定します。これは正常です。
+> ウィンドウを閉じずに待ってください。Strata が何をしているかはウィンドウに表示されます。
+
+**次回からは** `START-HERE.bat`（または `./setup.sh`）をもう一度実行します。すぐに起動し、同じものを 2 回ダウンロードすることはありません。
+モデルを止めるときはウィンドウを閉じます。`UPDATE.bat`（`./update.sh`）を使うと、Strata を起動せずに更新できます。
+更新、Docker、複数のカード、ファイルの保存場所、すべてのオプションについて：[docs/INSTALL.md](docs/INSTALL.md)。
+
+## どのモデルを選べばいい
+
+インストーラーが RAM に合わせてひとつ勧めます。同じモデルにも、圧縮の強さが違う複数のサイズがあります。
+小さいサイズほど速く、大きいサイズほど少し賢くなります。
+
+| RAM | 選ぶもの | 理由 |
+| --- | --- | --- |
+| **32 GB** | **Coder** | 32 GB に収まり、コード向けに作られている（24 GB のカードなら Q2_0 と IQ2_XS も動く） |
+| **48 GB** | **IQ2_XS**（または最速の Q2_0） | 大きいサイズは収まらない |
+| **64 GB** | **IQ2_XS**（おすすめ）、または IQ3_XXS / IQ3_S | すべてのサイズが収まる。IQ3_S がいちばん賢く、いちばん遅い |
+| **96 GB 以上** | **IQ3_S**、または Unsloth の UD-IQ4_XS（約 4-bit） | ほかのものを全部開いたままでも、最大のサイズが入る余裕がある |
+
+- **[Coder](docs/MODELS.md#coder)：** エキスパートを半分取り除いたコーディング向け版です。フルモデルの SWE-bench Verified スコアの
+  91% に届き（作者による測定）、32 GB の RAM に収まります。コード以外では弱く、中国語などの CJK のテキストも苦手です（#438）。
+  そうした用途には、すべてのエキスパートを残している Q2_0、IQ2_XS、IQ3_S を選んでください。
+- **[Swift 1.5](docs/MODELS.md#swift-15)：** 答える前に考える時間がずっと短いファインチューン版です。
+  ほぼ同じ品質のまま、答えが早く返ってきます。
+- **[Unsloth UD-IQ4_XS](docs/MODELS.md#unsloth-ud-iq4_xs)：** Unsloth の約 4-bit 版で、品質は IQ3_S と UD-Q4_K_XL の間です。
+  ダウンロードは 94 GB。RAM が約 80 GB 未満だと、答えている間に Strata がその一部を SSD から読むので遅くなります（NVMe SSD が有効です）。
+- **[Unsloth UD-Q4_K_XL](docs/MODELS.md#unsloth-ud-q4_k_xl-experimental)**（試験的）：フルモデルにいちばん近いものです。
+  ただし答えている間、Strata はその大部分を SSD から読むので、64 GB の PC では毎秒 7-8.5 トークンしか書き出せません。
+- **[OrcaRouter's Uncensored IQ3_XXS](docs/MODELS.md#orcarouter-uncensored-iq3_xxs)：** 手動でセットアップします。
+  インストーラーのメニューにはありません。
+
+サイズ、ダウンロード、どれがどこに収まるかについて：[docs/MODELS.md](docs/MODELS.md)。あとで別のモデルを追加するには
+`SETUP.bat`（Linux：`./setup.sh --setup`）を実行します。
+
+## 使い方
+
+<p align="center"><img src="docs/media/runpagoda.png" width="900" alt="Strata アプリの Monitor タブと、その横で動くコーディングエージェント"><br>
+<sub>Strata アプリの <b>Monitor</b>（左）。コーディングエージェントが動画の五重塔の庭を書いているところ（右）</sub></p>
+
+- **ブラウザーで：** `http://127.0.0.1:8080` を開きます。**Chat**、モデルと GPU/CPU/RAM の様子をリアルタイムで見る **Monitor**、
+  設定とアドレスが載った **About** があります。
+- **アプリやコーディングエージェントから：** 「OpenAI-compatible」プロバイダーを追加し、ベース URL に
+  **`http://127.0.0.1:8080/v1`** を指定します。API キーとモデル名は何でもかまいません。
+  - Anthropic の API を使うアプリ：`http://127.0.0.1:8080/v1/messages`（Claude Code：
+    `ANTHROPIC_BASE_URL=http://127.0.0.1:8080`）。
+  - Codex CLI など OpenAI Responses API を使うアプリ：`/v1/responses`
+    （[設定方法](docs/DETAILS.md#the-responses-api-and-codex-cli)）。
+- **思考（Thinking）：** チャットのメニュー、またはアプリの「reasoning effort」で **off、low、medium、high** を選びます。
+  off がいちばん速く、high は難しい質問に向いています。
+- **画像：** セットアップで「Images?」に yes と答えます。そのあとチャットで **Picture** をクリックするか、アプリで画像を添付します。
+  AMD のカードは、Linux ではプロセッサーを使って画像を読みます。Windows ではまだ読めません。
+- **スマートフォンや別の PC から：** `START-HERE.bat --setup --host 0.0.0.0 --api-key <secret>`。キーは必ず設定してください。
+- **リクエストは 1 つずつ：** 初期設定では Strata は 1 つのリクエストに答え、ほかは待ちます。複数に同時に答えるには
+  `"parallel": 2` を設定します（[BATCHING.md](docs/BATCHING.md)）。12 GB のカードでは、それぞれの回答が遅くなります。
+- **長いプロンプト：** Strata はチャットの最初のメッセージをすべて読みます。30,000 トークンあたり約 1 分かかります。
+  2 通目以降のメッセージは数秒で始まります。
+
+詳しくは：[チャットの保存場所](docs/INSTALL.md#where-things-are-stored)、[API](docs/DETAILS.md#using-it)。
+
+## うまくいかないとき
+
+- **Strata の初回起動で PC が固まった。** モデルの読み込み中はよくあることです。ウィンドウを閉じずに待ってください。
+  10 分たっても固まったまま？ PC を再起動し、ほかのプログラムを閉じてもう一度試すか、小さいサイズを選んでください。
+- **ダウンロードやインストールの途中で止まった。** `START-HERE.bat`（または `./setup.sh`）をもう一度実行してください。
+  止まったところから続きます。
+- **とても遅く、ディスクのランプが点滅し続ける。または「the engine stopped unexpectedly」と表示される。** PC の空き RAM が
+  足りません。ほかのプログラムを閉じるか（ブラウザーは多く使います）、小さいサイズ（Q2_0 または IQ2_XS）を選んでください。
+- **ポート 8080 がすでに使われていると表示される。** Strata はもう動いています。そのウィンドウを探してください。
+
+ほかの問題と解決方法：[docs/TROUBLESHOOTING.md](docs/TROUBLESHOOTING.md)。それでも解決しない場合は
+[issue](https://github.com/Niko1221/Strata/issues) を作成し、Strata フォルダーにある `strata-<model>.log` を添付してください。
+セキュリティの問題を見つけたら、非公開で報告してください：[SECURITY.md](SECURITY.md)。
+
+## しくみ
+
+このようなモデルは、ふつう数百ギガバイトのグラフィックメモリを持つサーバーで動きます。あなたのグラフィックカードは
+12-24 GB です。Strata は **PC 全体で作業を分担する** ことで、モデルを収めています。台所を思い浮かべてください。
+いつも使う道具は調理台に置き、残りは食料庫にしまっておきます。
+
+<p align="center"><img src="docs/media/how-it-works.svg" width="860" alt="モデルの 24,576 個のエキスパート：よく使うものはグラフィックカードに、すべては RAM に、ルックアップテーブルは SSD に"></p>
+
+- **モデルは 24,576 個の小さな専門家（「エキスパート」）のチームです。** 1 語ごとに必要なのはそのうち 10 個だけです。
+- **グラフィックカード** は、よく使われる数千個のエキスパートを持ちます。**RAM** はすべてのエキスパートを持ち、
+  **プロセッサー** が残りを同時に処理します。**SSD** は大きなルックアップテーブルを持ちます。
+
+<p align="center"><img src="docs/media/guess-and-check.svg" width="860" alt="小さなヘルパーが次の単語を予想し、大きなモデルがまとめて確認して正しいものを残す"></p>
+
+- **予想してから確認：** 小さなヘルパーが次の数語を予想します。大きなモデルがそれをまとめて確認します。
+  答えは同じまま、1.6-1.8x 早く返ってきます。
+- **長いテキストは大きなまとまりで読みます**（一度に最大 8,192 トークン）。速さは毎秒 1,000 トークン以上です。
+
+もっと詳しい説明：[docs/HOW_IT_WORKS.md](docs/HOW_IT_WORKS.md)。各部分とその数値：
+[詳細](docs/DETAILS.md#how-it-works)と[論文](docs/paper/Strata-Paper.pdf)。
+
+## クレジットとライセンス
+
+モデルは Qwen チームの [Qwen3.8-Flash-Next](https://huggingface.co/Qwen/Qwen3.8-Flash-Next) です。
+圧縮は [ISTA-DASLab](https://huggingface.co/ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF)、UkisAI（Swift 1.5）、
+Unsloth によるものです。Strata は [llama.cpp / ggml](https://github.com/ggml-org/llama.cpp) の一部を使っています。すべてのクレジット：
+[docs/HOW_IT_WORKS.md](docs/HOW_IT_WORKS.md#credits)。Strata は [MIT License](LICENSE) のオープンソースです。一部のパーツと
+すべてのモデルには、それぞれ独自のライセンスがあります（[どれか](docs/HOW_IT_WORKS.md#license)）。
+
+## Strata を支援する
+
+Strata は無料のオープンソースです。役に立ったら、開発を支援していただけます：
+
+<p align="center"><a href="https://buymeacoffee.com/strataengine"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" height="50"></a></p>

README.md

@@ -1,221 +1,212 @@
 <h1 align="center">Strata</h1>
 
-<p align="center"><b>Run a 125-billion-parameter AI model on a normal gaming PC</b><br>
-one NVIDIA card (12-24 GB) + 64 GB of RAM · Windows or Linux · one click to install</p>
+**English** · [简体中文](README.zh-CN.md) · [日本語](README.ja.md) · [Deutsch](README.de.md) · [Français](README.fr.md) · [Español](README.es.md) · [Português](README.pt-BR.md)
+
+<p align="center"><b>Run a 125-billion-parameter AI model on your own gaming PC</b><br>
+NVIDIA or AMD graphics card (12 GB or more) · Windows or Linux · free and open source</p>
 
 <p align="center"><a href="https://github.com/Niko1221/Strata/releases/download/v0.1.10/Pagoda.mp4"><img src="docs/media/pagoda-preview.webp" width="720" alt="A voxel pagoda garden that Strata's model wrote, running in the browser"></a><br>
 <sub>A voxel pagoda garden, 1 shot prompt running on an RTX 5070 with Strata (IQ3_S, 128K context) ·
 <a href="https://github.com/Niko1221/Strata/releases/download/v0.1.10/Pagoda.mp4">full video (49 s)</a></sub></p>
 
-Strata runs **[Qwen3.8-Flash-Next](https://huggingface.co/Qwen/Qwen3.8-Flash-Next)** - a large, smart AI model that
-normally needs a server - on your own PC. It writes its answers at **60-95 tokens per second** (a token is about ¾
-of a word): faster than you can read.
-
-- **Free and open source.**
-
-> **Jump to:** [How fast?](#how-fast-is-it) · [Which model?](#which-model-should-i-pick) · [Install](#install) ·
-> [Using it](#using-it) · [Problems?](#something-went-wrong) · [How it works](#how-does-it-work) ·
-> [All the details](docs/DETAILS.md)
-
----
+Strata runs **[Qwen3.8-Flash-Next](https://huggingface.co/Qwen/Qwen3.8-Flash-Next)** on a normal PC. This is a
+large, smart AI model that usually needs a server. It chats, writes code, reads pictures and works with your apps
+and coding agents. Nothing leaves your PC.
 
