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).
Follow 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)
offers the same steps as tools.
- How the engine works, every measured number, the API and all settings: docs/DETAILS.md and the paper.
- AMD (HIP) build and validation: docs/AMD_HIP.md; multi-GPU: docs/MULTI_GPU.md.
- Setup's own tests run without a GPU or downloads:
python tools/test_setup_<name>.py(for exampletools/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.
- Search the open issues and pull requests first, and add to a thread that already covers your point.
- Open an issue with the form that fits (bug report, feature request or question).
- One change per pull request. Say what it changes and what it leaves alone.
- A new feature is opt-in, and the default path stays byte-identical to the last release. Say how you checked.
- Build every backend a file touches (CUDA, HIP, SYCL) before asking for review.
- Change a default only where you measured it faster, and show the numbers with what they were measured on.
- A report from hardware the maintainers do not have is welcome. Follow docs/COMMUNITY_BENCHMARKS.md, compare against a same-day run of the build you are testing, and say what you did not test.
- Open test requests and the hardware that is wanted are listed in docs/TEST_REQUESTS.md.