[gilby]

What this changes

A routed prompt matrix-core tile of 64 rows an item (affine_wmma_item<BITS, 4>, all 8 waves across the 128 columns, the 128-row tile's staging and fold unchanged, so rows are byte-identical), selected through a new gfx1151.zon (gfx1100's rows plus mlx.routed.prefill.item64 at cost 9 for routed plans of up to 12 pairs an item, using #1607's pairs-per-item bound). Fixes #1601.

Receipt

• Environment: TensorFold main f350cd3e (with HIP: bound the routed prompt's K-parallel tile by pairs per item, and give it gfx1201's short prompts #1607) plus this change, tensorfold-native 1.1.8 built with zig build native-hip -Dhip-arch=gfx1151, Ubuntu archive HIP 7.1 (clang 21), AMD Ryzen AI MAX+ 395 with Radeon 8060S gfx1151 (128 GB unified memory). Checkpoints TensorFold/Qwen3.8-Flash-Next-MLX-4bit-MTP revision 2b170fa6 and an oMLX oQ4e quantization of Qwen3.8-Flash-Next, MTP 3 drafts, --parallel 1 --context 131072 --prompt-cache-gib 0 --policy prefill_step=4096, main and candidate on the same machine.
• Exactness: token_sha256 of tools/bench_openai.py, tools/bench_concurrent.py and both tools/bench_cache.py prompts identical to main on both checkpoints; bench_concurrent --alone --serial 0 failed, 0 unmeasured, every cell equal.
• Kernel timing, zig build hip-affine-tune on gfx1151 at Flash Next's served routed plans (512 experts plus the shared one, 11 slots), us, 128-row tile -> 64-row tile: gate/up (n 1280, k 2560) 256 tokens 10,987 -> 9,741, 512 tokens 11,576 -> 9,932, 1,024 tokens 21,215 -> 10,629, 2,048 tokens 25,580 -> 12,303; down (n 2560, k 640) 5,790 -> 3,425, 6,343 -> 3,737, 11,465 -> 4,702, 14,281 -> 6,325. Plans of 1 to 64 tokens also favour it by about 10%. Those plans hold 3.3 to 6.8 pairs an item; a 4,096-token chunk holds about 9, so the bound is 12 (not timed at 4,096 directly; the served runs below use that step).
• Decode speed, tools/bench_openai.py --tokens 256 --reps 3 --temperatures 0: unchanged (4-bit 50.0 / 43.9 -> 50.2 / 44.1, oQ4e 42.1 / 36.2 -> 42.0 / 36.2 tok/s code / chat; decode takes the stream tiles).
• Prompt speed, tools/bench_cache.py --require-cold --tokens 16 on public 7,377- and 29,364-token arrays, ttft_s of the cold request after the warm-up: 4-bit 20.72 -> 19.90 s and 91.79 -> 88.48 s (-4.0% / -3.6%); oQ4e 22.42 -> 21.40 s and 95.37 -> 92.40 s (-4.5% / -3.1%). One run per cell. The routed products are a larger share of prompt time once the sparse attention is faster (HIP Flash Next: WMMA sparse attention for prompt chunks on gfx11 #1597), so the gain should grow with it.
• Tests run: zig build hip-kernel-contract on gfx1151: SUMMARY gfx1151 51 kernels: 51 pass gates 1-4, 0 fail, 0 known findings; 5 controls: 5 red as planted, 0 missed; 126 limit problems (main: 50 kernels, the same 126 live-versus-recorded register differences for existing kernels under this machine's compiler); the new tile's cases (groups 32 and 64): non-finite, repeat, shapes and sums pass, byte-equal to the 128-row tile it replaces; live limits 176 VGPRs and 34,304 LDS bytes as recorded. zig build hip-kernel-test 106/106 (the routed prompt byte-equality test now compares the new tile against the matrix, GEMM and K-parallel tiles on every routed prompt cut). zig build test -Dhip=true --summary all 468/468 steps, 0 failed. python3 tools/zig/lean_check.py 426 on main and change. git diff --check clean.
• Not run: hip-kernel-contract-limits stops on this machine's compiler at tiles/dot2_tiles.hip for gfx1100 (branch size exceeds simm16, same on main, see HIP: dot2_tiles.hip fails to compile for gfx1100 with Ubuntu clang 21.1.8 ("branch size exceeds simm16") #1521); the records for gfx1030, gfx1100 and gfx1201 come from compiling the tile for each chip and reading its code object (gfx1030 and gfx1201 get a trapping stub, as they have no gfx11 matrix cores, and never select it). gfx1100 keeps its table unchanged (not measured there). Metal and CUDA.

Checklist

• Every token still goes through the lane rounds. No serial path, and nothing that needs drafts off.
• Drafted output equals "draft": false, a resumed prompt equals a fresh one, and concurrent equals solo.
• No precision traded for speed. If the bits change, the description says which sums change and why.
• A new or changed GPU kernel is registered with the kernel contract (docs/kernel-contract.md); each backend's SUMMARY line is in the receipt.
• Prompt processing is no slower than the last release.
• The description says which platforms I ran, Metal M1 to M5 and CUDA, and which I could not.
• New tests fail before the change, pass after it, and skip cleanly without their dependency.
• Comments and docstrings are one line. No measurements or history in the source.
• No personal data, machine names, internal hosts or local paths. No AI attribution lines.