 ## How fast is it?
 
-Measured on an RTX 5070 (12 GB), a Ryzen 5 7600 and 64 GB of RAM:
+We measured it on two ordinary gaming PCs. A token is about ¾ of a word.
 
-| Size | Writes answers (short chat) | Writes answers (128K context) | Reads your prompt |
-| --- | ---: | ---: | ---: |
-| **Q2_0** | 93 tokens/s | 74 tokens/s | 2,170 tokens/s |
-| **IQ2_XS** | 79 tokens/s | 63 tokens/s | 2,090 tokens/s |
-| **IQ3_XXS** | 62 tokens/s | 49 tokens/s | 1,750 tokens/s |
-| **IQ3_S** | 53 tokens/s | 46 tokens/s | 1,620 tokens/s |
-| **Coder** (IQ1_M) | 55 tokens/s | 43 tokens/s | 2,180 tokens/s |
+- **Writes answers:** how fast the reply appears in a short chat. 60 tokens per second is faster than you can read.
+- **Reads your prompt:** how fast it takes in what you send (here a 32K-token document, code or chat history).
 
-- **Writes answers** = how fast the reply appears (tokens per second).
-- **Reads your prompt** = how fast it takes in what you send (long documents, code, chat history), measured on a
-  32K-token prompt; a 4K prompt reads at 910-1,580 tokens/s. A 32K prompt takes about 15 seconds with Q2_0.
+<table>
+<tr><th>NVIDIA: RTX 5070 (12 GB), Ryzen 5 7600, 64 GB RAM</th><th>AMD: RX 9070 XT (16 GB), Ryzen 9 3900X, 47 GB RAM</th></tr>
+<tr><td>
 
-A card with more VRAM is faster, because more of the model fits on the GPU: an RTX 3090 (24 GB) should do roughly
-100-140 tokens per second. All measurements, long-context numbers and estimates for other cards are in the
-[details](docs/DETAILS.md#speed-measured).
+| Size | Writes answers | Reads your prompt |
+| --- | ---: | ---: |
+| **Q2_0** | 94 tokens/s | 2,650 tokens/s |
+| **IQ2_XS** | 79 tokens/s | 2,090 tokens/s |
+| **IQ3_XXS** | 62 tokens/s | 1,750 tokens/s |
+| **IQ3_S** | 53 tokens/s | 1,620 tokens/s |
+| **Coder** | 55 tokens/s | 2,180 tokens/s |
 
-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).
+</td><td>
 
-**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
-card, this start only). Each card keeps the experts of its own layers, and prompts flow through the cards in a
-pipeline: on an RTX 5080 + RTX 3090 prompts were read 18-20% faster than on the 5080 alone, decoding on par.
-Every card must be an RTX 20 series or newer with 8 GB or more. See [docs/MULTI_GPU.md](docs/MULTI_GPU.md).
+| Size | Writes answers | Reads your prompt |
+| --- | ---: | ---: |
+| **Q2_0** | 60 tokens/s | 1,160 tokens/s |
+| **IQ2_XS** | 52 tokens/s | 1,110 tokens/s |
+| **Coder** | 44 tokens/s | 1,420 tokens/s |
 
-## Which model should I pick?
+</td></tr>
+</table>
 
-**The size** (the same model, compressed more or less):
+NVIDIA: Q2_0 with engine 0.1.36, the other rows with 0.1.26 (4K answers, 32K prompts). The full tables are in
+[DETAILS.md](docs/DETAILS.md#speed-measured). A card with more VRAM is faster: an RTX 3090 (24 GB) should write
+about 100-140 tokens per second. Long chats and other cards: [speed of each model](docs/MODELS.md#how-fast-is-each-size),
+[community results](docs/COMMUNITY_BENCHMARKS.md).
 
-| Model | RAM+VRAM Requirements | Speed | Quality |
-| --- | ---: | --- | --- |
-| **Q2_0** | 37.6 GB | fastest | good |
-| **IQ2_XS** | 39.2 GB | fast | better (**recommended**) |
-| **IQ3_XXS** | 47.0 GB | slower | great |
-| **IQ3_S** | 54.8 GB | slowest | best: matches the full model on the published tests (original model only) |
+<p align="center"><a href="https://buymeacoffee.com/strataengine"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" height="50"></a><br>
+<sub>Strata is free. If it runs well on your PC, a coffee keeps the work on it going.</sub></p>
 
-**Will it fit?** Shard 1 is the part of the model that gets loaded when it starts: its experts go into your **RAM**,
-the rest onto your graphics card (the second shard, a 29 GB lookup table, stays on the SSD). So it fits when your
-**RAM is at least shard 1 + about 10 GB** for Windows and your other programs. With 64 GB of RAM every size fits
-(IQ3_S with little else open); with 48 GB, Q2_0 and IQ2_XS. A bigger graphics card makes it faster, but it doesn't
-lower the RAM needed.
+## What you need
 
-**The version:**
+| | |
+| --- | --- |
+| **Graphics card** | **NVIDIA** GeForce RTX 20, 30, 40 or 50 series, or **AMD** Radeon RX 7900 XT / XTX, RX 7800 XT / 7700 XT, RX 9060 XT, RX 9070 / 9070 XT, Radeon AI PRO R9700 or RX 6800 / 6900 series. It needs **12 GB of VRAM or more**. |
+| **RAM** | 32 GB or more. Your RAM decides [which model](#which-model-should-i-pick) fits. 64 GB runs every size. |
+| **Disk** | About 80 GB free. Use an SSD if you can: the first start is much faster. |
+| **System** | Windows 10 / 11 or Linux, and a current graphics driver from NVIDIA or AMD. |
 
-- **Qwen3.8-Flash-Next** - the original.
-- **[Coder](https://huggingface.co/ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF)** - ISTA-DASLab's coding
-  version: half of the experts removed, keeping the ones that code, tool use and images need (91% of the full model's
-  SWE-bench Verified score, 99% of LiveCodeBench, by its authors). One size (IQ1_M: its experts stored like IQ3_S):
-  shard 1 is **29.6 GB**, so it fits a PC with **32 GB of RAM**, runs 262K context on 64 GB, and reads long prompts
-  the fastest of all. Weaker outside coding.
-- **[Swift 1.5](https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF)** - a fine-tune by UkisAI
-  that thinks much shorter before answering, so you get the answer sooner, with about the same quality. Same speed per
-  token, and about the same RAM as the same size of the original (no IQ3_S). Its own license applies (see its page).
+The installer sets up everything else. Two or three cards can share the model ([multi-GPU](docs/MULTI_GPU.md)).
 
-Not sure? Take **IQ2_XS** - or the **Coder** if you mainly write code, or have 32-48 GB of RAM. You can add another
-one later with `SETUP.bat` (the same as `START-HERE.bat --setup`; on Linux `./setup.sh --setup`).
+Experimental, written and tested by community members on their own machines:
 
-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.
+- **Older graphics cards** (Tesla P40 / V100, GTX 10, Radeon VII / MI50, RX 6700 XT, RX 5500 XT): [Older GPUs](docs/OLDER_GPUS.md).
+- **Intel Arc**, built from source on Linux: [Intel Arc](docs/INTEL_ARC.md).
+- **AMD Ryzen AI Max (Strix Halo)**, built from source on Linux: [Strix Halo](docs/STRIX_HALO.md).
+- **Older processors without AVX2**: they work, but slowly. [Older CPUs](docs/INSTALL.md#older-cpus-experimental).
 
-An **AMD Radeon RX 7900 XT / XTX on Linux** works too (experimental): `./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).
+The full list: [docs/INSTALL.md](docs/INSTALL.md#what-you-need).
 
 ## Install
 
-**You need:** an NVIDIA RTX 20, 30, 40 or 50 card with 12 GB of VRAM or more (RTX 20 since 0.1.27), enough RAM for the size you pick (above;
-a big GPU makes up for less RAM - the [low-RAM mode](docs/DETAILS.md)),
-~80 GB of free disk space (an SSD makes the first start much faster), and Windows 10/11 or Linux. The only thing you
-install yourself is a current **NVIDIA driver** ([nvidia.com/drivers](https://www.nvidia.com/drivers) or the NVIDIA
-App). Everything else - Python, the engine, the model - is set up for you.
+### Let your AI set it up
 
-**Windows**
+Do you use an AI coding assistant (Claude Code, Cursor, Codex, GitHub Copilot, ...)? Paste this into it:
 
-1. [Download this project](https://github.com/Niko1221/Strata/archive/refs/heads/main.zip) and unzip it (or `git clone` it).
-2. Double-click **`START-HERE.bat`**.
-3. Answer a few questions - or just press Enter each time for the recommended choice:
-   - **Which model and size?** The original or Swift 1.5, and Q2_0, IQ2_XS, IQ3_XXS or IQ3_S - see [above](#which-model-should-i-pick)
-   - **How much context?** How much text it can keep in mind at once (it suggests one for your card)
-   - **Images?** Whether it should also read pictures
-   - **Experimental speed projection?** Off unless you say yes - [read what it does](docs/DETAILS.md#experimental-speed-projection-experimental-off-by-default) first
+```text
+Set up Strata on this PC for me: https://github.com/Niko1221/Strata - follow docs/AI_SETUP.md in that repository.
+```
 
-Then it downloads everything (the model is ~70 GB, so the first time takes a while - you can stop and it picks up
-where it left off) and **starts the model**. Your browser opens the Strata app at `http://127.0.0.1:8080`.
+It checks your graphics card, RAM and disk and picks the model that fits. Then it installs and starts it and tells
+you how to connect your apps. AI tools can also install, start and stop Strata through its
+[MCP server](docs/MCP_SERVER.md).
 
-> **While the model starts, your PC can be slow or stop responding for 1-3 minutes** (longest the first time): Strata
-> loads 35-55 GB into your RAM and locks part of it for the graphics card. That's normal - wait, and don't close the
-> window. The window tells you what it is doing.
+### Or do it yourself
 
-**Next time**, just double-click `START-HERE.bat` again: it starts right away, nothing is downloaded twice. Close its
-window to stop the model.
+[Download Strata](https://github.com/Niko1221/Strata/archive/refs/heads/main.zip) and unzip it (or `git clone` it).
+**Windows:** double-click **`START-HERE.bat`**. **Linux:** run **`./setup.sh`** in the Strata folder.
 
