Record fleet benchmark results and the optimisation story

Final ordering at 128^3/128ch now tracks GPU core count, which is the evidence the
op is genuinely compute-bound rather than dispatch-bound:
  m3ultra (80c) 13.8ms | m2max (38c) 27.3ms | m1ultra (64c) 39.7ms | m1max (32c) 41.4ms

m4pro reachable by ICMP but not ssh (key/user), and it is HANDS OFF per fleet rules,
so it is excluded rather than provisioned.

README.md

@@ -45,6 +45,41 @@ So the whole blocker is one operation: **submanifold 3×3×3 convolution**.
 - [ ] End-to-end inference
 - [ ] Fleet benchmark (m1max / m2max / m4pro / m1ultra / m3ultra)
 
+## Fleet benchmark
+
+`SubMConv3d` at 128³ grid, 128 channels, ~10% occupancy (209,715 voxels) — the hot op
+in the V-VAE. `conv_ms` is GPU work with a warm indice cache.
+
+| box | chip | GPU cores | conv_ms | Mvox/s |
+|---|---|---|---|---|
+| m3ultra | M3 Ultra | 80 | **13.8** | 15.25 |
+| m2max | M2 Max | 38 | 27.3 | 7.69 |
+| m1ultra | M1 Ultra | 64 | 39.7 | 5.28 |
+| m1max | M1 Max | 32 | 41.4 | 5.06 |
+
+Getting there took two rounds, both of which the fleet data — not local profiling —
+made visible:
+
+| version | m3ultra | note |
+|---|---|---|
+| per-offset loop | 81.6 ms | *slower than a 38-core M2 Max* |
+| fused gather + matmul | 39.3 ms | 2.1× |
+| cached device index | **13.8 ms** | 5.9× total |
+
+The first version was launch-latency bound: 2·K³ = 54 tiny dispatches per layer, none
+large enough to occupy the GPU. The giveaway was throughput being *anti-correlated* with
+core count — the 80-core Ultra lost to every smaller box, because its fused-die design
+punishes small dispatches hardest. Fusing the taps into one `[N,K³·Cin]×[K³·Cin,Cout]`
+matmul fixed the dispatch count; caching the prepared device index removed ~26 ms of
+per-layer numpy bookkeeping that was hiding behind ~13 ms of real GPU work.
+
+Only after both did core count start predicting performance, which is the sign the op is
+finally compute-bound. Note the M1 Ultra (64 cores) still barely beats the M1 Max (32) —
+gather-heavy work scales poorly across UltraFusion.
+
+**Next target:** the indice map is now dominant (232–357 ms, CPU numpy) and is only
+amortised because it is cached per coordinate set. It matters whenever coordinates change.
+
 ## Correctness
 
 `spconv` cannot be installed here — that is the reason this port exists — so there is no