Random AI notes for working with local models or playing around with random machine learning bits.
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ai-notes

Notes, scripts, and solutions for running local AI models on AMD hardware.

Covers optimization issues, build workarounds, and practical solutions discovered while building inference stacks from source on bleeding-edge silicon.

Contents

strix-halo/

Complete from-source build pipeline for the vLLM inference stack on AMD Strix Halo APUs (Zen 5 + RDNA 3.5, gfx1151). Compiles every component -- from the ROCm SDK to Python itself -- with aggressive optimization flags targeting the Strix Halo unified memory architecture.

  • 40-step build pipeline across 11 phases (TheRock ROCm → AOCL → Python → PyTorch + TorchVision → Triton → vLLM → Flash Attention → optimized wheels → Lemonade → backend smoke test + benchmarks → deferred AITER JIT pre-warm)
  • 55+ documented build fixes with root cause analysis (BUILD-FIXES.md)
  • Environment activation script with compiler flags for Zen 5 CPU + RDNA 3.5 GPU
  • Native AVX-512, Polly loop optimizer, AMD-specific -famd-opt tuning
  • Strict local toolchain policy: downstream optimized phases require TheRock amdclang, source-built CPython, local ROCm math/runtime libraries including hipBLASLt, hipSPARSELt, MIOpen, RCCL, rocBLAS/rocSPARSE, local TheRock libomp, and fail immediately on missing artifacts, /opt/rocm leakage, or system OpenMP fallback.
  • Builds 28+ optimized Python wheels, including Rust/native server hot paths such as tokenizers, safetensors, pydantic-core, uvloop, httptools, msgspec, aiohttp, and watchfiles.
  • Current full-stack smoke coverage verifies source-built CPython 3.13.12, PyTorch 2.12.0a0, upstream vLLM 0.19.2rc1 dev, TheRock ROCm 7.13, local AOTriton/AOCL-LibM, flash attention import, and gfx1151 AITER enablement.

Hardware

Primary target is the AMD Strix Halo platform:

Component Specification
CPU Zen 5, 16 cores, native 512-bit AVX-512
GPU RDNA 3.5 iGPU, gfx1151, 40 CUs
Memory 128 GB unified LPDDR5X (shared CPU/GPU)
NPU XDNA 2 (Phoenix)

License

MIT

The upstream projects referenced by these scripts (TheRock, PyTorch, Triton, vLLM, etc.) each have their own licenses. See the respective repositories for details.