-**Updating:** download the new version and unzip it anywhere (or `git pull`), then run `START-HERE.bat` in it. The
-model files are kept in a `Strata-data` folder next to your Strata folder, so a new copy finds them and sets itself up
-the same way - nothing big is downloaded again.
+The steps are the same for NVIDIA and AMD. The installer finds your card and sets up the right engine for it. It
+asks you a few questions:
 
-**Linux:** run `./setup.sh` - same questions, same result.
+- which model and which size,
+- how much context (how much text the model keeps in mind),
+- whether it should read pictures.
+
+Press Enter each time for the recommended answer. Then it downloads the model (about 70 GB) and starts it. If the
+download stops, run it again: it continues where it left off. Your browser opens the Strata app at
+`http://127.0.0.1:8080`.
+
+> **While the model starts, your PC can be slow or stop responding for 1-3 minutes** (longest the first time).
+> Strata loads 35-55 GB into your RAM and locks part of it for the graphics card. This is normal. Wait, and don't
+> close the window. The window shows what Strata is doing.
+
+**Next time**, run `START-HERE.bat` (or `./setup.sh`) again. It starts right away and downloads nothing twice. Close
+its window to stop the model. `UPDATE.bat` (`./update.sh`) updates Strata without starting it. Updating, Docker,
+several cards, where the files go and every option: [docs/INSTALL.md](docs/INSTALL.md).
+
+## Which model should I pick?
+
+The installer recommends one for your RAM. The same model comes in several sizes, compressed more or less. Smaller
+sizes are faster. Larger sizes are a bit smarter.
+
+| Your RAM | Take | Why |
+| --- | --- | --- |
+| **32 GB** | **Coder** | it fits 32 GB, and it is made for code (with a 24 GB card, Q2_0 and IQ2_XS run too) |
+| **48 GB** | **IQ2_XS** (or Q2_0, the fastest) | the larger sizes do not fit |
+| **64 GB** | **IQ2_XS** (recommended), or IQ3_XXS / IQ3_S | every size fits; IQ3_S is the best and the slowest |
+| **96 GB or more** | **IQ3_S**, or Unsloth's UD-IQ4_XS (~4-bit) | room for the largest sizes with everything else open |
+
+- **[Coder](docs/MODELS.md#coder):** a coding version with half of the experts removed. It reaches 91% of the full
+  model's SWE-bench Verified score (measured by its authors) and fits 32 GB of RAM. It is weaker outside code,
+  including Chinese and other CJK text (#438). For those, take Q2_0, IQ2_XS or IQ3_S, which keep every expert.
+- **[Swift 1.5](docs/MODELS.md#swift-15):** a fine-tune that thinks for a much shorter time before it answers. You
+  get the answer sooner, at about the same quality.
+- **[Unsloth UD-IQ4_XS](docs/MODELS.md#unsloth-ud-iq4_xs):** Unsloth's ~4-bit version, between IQ3_S and
+  UD-Q4_K_XL in quality. A 94 GB download. With less than ~80 GB of RAM, Strata reads part of it from the SSD
+  while it answers, so it is slower there (an NVMe SSD helps).
+- **[Unsloth UD-Q4_K_XL](docs/MODELS.md#unsloth-ud-q4_k_xl-experimental)** (experimental): the closest to the full
+  model. But Strata reads most of it from the SSD while it answers, so it writes only 7-8.5 tokens/s on a 64 GB PC.
+- **[OrcaRouter's Uncensored IQ3_XXS](docs/MODELS.md#orcarouter-uncensored-iq3_xxs):** you set it up by hand. It is
+  not in the installer's menu.
+
+Sizes, downloads and what fits where: [docs/MODELS.md](docs/MODELS.md). To add another model later, run
+`SETUP.bat` (Linux: `./setup.sh --setup`).
 
 ## Using it
 
 <p align="center"><img src="docs/media/runpagoda.png" width="900" alt="The Strata app's Monitor tab next to a coding agent"><br>
 <sub>The Strata app's <b>Monitor</b> (left) while a coding agent writes the pagoda garden from the video (right)</sub></p>
 
-- **In the browser:** `http://127.0.0.1:8080` - the Strata app (it opens by itself when the model starts): **Chat**, a
-  live **Monitor** of the model and your GPU/CPU/RAM, and **About** with the settings and addresses.
-- **Chat in the terminal:** `.venv\Scripts\python chat.py`
-- **Your apps and coding agents:** add it as an "OpenAI-compatible" provider with base URL
-  **`http://127.0.0.1:8080/v1`**, any API key and any model name. Apps that use Anthropic's API: `http://127.0.0.1:8080/v1/messages`.
-- **Thinking:** the model thinks before it answers. Choose **off, low, medium or high** - in the chat page menu, with
-  `/think low` in `chat.py`, or with your app's "reasoning effort" setting. Off is fastest; high is best for hard questions.
-- **Pictures:** in the chat page click **Picture**; in `chat.py` type `/image <path>`; in apps just attach them.
-- **From your phone or another PC:** `START-HERE.bat --setup --host 0.0.0.0 --api-key <secret>`, then open the
-  address the server window prints; see the [details](docs/DETAILS.md#using-it).
-- **Experimental speed projection (off by default):** an experimental control vector that setup can turn on; it
-  changes how the model answers - read [what it does](docs/DETAILS.md#experimental-speed-projection-experimental-off-by-default) first.
+- **In the browser:** open `http://127.0.0.1:8080`. It has **Chat**, a live **Monitor** of the model and your
+  GPU/CPU/RAM, and **About** with the settings and addresses.
+- **Your apps and coding agents:** add an "OpenAI-compatible" provider with the base URL
+  **`http://127.0.0.1:8080/v1`**. Any API key and any model name work.
+  - Apps that use Anthropic's API: `http://127.0.0.1:8080/v1/messages` (Claude Code:
+    `ANTHROPIC_BASE_URL=http://127.0.0.1:8080`).
+  - Codex CLI and other apps that use the OpenAI Responses API: `/v1/responses`
+    ([setup](docs/DETAILS.md#the-responses-api-and-codex-cli)).
+- **Thinking:** choose **off, low, medium or high** in the chat menu or in your app's "reasoning effort". Off is the
+  fastest. High is best for hard questions.
+- **Pictures:** say yes to "Images?" in setup. Then click **Picture** in the chat, or attach pictures in your app.
+  AMD cards read pictures on Linux through the processor; on Windows they can't yet.
+- **From your phone or another PC:** `START-HERE.bat --setup --host 0.0.0.0 --api-key <secret>`. Always set a key.
+- **One request at a time:** by default Strata answers one request, and the others wait. To answer several at once,
+  set `"parallel": 2` ([BATCHING.md](docs/BATCHING.md)). On a 12 GB card this makes each answer slower.
+- **Long prompts:** Strata reads the first message of a chat in full, about 1 minute per 30,000 tokens. Follow-up
+  messages start in seconds.
 
-**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.
+More: [where your chats are stored](docs/INSTALL.md#where-things-are-stored), [the API](docs/DETAILS.md#using-it).
 
 ## Something went wrong?
 
-**My PC froze, or got very slow, the first time Strata started.**
-That's normal while it starts, most of all the first time. Strata loads 35-55 GB into your RAM, locks part of it for
-the graphics card, and works out how much of the model fits on your GPU. The mouse can freeze for a few minutes. **Wait, and don't close the
-window.** The next starts are much faster. Still frozen after 10 minutes? Restart the PC, close other programs
-(browsers use a lot of RAM) and try again. If it keeps happening, pick a smaller size (Q2_0 or IQ2_XS).
+- **My PC froze the first time Strata started.** This is normal while it loads the model. Wait, and don't close the
+  window. Still frozen after 10 minutes? Restart the PC, close other programs and try again, or pick a smaller size.
+- **It stopped while downloading or installing.** Run `START-HERE.bat` (or `./setup.sh`) again. It continues where
+  it stopped.
+- **It's very slow and the disk light keeps blinking, or it says "the engine stopped unexpectedly".** Your PC does
+  not have enough free RAM. Close other programs (browsers use a lot), or pick a smaller size (Q2_0 or IQ2_XS).
+- **It says port 8080 is already in use.** Strata is already running. Look for its window.
 
-**It stopped while downloading or installing.**
-Run `START-HERE.bat` again. It continues where it stopped.
-
-**It says the NVIDIA driver is too old.**
-Update it (NVIDIA App or [nvidia.com/drivers](https://www.nvidia.com/drivers)), restart the PC, and run
-`START-HERE.bat` again.
-
-**It says port 8080 is already in use.**
-Strata is already running. Look for its window.
-
-**It's very slow and the disk light keeps blinking.**
-Your PC is out of free RAM. Close other programs, or pick a smaller size (Q2_0 or IQ2_XS).
-
-**An answer stopped with "the engine stopped unexpectedly".**
-Usually not enough RAM (on Linux the system then stops the engine). Just send your message again: Strata starts the
-engine by itself. If it keeps happening, close other programs or pick a smaller size.
-
-**It says the prompt exceeds the context.**
-The conversation is longer than the context you chose. Start a new chat, or run `SETUP.bat` and pick more
-context.
-
-**Still stuck?** Look in the [full troubleshooting table](docs/DETAILS.md#troubleshooting), or open an issue and
-attach `strata-<model>.log` from the Strata folder.
+More problems and their fixes: [docs/TROUBLESHOOTING.md](docs/TROUBLESHOOTING.md). Still stuck? Open an
+[issue](https://github.com/Niko1221/Strata/issues) and attach `strata-<model>.log` from the Strata folder. Found a
+security problem? Report it privately: [SECURITY.md](SECURITY.md).
 
 ## How does it work?
 
-Models like this one normally run on servers with hundreds of gigabytes of graphics memory. Your graphics card has
-12-24 GB. Strata makes it fit by **sharing the work across your whole PC** - the same idea as a kitchen, where the
-things you use all the time stay on the counter and the rest waits in the pantry.
+Models like this one usually run on servers with hundreds of gigabytes of graphics memory. Your graphics card has
+12-24 GB. Strata makes the model fit by **sharing the work across your whole PC**. Think of a kitchen: the things
+you use all the time stay on the counter, and the rest waits in the pantry.
 
 <p align="center"><img src="docs/media/how-it-works.svg" width="860" alt="The model's 24,576 experts: the busiest on the graphics card, all of them in RAM, a lookup table on the SSD"></p>
 
-- **The model is a team of 24,576 small specialists ("experts"),** and each word it writes needs only 10 of them.
-  So it doesn't have to have all of them on the graphics card at once.
-- **Your graphics card** does the part of the work needed for every word, and keeps the few thousand experts that
-  are asked most often. It keeps learning which ones those are while you use it.
-- **Your RAM** holds every expert. When a word needs one the card doesn't have, **your processor** works on it -
-  at the same time as the graphics card, so neither waits for the other.
-- **Your SSD** holds a big lookup table; the model only reads a few small rows of it per word.
+- **The model is a team of 24,576 small specialists ("experts").** Each word needs only 10 of them.
+- **Your graphics card** keeps the few thousand experts that are used most often. **Your RAM** holds all of them,
+  and **your processor** works on the rest at the same time. **Your SSD** holds a big lookup table.
 
 <p align="center"><img src="docs/media/guess-and-check.svg" width="860" alt="A small helper guesses the next words; the big model checks them all at once and keeps the right ones"></p>
 
-- **Guess, then check.** A small, fast helper built into the model guesses the next few words, and the big model
-  checks all the guesses in one go. It keeps the ones it agrees with and writes the next word itself - so one step
-  often produces several words. The helper only guesses - the big model decides every word - so you get the same
-  quality answer, 1.6-1.8x sooner.
-- **Long texts are read in big pieces** (up to 8,192 tokens - pieces of words - at a time), which is why a long
-  document or code base is read at over 1,000 tokens per second.
+- **Guess, then check:** a small helper guesses the next few words. The big model checks them all at once. You get
+  the same answer, 1.6-1.8x sooner.
+- **Long texts are read in big pieces** (up to 8,192 tokens at a time), at over 1,000 tokens per second.
 
-Want the full picture? The [details](docs/DETAILS.md#how-it-works) explain every part and its numbers, and the
-[paper](docs/paper/Strata-Paper.pdf) tells the whole story, with the measurements behind it.
+The longer explanation: [docs/HOW_IT_WORKS.md](docs/HOW_IT_WORKS.md). Every part and its numbers:
+[the details](docs/DETAILS.md#how-it-works) and the [paper](docs/paper/Strata-Paper.pdf).
 
-## Credits
+## Credits and license
 
-- 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.
-- 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).
+The model is [Qwen3.8-Flash-Next](https://huggingface.co/Qwen/Qwen3.8-Flash-Next) by the Qwen team. It was
+compressed by [ISTA-DASLab](https://huggingface.co/ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF), UkisAI (Swift 1.5)
+and Unsloth. Strata uses parts of [llama.cpp / ggml](https://github.com/ggml-org/llama.cpp). All credits:
+[docs/HOW_IT_WORKS.md](docs/HOW_IT_WORKS.md#credits). Strata is open source under the [MIT License](LICENSE). A few
+parts and every model have their own licenses ([which ones](docs/HOW_IT_WORKS.md#license)).
 
