README: restructure around the technical path — parameters and full results move to docs/, four new figures

docs/metal.md

@@ -0,0 +1,30 @@
+# 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.
+
+```bash
+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](https://github.com/JustVugg/colibri/issues/103); see also the
+[M5 Max performance report](METAL-M5MAX-PERF-REPORT.md)).
+
+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.