Validation

Required on NVIDIA Thor with CUDA 13 and CUTLASS DSL 4.4.x, 4.5.x, or 4.6.x:

python tests/test_fa4_cute_runtime.py --backend source --mode full
python tests/test_fa4_cute_runtime.py --backend installed --mode full \
  --artifact build/torch-cuda

The gate covers non-causal and causal GQA, head dimensions 48/72/128, and the PI0.5 D256 encoder family. D256 cases include dense lengths 320/968 and padded seqused_k lengths 456/712 in a 968-token buffer. Every case reports max/p99/mean/cosine metrics and requires bitwise CUDA Graph replay after JIT warmup.

On 2026-08-06, the current source snapshot passed 8/8 rows on NVIDIA Thor (SM110), PyTorch 2.13.0+cu130, CUDA 13.0, and CUTLASS DSL 4.4.2. D48/D72/D128 and all four D256 rows passed; worst p99 absolute error was 0.000244 and worst cosine was 0.99999982. CUDA Graph replay was bitwise deterministic. The torch-noarch final-layout import probe also passed from the isolated Thor environment.

The same 8/8 source matrix passed on the same Thor with CUTLASS DSL 4.6.1. The 4.6 compatibility aliases cover public API relocations only; D48/D72/D128 and D256 dense/seqused results matched the 4.4.2 gate.

Installed-artifact execution and performance must still be rerun after every Hub rebuild. Source qualification alone is not a release artifact claim.