-## License
+## Support Strata
 
-Strata is open source under the [MIT License](LICENSE). A few parts carry their own licenses: `third_party/ggml`
-(MIT, llama.cpp / ggml), the web app's font (SIL Open Font License 1.1) and the experimental speed projection's
-vector in `data/experimental-speed-projection` (Qwen Community License 1.0, from the model's activations). The
-models are not part of this repository; each model's own license applies to its files.
+Strata is free and open source. If it is useful to you, you can support its development:
+
+<p align="center"><a href="https://buymeacoffee.com/strataengine"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" height="50"></a></p>

README.pt-BR.md

@@ -0,0 +1,217 @@
+<h1 align="center">Strata</h1>
+
+[English](README.md) · [简体中文](README.zh-CN.md) · [日本語](README.ja.md) · [Deutsch](README.de.md) · [Français](README.fr.md) · [Español](README.es.md) · **Português**
+
+<p align="center"><b>Rode um modelo de IA de 125 bilhões de parâmetros no seu próprio PC gamer</b><br>
+Placa de vídeo NVIDIA ou AMD (12 GB ou mais) · Windows ou Linux · gratuito e de código aberto</p>
+
+<p align="center"><a href="https://github.com/Niko1221/Strata/releases/download/v0.1.10/Pagoda.mp4"><img src="docs/media/pagoda-preview.webp" width="720" alt="Um jardim com pagode em voxels que o modelo do Strata escreveu, rodando no navegador"></a><br>
+<sub>Um jardim com pagode em voxels, feito com um único prompt numa RTX 5070 com o Strata (IQ3_S, contexto de 128K) ·
+<a href="https://github.com/Niko1221/Strata/releases/download/v0.1.10/Pagoda.mp4">vídeo completo (49 s)</a></sub></p>
+
+O Strata roda o **[Qwen3.8-Flash-Next](https://huggingface.co/Qwen/Qwen3.8-Flash-Next)** num PC comum. É um modelo
+de IA grande e inteligente que normalmente precisa de um servidor. Ele conversa, escreve código, entende imagens e
+funciona com seus apps e agentes de programação. Nada sai do seu PC.
+
+## Qual é a velocidade?
+
+Medimos em dois PCs gamer comuns. Um token equivale a cerca de ¾ de uma palavra.
+
+- **Escreve respostas:** a velocidade com que a resposta aparece numa conversa curta. 60 tokens por segundo é mais rápido do que você consegue ler.
+- **Lê seu prompt:** a velocidade com que ele absorve o que você envia (aqui, um documento, código ou histórico de conversa de 32K tokens).
+
+<table>
+<tr><th>NVIDIA: RTX 5070 (12 GB), Ryzen 5 7600, 64 GB de RAM</th><th>AMD: RX 9070 XT (16 GB), Ryzen 9 3900X, 47 GB de RAM</th></tr>
+<tr><td>
+
+| Tamanho | Escreve respostas | Lê seu prompt |
+| --- | ---: | ---: |
+| **Q2_0** | 94 tokens/s | 2,650 tokens/s |
+| **IQ2_XS** | 79 tokens/s | 2,090 tokens/s |
+| **IQ3_XXS** | 62 tokens/s | 1,750 tokens/s |
+| **IQ3_S** | 53 tokens/s | 1,620 tokens/s |
+| **Coder** | 55 tokens/s | 2,180 tokens/s |
+
+</td><td>
+
+| Tamanho | Escreve respostas | Lê seu prompt |
+| --- | ---: | ---: |
+| **Q2_0** | 60 tokens/s | 1,160 tokens/s |
+| **IQ2_XS** | 52 tokens/s | 1,110 tokens/s |
+| **Coder** | 44 tokens/s | 1,420 tokens/s |
+
+</td></tr>
+</table>
+
+NVIDIA: Q2_0 com o engine 0.1.36, as outras linhas com 0.1.26 (respostas de 4K, prompts de 32K). As tabelas
+completas estão em [DETAILS.md](docs/DETAILS.md#speed-measured). Uma placa com mais VRAM é mais rápida: uma
+RTX 3090 (24 GB) deve escrever cerca de 100-140 tokens por segundo. Conversas longas e outras placas:
+[velocidade de cada modelo](docs/MODELS.md#how-fast-is-each-size), [resultados da comunidade](docs/COMMUNITY_BENCHMARKS.md).
+
+<p align="center"><a href="https://buymeacoffee.com/strataengine"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" height="50"></a><br>
+<sub>O Strata é gratuito. Se ele roda bem no seu PC, um café ajuda a manter o trabalho nele.</sub></p>
+
+## Do que você precisa
+
+| | |
+| --- | --- |
+| **Placa de vídeo** | **NVIDIA** GeForce RTX séries 20, 30, 40 ou 50, ou **AMD** Radeon RX 7900 XT / XTX, RX 7800 XT / 7700 XT, RX 9060 XT, RX 9070 / 9070 XT, Radeon AI PRO R9700 ou séries RX 6800 / 6900. Ela precisa de **12 GB de VRAM ou mais**. |
+| **RAM** | 32 GB ou mais. Sua RAM decide [qual modelo](#qual-modelo-devo-escolher) cabe. Com 64 GB, roda qualquer tamanho. |
+| **Disco** | Cerca de 80 GB livres. Use um SSD se puder: a primeira inicialização fica bem mais rápida. |
+| **Sistema** | Windows 10 / 11 ou Linux, e um driver de vídeo atualizado da NVIDIA ou da AMD. |
+
+O instalador cuida de todo o resto. Duas ou três placas podem dividir o modelo ([multi-GPU](docs/MULTI_GPU.md)).
+
+Experimental, escrito e testado por membros da comunidade nas próprias máquinas:
+
+- **Placas de vídeo mais antigas** (Tesla P40 / V100, GTX 10, Radeon VII / MI50, RX 6700 XT, RX 5500 XT): [Older GPUs](docs/OLDER_GPUS.md).
+- **Intel Arc**, compilado a partir do código-fonte no Linux: [Intel Arc](docs/INTEL_ARC.md).
+- **AMD Ryzen AI Max (Strix Halo)**, compilado a partir do código-fonte no Linux: [Strix Halo](docs/STRIX_HALO.md).
+- **Processadores mais antigos sem AVX2**: funcionam, mas devagar. [Older CPUs](docs/INSTALL.md#older-cpus-experimental).
+
+A lista completa: [docs/INSTALL.md](docs/INSTALL.md#what-you-need).
+
+## Instalação
+
+### Deixe sua IA instalar
+
+Você usa um assistente de programação com IA (Claude Code, Cursor, Codex, GitHub Copilot, ...)? Cole isto nele:
+
+```text
+Set up Strata on this PC for me: https://github.com/Niko1221/Strata - follow docs/AI_SETUP.md in that repository.
+```
+
+Ele verifica sua placa de vídeo, RAM e disco e escolhe o modelo que cabe. Depois instala, inicia e explica como
+conectar seus apps. Ferramentas de IA também podem instalar, iniciar e parar o Strata pelo
+[servidor MCP](docs/MCP_SERVER.md) dele.
+
+### Ou faça você mesmo
+
+[Baixe o Strata](https://github.com/Niko1221/Strata/archive/refs/heads/main.zip) e descompacte (ou use `git clone`).
+**Windows:** clique duas vezes em **`START-HERE.bat`**. **Linux:** rode **`./setup.sh`** na pasta do Strata.
+
+Os passos são os mesmos para NVIDIA e AMD. O instalador encontra sua placa e configura o engine certo para ela.
+Ele faz algumas perguntas:
+
+- qual modelo e qual tamanho,
+- quanto contexto (quanto texto o modelo guarda na memória),
+- se ele deve entender imagens.
+
+Aperte Enter em cada uma para usar a resposta recomendada. Depois ele baixa o modelo (cerca de 70 GB) e o inicia.
+Se o download parar, rode de novo: ele continua de onde parou. Seu navegador abre o app do Strata em
+`http://127.0.0.1:8080`.
+
+> **Enquanto o modelo inicia, seu PC pode ficar lento ou parar de responder por 1-3 minutos** (mais na primeira vez).
+> O Strata carrega 35-55 GB na sua RAM e reserva parte dela para a placa de vídeo. Isso é normal. Espere e não
+> feche a janela. A janela mostra o que o Strata está fazendo.
+
+**Da próxima vez**, rode `START-HERE.bat` (ou `./setup.sh`) de novo. Ele inicia na hora e não baixa nada duas
+vezes. Feche a janela para parar o modelo. `UPDATE.bat` (`./update.sh`) atualiza o Strata sem iniciá-lo.
+Atualização, Docker, várias placas, onde ficam os arquivos e todas as opções: [docs/INSTALL.md](docs/INSTALL.md).
+
+## Qual modelo devo escolher?
+
+O instalador recomenda um de acordo com sua RAM. O mesmo modelo vem em vários tamanhos, mais ou menos comprimidos.
+Os tamanhos menores são mais rápidos. Os maiores são um pouco mais inteligentes.
+
+| Sua RAM | Escolha | Por quê |
+| --- | --- | --- |
+| **32 GB** | **Coder** | cabe em 32 GB e é feito para código (com uma placa de 24 GB, Q2_0 e IQ2_XS também rodam) |
+| **48 GB** | **IQ2_XS** (ou Q2_0, o mais rápido) | os tamanhos maiores não cabem |
+| **64 GB** | **IQ2_XS** (recomendado), ou IQ3_XXS / IQ3_S | todos os tamanhos cabem; IQ3_S é o melhor e o mais lento |
+| **96 GB ou mais** | **IQ3_S**, ou o UD-IQ4_XS da Unsloth (~4-bit) | espaço para os maiores tamanhos com todo o resto aberto |
+
+- **[Coder](docs/MODELS.md#coder):** uma versão para programação com metade dos experts removida. Ela alcança 91%
+  da pontuação do modelo completo no SWE-bench Verified (medido pelos autores) e cabe em 32 GB de RAM. É mais fraca
+  fora do código, inclusive em chinês e outros textos CJK (#438). Para isso, escolha Q2_0, IQ2_XS ou IQ3_S, que
+  mantêm todos os experts.
+- **[Swift 1.5](docs/MODELS.md#swift-15):** um fine-tune que pensa por bem menos tempo antes de responder. Você
+  recebe a resposta mais cedo, com quase a mesma qualidade.
+- **[Unsloth UD-IQ4_XS](docs/MODELS.md#unsloth-ud-iq4_xs):** a versão de ~4 bits da Unsloth, entre IQ3_S e
+  UD-Q4_K_XL em qualidade. Um download de 94 GB. Com menos de ~80 GB de RAM, o Strata lê uma parte dele do SSD
+  enquanto responde, então ali ele fica mais lento (um SSD NVMe ajuda).
+- **[Unsloth UD-Q4_K_XL](docs/MODELS.md#unsloth-ud-q4_k_xl-experimental)** (experimental): o mais próximo do modelo
+  completo. Mas o Strata lê a maior parte dele do SSD enquanto responde, então ele escreve só 7-8.5 tokens/s num PC
+  com 64 GB.
+- **[OrcaRouter's Uncensored IQ3_XXS](docs/MODELS.md#orcarouter-uncensored-iq3_xxs):** você configura à mão. Ele
+  não está no menu do instalador.
+
+Tamanhos, downloads e o que cabe onde: [docs/MODELS.md](docs/MODELS.md). Para adicionar outro modelo depois, rode
+`SETUP.bat` (Linux: `./setup.sh --setup`).
+
+## Como usar
+
+<p align="center"><img src="docs/media/runpagoda.png" width="900" alt="A aba Monitor do app Strata ao lado de um agente de programação"><br>
+<sub>O <b>Monitor</b> do app Strata (à esquerda) enquanto um agente de programação escreve o jardim com pagode do vídeo (à direita)</sub></p>
+
+- **No navegador:** abra `http://127.0.0.1:8080`. Lá tem o **Chat**, um **Monitor** ao vivo do modelo e da sua
+  GPU/CPU/RAM, e o **About** com as configurações e os endereços.
+- **Seus apps e agentes de programação:** adicione um provedor "OpenAI-compatible" com a URL base
+  **`http://127.0.0.1:8080/v1`**. Qualquer chave de API e qualquer nome de modelo funcionam.
+  - Apps que usam a API da Anthropic: `http://127.0.0.1:8080/v1/messages` (Claude Code:
+    `ANTHROPIC_BASE_URL=http://127.0.0.1:8080`).
+  - Codex CLI e outros apps que usam a OpenAI Responses API: `/v1/responses`
+    ([configuração](docs/DETAILS.md#the-responses-api-and-codex-cli)).
+- **Raciocínio:** escolha **off, low, medium ou high** no menu do chat ou no "reasoning effort" do seu app. Off é o
+  mais rápido. High é o melhor para perguntas difíceis.
+- **Imagens:** responda sim para "Images?" na instalação. Depois clique em **Picture** no chat, ou anexe imagens no
+  seu app. Placas AMD entendem imagens no Linux usando o processador; no Windows, ainda não.
+- **Do seu celular ou de outro PC:** `START-HERE.bat --setup --host 0.0.0.0 --api-key <secret>`. Sempre defina uma chave.
+- **Uma requisição por vez:** por padrão, o Strata responde uma requisição e as outras esperam. Para responder
+  várias ao mesmo tempo, defina `"parallel": 2` ([BATCHING.md](docs/BATCHING.md)). Numa placa de 12 GB, isso deixa
+  cada resposta mais lenta.
+- **Prompts longos:** o Strata lê a primeira mensagem de uma conversa inteira, cerca de 1 minuto a cada 30,000
+  tokens. As mensagens seguintes começam em segundos.
+
+Mais: [onde suas conversas ficam salvas](docs/INSTALL.md#where-things-are-stored), [a API](docs/DETAILS.md#using-it).
+
+## Algo deu errado?
+
+- **Meu PC travou na primeira vez que o Strata iniciou.** Isso é normal enquanto ele carrega o modelo. Espere e não
+  feche a janela. Ainda travado depois de 10 minutos? Reinicie o PC, feche outros programas e tente de novo, ou
+  escolha um tamanho menor.
+- **Parou durante o download ou a instalação.** Rode `START-HERE.bat` (ou `./setup.sh`) de novo. Ele continua de
+  onde parou.
+- **Está muito lento e a luz do disco não para de piscar, ou aparece "the engine stopped unexpectedly".** Seu PC
+  não tem RAM livre suficiente. Feche outros programas (navegadores usam muita), ou escolha um tamanho menor (Q2_0
+  ou IQ2_XS).
+- **Diz que a porta 8080 já está em uso.** O Strata já está rodando. Procure a janela dele.
+
+Mais problemas e como resolver: [docs/TROUBLESHOOTING.md](docs/TROUBLESHOOTING.md). Ainda sem solução? Abra uma
+[issue](https://github.com/Niko1221/Strata/issues) e anexe o `strata-<model>.log` da pasta do Strata. Encontrou um
+problema de segurança? Informe em particular: [SECURITY.md](SECURITY.md).
+
+## Como funciona?
+
+Modelos como este normalmente rodam em servidores com centenas de gigabytes de memória de vídeo. Sua placa de vídeo
+tem 12-24 GB. O Strata faz o modelo caber **dividindo o trabalho pelo PC inteiro**. Pense numa cozinha: o que você
+usa o tempo todo fica na bancada, e o resto espera na despensa.
+
+<p align="center"><img src="docs/media/how-it-works.svg" width="860" alt="Os 24,576 experts do modelo: os mais usados na placa de vídeo, todos na RAM, uma tabela de consulta no SSD"></p>
+
+- **O modelo é uma equipe de 24,576 pequenos especialistas ("experts").** Cada palavra precisa de só 10 deles.
+- **Sua placa de vídeo** guarda os poucos milhares de experts usados com mais frequência. **Sua RAM** guarda todos
+  eles, e **seu processador** trabalha no resto ao mesmo tempo. **Seu SSD** guarda uma grande tabela de consulta.
+
+<p align="center"><img src="docs/media/guess-and-check.svg" width="860" alt="Um pequeno ajudante adivinha as próximas palavras; o modelo grande confere todas de uma vez e fica com as certas"></p>
+
+- **Adivinhar e conferir:** um pequeno ajudante adivinha as próximas palavras. O modelo grande confere todas de
+  uma vez. Você recebe a mesma resposta, 1.6-1.8x mais cedo.
+- **Textos longos são lidos em grandes pedaços** (até 8,192 tokens por vez), a mais de 1,000 tokens por segundo.
+
+A explicação mais longa: [docs/HOW_IT_WORKS.md](docs/HOW_IT_WORKS.md). Cada parte e seus números:
+[os detalhes](docs/DETAILS.md#how-it-works) e o [artigo](docs/paper/Strata-Paper.pdf).
+
+## Créditos e licença
+
+O modelo é o [Qwen3.8-Flash-Next](https://huggingface.co/Qwen/Qwen3.8-Flash-Next), da equipe Qwen. Ele foi
+comprimido por [ISTA-DASLab](https://huggingface.co/ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF), UkisAI (Swift 1.5)
+e Unsloth. O Strata usa partes do [llama.cpp / ggml](https://github.com/ggml-org/llama.cpp). Todos os créditos:
+[docs/HOW_IT_WORKS.md](docs/HOW_IT_WORKS.md#credits). O Strata é de código aberto sob a [MIT License](LICENSE).
+Algumas partes e todos os modelos têm licenças próprias ([quais](docs/HOW_IT_WORKS.md#license)).
+
+## Apoie o Strata
+
+O Strata é gratuito e de código aberto. Se ele é útil para você, você pode apoiar o desenvolvimento:
+
+<p align="center"><a href="https://buymeacoffee.com/strataengine"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" height="50"></a></p>

