colibri/docs/metal.md

Metal backend (Apple Silicon, experimental)

On Apple Silicon the decode profile is matmul-bound, and unified memory removes the PCIe copy tax that keeps CUDA's streaming experts on the CPU — so colibrì has an opt-in Metal backend that runs the routed-expert SwiGLU (batched, zero-copy from the RAM slabs), the fused decode attention (full MLA layer in one command buffer, S≤4), and prefill's large GEMMs on the GPU. Token-exact vs the CPU path.

cd c
make glm METAL=1          # macOS only; no Xcode needed (shader compiles at runtime)
make metal-test           # standalone kernel/attention correctness vs CPU reference
COLI_METAL=1 COLI_MODEL=/path/glm52_i4 ./coli chat --ram 96

Measured on an M4 Max (128 GB, warm cache, MTP on): CPU 0.30 → Metal 0.42 tok/s (~1.4×) (best config adds DIRECT=1; ~3× vs this machine's first cold run). An M5 Max with a 46.9 GB learned pin reached 2.06 tok/s (#103; see also the M5 Max performance report).

Key design points: Metal's ~5 ms submit latency makes per-matmul dispatch a loss — everything is batched into few command buffers per layer, and the resident experts' GPU work is submitted before the missed experts' disk reads so I/O and compute overlap. COLI_METAL_GEMM_MIN tunes the prefill GEMM row threshold (default 16). Streaming, cache, MTP, DSA and the persistence formats are unchanged; every GPU path falls back to the CPU per-block on any fault. Numerics are dequant→f32-MAC (same as the CUDA tier); greedy outputs are byte-identical to the CPU engine.