README.zh-CN.md

@@ -0,0 +1,200 @@
+<h1 align="center">Strata</h1>
+
+[English](README.md) · **简体中文** · [日本語](README.ja.md) · [Deutsch](README.de.md) · [Français](README.fr.md) · [Español](README.es.md) · [Português](README.pt-BR.md)
+
+<p align="center"><b>在你自己的游戏电脑上运行 1250 亿参数的 AI 模型</b><br>
+NVIDIA 或 AMD 显卡（12 GB 及以上）· Windows 或 Linux · 免费开源</p>
+
+<p align="center"><a href="https://github.com/Niko1221/Strata/releases/download/v0.1.10/Pagoda.mp4"><img src="docs/media/pagoda-preview.webp" width="720" alt="Strata 的模型写出的体素宝塔花园，在浏览器中运行"></a><br>
+<sub>体素宝塔花园，一次提示生成，在 RTX 5070 上用 Strata 运行（IQ3_S，128K 上下文）·
+<a href="https://github.com/Niko1221/Strata/releases/download/v0.1.10/Pagoda.mp4">完整视频（49 秒）</a></sub></p>
+
+Strata 能在普通电脑上运行 **[Qwen3.8-Flash-Next](https://huggingface.co/Qwen/Qwen3.8-Flash-Next)**。这是一个又大又聪明的
+AI 模型，通常要用服务器才能跑。它能聊天、写代码、看图片，还能配合你的应用和编程智能体一起工作。所有数据都留在你的电脑上。
+
+## 速度有多快？
+
+我们在两台普通的游戏电脑上做了测试。一个 token 大约相当于 ¾ 个英文单词。
+
+- **写回答：** 短对话中回复出现的速度。每秒 60 个 token 已经比你的阅读速度还快。
+- **读提示：** 读入你发送内容的速度（这里是一份 32K token 的文档、代码或聊天记录）。
+
+<table>
+<tr><th>NVIDIA：RTX 5070（12 GB）、Ryzen 5 7600、64 GB 内存</th><th>AMD：RX 9070 XT（16 GB）、Ryzen 9 3900X、47 GB 内存</th></tr>
+<tr><td>
+
+| 规格 | 写回答 | 读提示 |
+| --- | ---: | ---: |
+| **Q2_0** | 94 tokens/s | 2,650 tokens/s |
+| **IQ2_XS** | 79 tokens/s | 2,090 tokens/s |
+| **IQ3_XXS** | 62 tokens/s | 1,750 tokens/s |
+| **IQ3_S** | 53 tokens/s | 1,620 tokens/s |
+| **Coder** | 55 tokens/s | 2,180 tokens/s |
+
+</td><td>
+
+| 规格 | 写回答 | 读提示 |
+| --- | ---: | ---: |
+| **Q2_0** | 60 tokens/s | 1,160 tokens/s |
+| **IQ2_XS** | 52 tokens/s | 1,110 tokens/s |
+| **Coder** | 44 tokens/s | 1,420 tokens/s |
+
+</td></tr>
+</table>
+
+NVIDIA：Q2_0 用的是引擎 0.1.36，其他各行用的是 0.1.26（4K 回答，32K 提示）。完整表格见
+[DETAILS.md](docs/DETAILS.md#speed-measured)。显存越大的显卡越快：RTX 3090（24 GB）写回答应该能达到每秒约
+100-140 个 token。长对话和其他显卡的数据：[各模型的速度](docs/MODELS.md#how-fast-is-each-size)、
+[社区测试结果](docs/COMMUNITY_BENCHMARKS.md)。
+
+<p align="center"><a href="https://buymeacoffee.com/strataengine"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="请我喝杯咖啡" height="50"></a><br>
+<sub>Strata 是免费的。如果它在你的电脑上跑得不错，请我喝杯咖啡，让这个项目继续下去。</sub></p>
+
+## 你需要什么
+
+| | |
+| --- | --- |
+| **显卡** | **NVIDIA** GeForce RTX 20、30、40 或 50 系列，或 **AMD** Radeon RX 7900 XT / XTX、RX 7800 XT / 7700 XT、RX 9060 XT、RX 9070 / 9070 XT、Radeon AI PRO R9700 或 RX 6800 / 6900 系列。需要 **12 GB 或以上显存**。 |
+| **内存** | 32 GB 或以上。内存大小决定能装下[哪个模型](#该选哪个模型)。64 GB 可以运行所有规格。 |
+| **硬盘** | 约 80 GB 可用空间。尽量用 SSD：第一次启动会快很多。 |
+| **系统** | Windows 10 / 11 或 Linux，以及 NVIDIA 或 AMD 的最新显卡驱动。 |
+
+其他的都由安装程序搞定。两到三张显卡可以分担同一个模型（[多 GPU](docs/MULTI_GPU.md)）。
+
+实验性支持，由社区成员在自己的机器上编写和测试：
+
+- **较老的显卡**（Tesla P40 / V100、GTX 10、Radeon VII / MI50、RX 6700 XT、RX 5500 XT）：[较老的 GPU](docs/OLDER_GPUS.md)。
+- **Intel Arc**，在 Linux 上从源码构建：[Intel Arc](docs/INTEL_ARC.md)。
+- **AMD Ryzen AI Max（Strix Halo）**，在 Linux 上从源码构建：[Strix Halo](docs/STRIX_HALO.md)。
+- **不支持 AVX2 的老处理器**：能用，但很慢。[较老的 CPU](docs/INSTALL.md#older-cpus-experimental)。
+
+完整列表：[docs/INSTALL.md](docs/INSTALL.md#what-you-need)。
+
+## 安装
+
+### 让你的 AI 帮你装
+
+你在用 AI 编程助手吗（Claude Code、Cursor、Codex、GitHub Copilot 等）？把下面这段粘贴给它：
+
+```text
+Set up Strata on this PC for me: https://github.com/Niko1221/Strata - follow docs/AI_SETUP.md in that repository.
+```
+
+它会检查你的显卡、内存和硬盘，选出合适的模型。然后安装并启动它，再告诉你怎么连接你的应用。AI 工具也可以通过
+Strata 的 [MCP 服务器](docs/MCP_SERVER.md)来安装、启动和停止 Strata。
+
+### 或者自己动手
+
+[下载 Strata](https://github.com/Niko1221/Strata/archive/refs/heads/main.zip) 并解压（或者用 `git clone`）。
+**Windows：** 双击 **`START-HERE.bat`**。**Linux：** 在 Strata 文件夹里运行 **`./setup.sh`**。
+
+NVIDIA 和 AMD 的步骤完全一样。安装程序会识别你的显卡，并装好对应的引擎。它会问你几个问题：
+
+- 用哪个模型、哪个规格，
+- 上下文多大（模型能记住多少文字），
+- 是否要识别图片。
+
+每次直接按回车就是推荐选项。之后它会下载模型（约 70 GB）并启动。如果下载中断了，再运行一次即可：它会从中断的地方继续。
+浏览器会打开 Strata 应用，地址是 `http://127.0.0.1:8080`。
+
+> **模型启动时，你的电脑可能会变慢或卡住 1-3 分钟**（第一次最久）。
+> Strata 会把 35-55 GB 加载到内存，并为显卡锁定其中一部分。这是正常的。请耐心等待，不要关闭窗口。
+> 窗口里会显示 Strata 正在做什么。
+
+**下次使用时**，再运行一次 `START-HERE.bat`（或 `./setup.sh`）。它会马上启动，已下载的东西不会重复下载。关闭它的窗口就能停止模型。
+`UPDATE.bat`（`./update.sh`）只更新 Strata，不启动。更新、Docker、多张显卡、文件存放位置以及所有选项：
+[docs/INSTALL.md](docs/INSTALL.md)。
+
+## 该选哪个模型
+
+安装程序会根据你的内存推荐一个。同一个模型有几种规格，压缩程度不同。规格越小越快，越大越聪明一些。
+
+| 你的内存 | 选择 | 原因 |
+| --- | --- | --- |
+| **32 GB** | **Coder** | 32 GB 装得下，而且专为代码打造（如果显卡是 24 GB，Q2_0 和 IQ2_XS 也能跑） |
+| **48 GB** | **IQ2_XS**（或 Q2_0，最快） | 更大的规格装不下 |
+| **64 GB** | **IQ2_XS**（推荐），或 IQ3_XXS / IQ3_S | 所有规格都装得下；IQ3_S 最好，也最慢 |
+| **96 GB 或以上** | **IQ3_S**，或 Unsloth 的 UD-IQ4_XS（约 4-bit） | 开着其他程序也能放下最大的规格 |
+
+- **[Coder](docs/MODELS.md#coder)：** 编程版本，去掉了一半专家。它达到完整模型 SWE-bench Verified 分数的 91%（由其作者测得），
+  32 GB 内存就能装下。代码以外的能力较弱，包括中文和其他中日韩文本（#438）。这类用途请选 Q2_0、IQ2_XS 或 IQ3_S，
+  它们保留了所有专家。
+- **[Swift 1.5](docs/MODELS.md#swift-15)：** 一个微调版本，回答前思考的时间短得多。你能更早拿到答案，质量基本不变。
+- **[Unsloth UD-IQ4_XS](docs/MODELS.md#unsloth-ud-iq4_xs)：** Unsloth 的约 4-bit 版本，质量介于 IQ3_S 和 UD-Q4_K_XL 之间。
+  下载 94 GB。内存少于约 80 GB 时，Strata 回答时要从 SSD 读取其中一部分，所以会更慢（NVMe SSD 有帮助）。
+- **[Unsloth UD-Q4_K_XL](docs/MODELS.md#unsloth-ud-q4_k_xl-experimental)**（实验性）：最接近完整模型。但 Strata
+  回答时要从 SSD 读取其中大部分内容，所以在 64 GB 的电脑上每秒只能写 7-8.5 个 token。
+- **[OrcaRouter 的 Uncensored IQ3_XXS](docs/MODELS.md#orcarouter-uncensored-iq3_xxs)：** 需要手动设置，不在安装程序的菜单里。
+
+规格、下载以及各配置能装下什么：[docs/MODELS.md](docs/MODELS.md)。以后想再添加模型，运行
+`SETUP.bat`（Linux：`./setup.sh --setup`）。
+
+## 使用方法
+
+<p align="center"><img src="docs/media/runpagoda.png" width="900" alt="Strata 应用的 Monitor 标签页，旁边是一个编程智能体"><br>
+<sub>Strata 应用的 <b>Monitor</b>（左），编程智能体正在写视频里的宝塔花园（右）</sub></p>
+
+- **在浏览器里：** 打开 `http://127.0.0.1:8080`。里面有 **Chat**（聊天）、实时 **Monitor**（监控模型和你的
+  GPU/CPU/内存），以及 **About**（设置和地址）。
+- **你的应用和编程智能体：** 添加一个“OpenAI 兼容”的提供商，base URL 填 **`http://127.0.0.1:8080/v1`**。
+  API key 和模型名随便填都行。
+  - 使用 Anthropic API 的应用：`http://127.0.0.1:8080/v1/messages`（Claude Code：
+    `ANTHROPIC_BASE_URL=http://127.0.0.1:8080`）。
+  - Codex CLI 和其他使用 OpenAI Responses API 的应用：`/v1/responses`
+    （[设置方法](docs/DETAILS.md#the-responses-api-and-codex-cli)）。
+- **思考：** 在聊天菜单或应用的“reasoning effort”里选择 **off、low、medium 或 high**。off 最快，
+  high 最适合难题。
+- **图片：** 在安装时对“Images?”选是。然后在聊天里点 **Picture**，或在你的应用里附上图片。
+  AMD 显卡在 Linux 上通过处理器识别图片；在 Windows 上暂时还不行。
+- **从手机或另一台电脑访问：** `START-HERE.bat --setup --host 0.0.0.0 --api-key <secret>`。一定要设置 key。
+- **一次处理一个请求：** 默认情况下 Strata 一次只回答一个请求，其他请求排队等待。想同时回答多个，
+  设置 `"parallel": 2`（[BATCHING.md](docs/BATCHING.md)）。在 12 GB 显卡上，这会让每个回答变慢。
+- **长提示：** Strata 会完整读入对话的第一条消息，大约每 30,000 个 token 需要 1 分钟。之后的消息几秒内就开始回答。
+
+更多：[聊天记录存在哪里](docs/INSTALL.md#where-things-are-stored)、[API](docs/DETAILS.md#using-it)。
+
+## 出问题了？
+
+- **Strata 第一次启动时电脑卡死了。** 加载模型时这是正常的。请耐心等待，不要关闭窗口。
+  10 分钟后还卡着？重启电脑，关掉其他程序再试一次，或者换一个更小的规格。
+- **下载或安装时中断了。** 再运行一次 `START-HERE.bat`（或 `./setup.sh`）。它会从中断的地方继续。
+- **非常慢，硬盘灯一直闪，或者提示“the engine stopped unexpectedly”。** 你的电脑可用内存不够。
+  关掉其他程序（浏览器很占内存），或者换一个更小的规格（Q2_0 或 IQ2_XS）。
+- **提示 8080 端口已被占用。** Strata 已经在运行了。找一下它的窗口。
+
+更多问题和解决办法：[docs/TROUBLESHOOTING.md](docs/TROUBLESHOOTING.md)。还是解决不了？提交一个
+[issue](https://github.com/Niko1221/Strata/issues)，并附上 Strata 文件夹里的 `strata-<model>.log`。发现了安全问题？
+请私下报告：[SECURITY.md](SECURITY.md)。
+
+## 它是怎么工作的？
+
+这类模型通常运行在拥有数百 GB 显存的服务器上。而你的显卡只有 12-24 GB。Strata 让模型装得下的办法是
+**把工作分摊到整台电脑上**。可以想象一个厨房：常用的东西放在台面上，其余的放在储藏室里。
+
+<p align="center"><img src="docs/media/how-it-works.svg" width="860" alt="模型的 24,576 个专家：最常用的在显卡上，全部在内存里，一张查找表在 SSD 上"></p>
+
+- **这个模型是由 24,576 个小专家（“experts”）组成的团队。** 每个词只需要其中 10 个。
+- **你的显卡** 存放最常用的几千个专家。**你的内存** 存放全部专家，
+  **你的处理器** 同时处理其余的部分。**你的 SSD** 存放一张大查找表。
+
+<p align="center"><img src="docs/media/guess-and-check.svg" width="860" alt="一个小助手猜测接下来的几个词；大模型一次性全部检查，保留正确的"></p>
+
+- **先猜，再检查：** 一个小助手先猜接下来的几个词，大模型再一次性检查它们。答案完全一样，但快 1.6-1.8 倍。
+- **长文本分大块读入**（每次最多 8,192 个 token），速度超过每秒 1,000 个 token。
+
+更详细的解释：[docs/HOW_IT_WORKS.md](docs/HOW_IT_WORKS.md)。每个部分及其数据：
+[详细说明](docs/DETAILS.md#how-it-works)和[论文](docs/paper/Strata-Paper.pdf)。
+
+## 致谢与许可证
+
+模型是 Qwen 团队的 [Qwen3.8-Flash-Next](https://huggingface.co/Qwen/Qwen3.8-Flash-Next)。它由
+[ISTA-DASLab](https://huggingface.co/ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF)、UkisAI（Swift 1.5）
+和 Unsloth 压缩。Strata 使用了 [llama.cpp / ggml](https://github.com/ggml-org/llama.cpp) 的部分代码。完整致谢：
+[docs/HOW_IT_WORKS.md](docs/HOW_IT_WORKS.md#credits)。Strata 以 [MIT 许可证](LICENSE)开源。少数部分和每个模型
+有各自的许可证（[具体是哪些](docs/HOW_IT_WORKS.md#license)）。
+
+## 支持 Strata
+
+Strata 免费且开源。如果它对你有用，欢迎支持它的开发：
+
+<p align="center"><a href="https://buymeacoffee.com/strataengine"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="请我喝杯咖啡" height="50"></a></p>

SECURITY.md

@@ -0,0 +1,44 @@
+# Security
+
+## Reporting a vulnerability
+
+Please report anything that should not be public until it is fixed **privately**, through GitHub's private
+vulnerability reporting: the repository's **Security** tab, **Report a vulnerability**
+([direct link](https://github.com/Niko1221/Strata/security/advisories/new)). Say what you found, how to reproduce it
+(the request, the config keys involved, the Strata version), and what an attacker gains. We answer there, fix it in
+a release, and credit you in the advisory unless you ask us not to.
+
+Ordinary hardening ideas and findings that are safe to discuss in public are welcome as an
+[issue](https://github.com/Niko1221/Strata/issues) (#544 collects audit findings) or as a pull request, which we
+review like our own code.
+
+Supported: the latest release. Fixes go into the next release, not into older ones.
+
+## What the server exposes
+
+Strata runs a model on your PC and serves it over HTTP (`serve/server.py`). Out of the box it is reachable from
+this PC only. The details and every setting are in [docs/DETAILS.md](docs/DETAILS.md) ("From other devices",
+"Host names", "Web pages without an API key", "Tools from MCP servers").
+
+- **Where it listens.** `127.0.0.1` by default. `--host 0.0.0.0` (or `"host"` in `strata-<model>.json`) opens it to
+  your network, and the server then warns when no API key is set.
+- **API key.** `"api_key"` in the run config (or `STRATA_API_KEY`) is required on `/v1/*` and on every endpoint
+  that shows the model's state, requests or answers (`/status`, `/metrics`, `/settings`, `/mcp`, `/props`, `/slots`,
+  `/api/requests`, `/config`) and on every `POST`. It is compared in constant time. Set one before you open the
+  server to your network or put a tunnel in front of it.
+- **Host check (DNS rebinding, 0.1.38).** Without an API key the server answers only requests whose `Host` is a name
+  it knows (`localhost`, an IP address, the address it listens on and, when it listens beyond this PC, this PC's
+  name). Others get 403. `"allowed_hosts"` adds names; with an API key the key decides.
+- **Origin check (0.1.38).** Without an API key, a browser `POST` to `/v1/*` from another web site's page (it carries
+  an `Origin` header) gets 403 unless that origin is Strata's own page, `localhost`, an allowed host, or listed in
+  `"trusted_origins"` / `"cors_origins"`. Changing settings (`/settings`, and `/config`: the few run config keys the
+  page's Model settings may change - never the network, key, MCP or program keys), `/load` and `/unload` and the
+  MCP tools are accepted only as JSON from Strata's own page (or `"trusted_origins"`), so a page elsewhere cannot
+  change settings or run tools.
+- **CORS** is off unless `"cors_origins"` lists origins, and then only for `/v1/*`.
+- **MCP tools** are opt-in: only the servers you put in the run config, only for requests from Strata's own page
+  that ask for them. They run with your user's rights, and the model decides when to call them.
+- **The request monitor** (`/api-monitor`, which keeps the last prompts and answers in memory) is off unless
+  `"api_monitor": true` is set.
+
+Strata has not had an outside security audit yet.

START-HERE.bat

@@ -1,6 +1,6 @@
 @echo off
 rem Strata for Windows: the first run installs everything and starts the model; later runs just start it.
-rem Needs only an NVIDIA driver. Python is installed for your user account if it is missing (no admin needed).
+rem Needs only an NVIDIA or AMD graphics driver. Python is installed for your user account if it is missing (no admin needed).
 setlocal
 title Strata
 cd /d "%~dp0"

UPDATE.bat

@@ -0,0 +1,36 @@
+@echo off
+rem Update Strata without starting the model (#475): the newest code (git pull, when this folder is a git clone), then
+rem what START-HERE.bat does before a start - the engine (a new one when this version needs it), the Python packages,
+rem each installed model's settings and draft subset. The model files are not touched. Start the model later with
+rem START-HERE.bat. Options are passed on to setup.py.
+setlocal
+title Strata - update
+cd /d "%~dp0"
+rem All of it in one block: cmd reads a .bat file while it runs it, and the git pull can change this very file.
+(
+  if exist ".git" (
+    where git >nul 2>nul || (
+      echo  This folder is a git clone, but git is not on PATH: install Git for Windows ^(winget install Git.Git^)
+      echo  or run "git pull" here yourself, then run UPDATE.bat again.
+      pause
+      exit /b 1
+    )
+    echo  Getting the newest Strata ^(git pull^) ...
+    git pull --ff-only || (
+      echo.
+      echo  git pull did not succeed ^(the reason is above^): nothing was updated. Files you changed here can stop it:
+      echo  "git status" lists them.
+      pause
+      exit /b 1
+    )
+  ) else (
+    echo  This copy of Strata was not made with git, so it cannot fetch new files itself. Download the newest one:
+    echo    https://github.com/Niko1221/Strata/archive/refs/heads/main.zip
+    echo  unzip it anywhere and run START-HERE.bat ^(or UPDATE.bat^) in it: it finds the model files in Strata-data
+    echo  and sets itself up the same way - nothing big is downloaded again.
+    echo  Checking this copy's engine and settings meanwhile ...
+  )
+  call "%~dp0START-HERE.bat" --update %*
+  if not errorlevel 1 pause
+  exit /b
+)

bench/results/2026-09-28-rope-scaling/README.md

@@ -0,0 +1,49 @@
+# Rope scaling: needle recall past the trained 262K
+
+The feature (PR #84, experimental): `--rope-scaling none|linear|yarn` extends the context past the model's
+trained 262,144 positions by rescaling the rotary angles - llama.cpp's types, ggml's `rope_yarn`
+arithmetic, one process config applied at the cos/sin table (default path) and as kernel constants
+(`--native-rope`, the native indexer, prefill). The three tables here are the first end-to-end check
+that a scaled run actually recalls past the trained end.
+
+**Hardware:** RTX 4070 Ti SUPER 16 GB, Ryzen 9 5900X (no AVX-512 - the IQ3_XXS native pack runs its
+expert kernels on AVX-2), 125 GB RAM, Linux. **Engine:** this branch built from source (the log records
+`rope scaling ... --max-context ... against a trained context of 262144` at startup). The runs below
+were taken on the rope tree built over the 0.1.17-era base; the branch then rebased onto main (engine
+0.1.20) - the drift touches none of the rope files - and the rebase was re-validated: `rope_parity`
+green, a 64-token greedy run token-identical to the plain 0.1.20 build, and the yarn 262k probe
+repeated green (see the last table row). Baseline arm A also re-checked the default is the stock
+model: a 64-token greedy run produces the same token ids as the ready-made 0.1.17 engine, and
+`rope_parity` holds the `none` table bit-identical to the pre-scaling builder.
+**Method:** `tools/needle_bench.py` against a running server (launch recipes in `config-*.json`),
+`temperature 0`, thinking off, depth 50%, one probe per length.
+
+| Arm | Scaling | Context | Probe | Prompt tokens | Result | Probe time |
+| --- | --- | --- | --- | --- | --- | --- |
+| A | none | 262,144 | 1k @ 50% | 1,442 | FOUND | 4 s |
+| A | none | 262,144 | 32k @ 50% | 31,763 | FOUND | 27 s |
+| A | none | 262,144 | 128k @ 50% | 128,328 | FOUND | 86 s |
+| B | linear, factor 2 | 560,000 | 262k @ 50% | 274,222 | FOUND | 232 s |
+| B | linear, factor 2 | 560,000 | 512k @ 50% | 527,051 | FOUND | 619 s |
+| C | yarn, factor 2 | 560,000 | 262k @ 50% | 274,215 | FOUND | 236 s |
+| C | yarn, factor 2 | 560,000 | 512k @ 50% | 526,988 | FOUND | 615 s |
+| C | yarn, factor 2 | 560,000 | 262k @ 50% (the rebase re-check) | 273,708 | FOUND | 235 s |
+
+All 8 of 8 probes found the needle; JSON per arm: `needle-A-none.json`, `needle-B-linear2.json`,
+`needle-B-linear2-512k.json`, `needle-C-yarn2.json` (+ the rebase re-check in
+`needle-C-yarn2-rebased.json`); server and engine logs alongside.
+
+What this establishes, and what it does not:
+
+- **Past the trained end the model still reads.** 512k probes are 2.01x the trained 262,144 - position
+  interpolation (B) and YaRN (C) both hold a mid-context lookup there.
+- **Scaled runs within the trained range are a slightly different model.** `yarn`'s mscale applies at
+  every position (a 64-token greedy prompt diverges from the stock run after ~19 tokens); arm A is the
+  stock model, arms B/C are not, by design.
+- **One depth, one needle, one machine.** A single probe per (length, arm) at depth 50% says "recall
+  works there", not "no degradation anywhere" - a full depth sweep and a longer-length ladder (768K,
+  1M) on the bench machine (64 GB RAM there vs 125 GB here, so 262K IQ3_XXS fits either way) is the
+  natural follow-up. 512K prompts prefill at ~850-950 tokens/s on this card; the probe time is almost
+  entirely prefill.
+- **The `560,000` context** in arms B/C is headroom for the needle harness's tokenizer overshoot
+  (a "512k" probe tokenizes to ~527k); 524,288 exactly would refuse the probe.

bench/results/2026-09-28-rope-scaling/config-A-none-262144.json

@@ -0,0 +1,38 @@
+{
+ "exe": "/home/jakub/workspace/deepseek-harness/harnes_workspace/Strata/build/strata",
+ "cwd": "/home/jakub/workspace/deepseek-harness/harnes_workspace/Strata",
+ "tokenizer": "/home/jakub/workspace/deepseek-harness/harnes_workspace/Strata-data/packs/iq3_xxs/tokenizer",
+ "model_name": "qwen3.8-flash-next-iq3_xxs-rope-scaling",
+ "lib_dirs": [
+  "/usr/local/cuda/bin",
+  "/usr/local/cuda/lib64"
+ ],
+ "port": 8130,
+ "args": [
+  "--pack",
+  "/home/jakub/workspace/deepseek-harness/harnes_workspace/Strata-data/packs/iq3_xxs",
+  "--native",
+  "/home/jakub/workspace/deepseek-harness/harnes_workspace/Strata-data/models/IQ3_XXS/Qwen3.8-Flash-Next-GSQ-RCO-IQ3_XXS-00001-of-00002.gguf",
+  "--ple-gguf",
+  "/home/jakub/workspace/deepseek-harness/harnes_workspace/Strata-data/models/IQ3_XXS/Qwen3.8-Flash-Next-GSQ-RCO-IQ3_XXS-00002-of-00002.gguf",
+  "--expert-profile",
+  "/home/jakub/workspace/deepseek-harness/harnes_workspace/Strata/data/expert-profile.bin",
+  "--expert-cache",
+  "auto",
+  "--prefill",
+  "auto",
+  "--spec",
+  "4",
+  "--spec-min-p",
+  "0.5",
+  "--mtp",
+  "/home/jakub/workspace/deepseek-harness/harnes_workspace/Strata-data/mtp/rt",
+  "--kv",
+  "int8",
+  "--kv-resident",
+  "32768",
+  "--max-context",
+  "262144"
+ ],
+ "log": "/home/jakub/workspace/deepseek-harness/harnes_workspace/Strata/bench/results/2026-09-28-rope-scaling/engine-A-none-262144.log"
+}

bench/results/2026-09-28-rope-scaling/config-B-linear2.json

@@ -0,0 +1,42 @@
+{
+ "exe": "/home/jakub/workspace/deepseek-harness/harnes_workspace/Strata/build/strata",
+ "cwd": "/home/jakub/workspace/deepseek-harness/harnes_workspace/Strata",
+ "tokenizer": "/home/jakub/workspace/deepseek-harness/harnes_workspace/Strata-data/packs/iq3_xxs/tokenizer",
+ "model_name": "qwen3.8-flash-next-iq3_xxs-rope-scaling",
+ "lib_dirs": [
+  "/usr/local/cuda/bin",
+  "/usr/local/cuda/lib64"
+ ],
+ "port": 8130,
+ "args": [
+  "--pack",
+  "/home/jakub/workspace/deepseek-harness/harnes_workspace/Strata-data/packs/iq3_xxs",
+  "--native",
+  "/home/jakub/workspace/deepseek-harness/harnes_workspace/Strata-data/models/IQ3_XXS/Qwen3.8-Flash-Next-GSQ-RCO-IQ3_XXS-00001-of-00002.gguf",
+  "--ple-gguf",
+  "/home/jakub/workspace/deepseek-harness/harnes_workspace/Strata-data/models/IQ3_XXS/Qwen3.8-Flash-Next-GSQ-RCO-IQ3_XXS-00002-of-00002.gguf",
+  "--expert-profile",
+  "/home/jakub/workspace/deepseek-harness/harnes_workspace/Strata/data/expert-profile.bin",
+  "--expert-cache",
+  "auto",
+  "--prefill",
+  "auto",
+  "--spec",
+  "4",
+  "--spec-min-p",
+  "0.5",
+  "--mtp",
+  "/home/jakub/workspace/deepseek-harness/harnes_workspace/Strata-data/mtp/rt",
+  "--kv",
+  "int8",
+  "--kv-resident",
+  "32768",
+  "--max-context",
+  "560000",
+  "--rope-scaling",
+  "linear",
+  "--rope-scale",
+  "2"
+ ],
+ "log": "/home/jakub/workspace/deepseek-harness/harnes_workspace/Strata/bench/results/2026-09-28-rope-scaling/engine-B-linear2-524288.log"
+}

bench/results/2026-09-28-rope-scaling/config-C-yarn2.json

@@ -0,0 +1,42 @@
+{
+ "exe": "/home/jakub/workspace/deepseek-harness/harnes_workspace/Strata/build/strata",
+ "cwd": "/home/jakub/workspace/deepseek-harness/harnes_workspace/Strata",
+ "tokenizer": "/home/jakub/workspace/deepseek-harness/harnes_workspace/Strata-data/packs/iq3_xxs/tokenizer",
+ "model_name": "qwen3.8-flash-next-iq3_xxs-rope-scaling",
+ "lib_dirs": [
+  "/usr/local/cuda/bin",
+  "/usr/local/cuda/lib64"
+ ],
+ "port": 8130,
+ "args": [
+  "--pack",
+  "/home/jakub/workspace/deepseek-harness/harnes_workspace/Strata-data/packs/iq3_xxs",
+  "--native",
+  "/home/jakub/workspace/deepseek-harness/harnes_workspace/Strata-data/models/IQ3_XXS/Qwen3.8-Flash-Next-GSQ-RCO-IQ3_XXS-00001-of-00002.gguf",
+  "--ple-gguf",
+  "/home/jakub/workspace/deepseek-harness/harnes_workspace/Strata-data/models/IQ3_XXS/Qwen3.8-Flash-Next-GSQ-RCO-IQ3_XXS-00002-of-00002.gguf",
+  "--expert-profile",
+  "/home/jakub/workspace/deepseek-harness/harnes_workspace/Strata/data/expert-profile.bin",
+  "--expert-cache",
+  "auto",
+  "--prefill",
+  "auto",
+  "--spec",
+  "4",
+  "--spec-min-p",
+  "0.5",
+  "--mtp",
+  "/home/jakub/workspace/deepseek-harness/harnes_workspace/Strata-data/mtp/rt",
+  "--kv",
+  "int8",
+  "--kv-resident",
+  "32768",
+  "--max-context",
+  "560000",
+  "--rope-scaling",
+  "yarn",
+  "--rope-scale",
+  "2"
+ ],
+ "log": "/home/jakub/workspace/deepseek-harness/harnes_workspace/Strata/bench/results/2026-09-28-rope-scaling/engine-C-yarn2-524288.log"
+}

bench/results/2026-09-28-rope-scaling/kv-sweep-tgs.json

@@ -0,0 +1,121 @@
+[
+ {
+  "length": "128k",
+  "round": 0,
+  "kind": "decode",
+  "prompt_tok": 128401,
+  "read": 52,
+  "gen": 357,
+  "read_tok_s": 79.2,
+  "gen_tok_s": 56.3,
+  "drafts_ok": 182,
+  "drafts_all": 305,
+  "seg_blocks": 3284340,
+  "seg_mib": 370.0,
+  "seg_misses": 91744,
+  "seg_hit_pct": 97.207,
+  "kib_per_token": 1061.29
+ },
+ {
+  "length": "128k",
+  "round": 1,
+  "kind": "read",
+  "prompt_tok": 128357,
+  "read": 128357,
+  "gen": 24,
+  "read_tok_s": 1389.2,
+  "gen_tok_s": 41.7,
+  "drafts_ok": 11,
+  "drafts_all": 23,
+  "seg_blocks": -3253560,
+  "seg_mib": -391.7,
+  "seg_misses": -97147,
+  "seg_hit_pct": 97.014,
+  "kib_per_token": -3.12
+ },
+ {
+  "length": "128k",
+  "round": 1,
+  "kind": "decode",
+  "prompt_tok": 128402,
+  "read": 52,
+  "gen": 388,
+  "read_tok_s": 76.5,
+  "gen_tok_s": 54.6,
+  "drafts_ok": 195,
+  "drafts_all": 363,
+  "seg_blocks": 3752616,
+  "seg_mib": 463.5,
+  "seg_misses": 114911,
+  "seg_hit_pct": 96.938,
+  "kib_per_token": 1223.26
+ },
+ {
+  "length": "512k",
+  "round": 0,
+  "kind": "read",
+  "prompt_tok": 522983,
+  "read": 522983,
+  "gen": 24,
+  "read_tok_s": 868.4,
+  "gen_tok_s": 42.4,
+  "drafts_ok": 13,
+  "drafts_all": 16,
+  "seg_blocks": -3808056,
+  "seg_mib": -387.8,
+  "seg_misses": -96113,
+  "seg_hit_pct": 97.476,
+  "kib_per_token": -0.76
+ },
+ {
+  "length": "512k",
+  "round": 0,
+  "kind": "decode",
+  "prompt_tok": 523028,
+  "read": 52,
+  "gen": 402,
+  "read_tok_s": 63.3,
+  "gen_tok_s": 48.4,
+  "drafts_ok": 221,
+  "drafts_all": 348,
+  "seg_blocks": 3580056,
+  "seg_mib": 920.4,
+  "seg_misses": 228658,
+  "seg_hit_pct": 93.613,
+  "kib_per_token": 2344.5
+ },
+ {
+  "length": "512k",
+  "round": 1,
+  "kind": "read",
+  "prompt_tok": 522984,
+  "read": 522984,
+  "gen": 24,
+  "read_tok_s": 857.1,
+  "gen_tok_s": 35.5,
+  "drafts_ok": 9,
+  "drafts_all": 18,
+  "seg_blocks": -3555408,
+  "seg_mib": -905.8,
+  "seg_misses": -225016,
+  "seg_hit_pct": 93.671,
+  "kib_per_token": -1.77
+ },
+ {
+  "length": "512k",
+  "round": 1,
+  "kind": "decode",
+  "prompt_tok": 523029,
+  "read": 52,
+  "gen": 402,
+  "read_tok_s": 61.3,
+  "gen_tok_s": 42.8,
+  "drafts_ok": 192,
+  "drafts_all": 388,
+  "seg_blocks": 4005216,
+  "seg_mib": 1079.9,
+  "seg_misses": 268221,
+  "seg_hit_pct": 93.303,
+  "kib_per_token": 2750.79
+ }
+]

bench/results/2026-09-28-rope-scaling/needle-A-none.json

@@ -0,0 +1,29 @@
+[
+ {
+  "length": "1k",
+  "depth": 50,
+  "prompt_tokens": 1442,
+  "seconds": 3.7,
+  "found": true,
+  "word": "meadow-copper-504",
+  "answer": "meadow-copper-504"
+ },
+ {
+  "length": "32k",
+  "depth": 50,
+  "prompt_tokens": 31763,
+  "seconds": 27.5,
+  "found": true,
+  "word": "falcon-quartz-940",
+  "answer": "falcon-quartz-940"
+ },
+ {
+  "length": "128k",
+  "depth": 50,
+  "prompt_tokens": 128328,
+  "seconds": 86.1,
+  "found": true,
+  "word": "willow-cobalt-696",
+  "answer": "willow-cobalt-696"
+ }
+]

bench/results/2026-09-28-rope-scaling/needle-B-linear2-512k.json

@@ -0,0 +1,11 @@
+[
+ {
+  "length": "512k",
+  "depth": 50,
+  "prompt_tokens": 527051,
+  "seconds": 619.0,
+  "found": true,
+  "word": "meadow-copper-504",
+  "answer": "meadow-copper-504"
+ }
+]

bench/results/2026-09-28-rope-scaling/needle-B-linear2.json

@@ -0,0 +1,16 @@
+[
+ {
+  "length": "262k",
+  "depth": 50,
+  "prompt_tokens": 274222,
+  "seconds": 232.2,
+  "found": true,
+  "word": "meadow-copper-504",
+  "answer": "meadow-copper-504"
+ },
+ {
+  "length": "512k",
+  "depth": 50,
+  "error": "HTTP Error 400: Bad Request"
+ }
+]

bench/results/2026-09-28-rope-scaling/needle-C-yarn2-rebased.json

@@ -0,0 +1,11 @@
+[
+ {
+  "length": "262k",
+  "depth": 50,
+  "prompt_tokens": 273708,
+  "seconds": 234.8,
+  "found": true,
+  "word": "meadow-copper-504",
+  "answer": "meadow-copper-504"
+ }
+]

bench/results/2026-09-28-rope-scaling/needle-C-yarn2.json

@@ -0,0 +1,20 @@
+[
+ {
+  "length": "262k",
+  "depth": 50,
+  "prompt_tokens": 274215,
+  "seconds": 235.5,
+  "found": true,
+  "word": "meadow-copper-504",
+  "answer": "meadow-copper-504"
+ },
+ {
+  "length": "512k",
+  "depth": 50,
+  "prompt_tokens": 526988,
+  "seconds": 615.0,
+  "found": true,
+  "word": "falcon-quartz-940",
+  "answer": "falcon-quartz-940"
+ }
+]

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

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+{
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+ "run": 1,
+ "run_id": "6c1252339f83",
+ "corpus_repeated": false,
+ "time": "2026-09-29T21:11:07",
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+  "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.",
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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-Coder-1x3090/speed-32k-2.json

@@ -0,0 +1,491 @@
+{
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+  "strata serve: decode expert cache hit rate: 99.1% (16646 hits / 16791 lookups)"
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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-Coder-1x3090/speed-32k-3.json

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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

@@ -0,0 +1,383 @@
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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

@@ -0,0 +1,279 @@
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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-Coder-2x3090-K24/speed-32k-2.json

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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

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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

@@ -0,0 +1,261 @@
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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-Coder-2x3090-K28/speed-32k-2.json

@@ -0,0 +1,374 @@
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bench/results/2026-09-29-rtx3090-epyc-milan/data/native-v0.1.26-Coder-2x3090-K28/speed-32k-3.json

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

@@ -0,0 +1,608 @@
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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

@@ -0,0 +1,750 @@
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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

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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/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

@@ -0,0 +1,707 @@
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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

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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/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-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

@@ -0,0 +1,829 @@
+{
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+  "strata serve: decode expert cache hit rate: 97.2% (145229 hits / 149472 lookups)"
+ ],
+ "answer_head": "The `CMakeLists.txt` file defines the top-level build configuration for the **Strata Engine** project. Its primary responsibilities include:\n\n1.  **Project Definition**: Sets the project name (`strata`), version (`0.1.26`), and language standard (C++20).\n2.  **Target Management**: Conditionally adds",
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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-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

@@ -0,0 +1,829 @@
+{
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+ "answer_head": "I'll start by exploring the repository structure to understand the context better.",
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+}

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

@@ -0,0 +1,829 @@
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+  "strata serve: decode expert cache hit rate: 96.0% (144002 hits / 149996 lookups)"
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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

@@ -0,0 +1,501 @@
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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

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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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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

@@ -0,0 +1,721 @@
+{
+ "label": "native-v0.1.26-IQ3_XXS-2x3090-auto",
+ "mode": "speed",
+ "ctx": "32k",
+ "target_tokens": 31488,
+ "run": 3,
+ "run_id": "60c6cbe1b14a",
+ "corpus_repeated": false,
+ "time": "2026-09-29T21:40:52",
+ "prompt_tokens": 31603,
+ "completion_tokens": 256,
+ "ttft_s": 27.476,
+ "e2e_s": 30.102,
+ "api_prompt_tps": 1150.2,
+ "api_decode_tps": 97.1,
+ "engine_prompt_tps": 1158.9,
+ "engine_decode_tps": 96.9,
+ "log_lines": [
+  "strata serve: prompt 31603 tokens = 0 reused + 31603 read in 27271 ms (1158.9 tok/s), 256 generated in 2641 ms (96.9 tok/s), drafts accepted 154 of 209, 2 checkpoints",
+  "strata serve: decode expert cache hit rate: 99.6% (148622 hits / 149275 lookups)"
+ ],
+ "answer_head": "An analysis of the last file (`docs/DETAILS.md`) and a proposed improvement follows.\n\n### What the file does\n\n`docs/DETAILS.md` serves as the comprehensive technical manual for the Strata engine. It bridges the gap between the high-level marketing in the `README.md` and the raw source code. Specific",
+ "metrics_after": {
+  "engine": {
+   "model": "qwen3.8-flash-next-iq3_xxs",
+   "max_context": 32768,
+   "images": false,
+   "version": "0.1.26",
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+   "kv": "int8",
+   "kv_resident": 0,
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+   "cvec": 0,
+   "arena_mib": 40925,
+   "pool_workers": 19,
+   "pcie_frac": "0.55",
+   "spec_min_p": "0.50",
+   "engine": "0.1.26"
+  },
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+  },
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+   {
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+  "requests_kept": 3,
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+  "hardware": {
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+     "mem_total": 25769803776,
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+   "gpu_power_limit": 560.0,
+   "gpu_pcie_gen": 4,
+   "gpu_pcie_gen_max": 4,
+   "gpu_pcie_width": 16,
+   "gpu_pcie_rx_mb": 11.77734375,
+   "gpu_pcie_tx_mb": 120.830078125,
+   "cpu": 85.4,
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+   "disk_write_mb": 0.08616042102070966,
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+   "tok_s_mean": 95.05553044960695
+  },
+  "hardware_static": {
+   "gpu_name": "NVIDIA GeForce RTX 3090 + NVIDIA GeForce RTX 3090",
+   "gpu_count": 2,
+   "cpu_name": "AMD EPYC 7453 28-Core Processor",
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+ }
+}

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": {
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+      "total_mib": 32607.0,
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+      "power_w": 83.403
+    },
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+      "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",
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+        "verified": true
+      },
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+]

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)

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

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