KernelBench hard · RTX PRO 6000

FP8 GEMM GLM-5.2

manually audited: clean

Genuine fp8 x fp8 Triton tensor-core GEMM, the most Blackwell-native of the three audited B200 cells: TMA loads via tl.make_tensor_descriptor (with the required triton.set_allocator workspace hook), persistent one-CTA-per-SM scheduling over grouped tiles, fp32 accumulate, per-channel scale in the epilogue, bf16 store. Weight is held transposed + K-padded to 256 in a cache invalidated by tensor._version (correct under numeric-stress in-place rescaling - empirically verified); the activation is re-padded fresh every call, and output is freshly allocated, so no output memoization is possible. Fixed per-M-class tile configs instead of autotune (deliberate: host-side autotune timing mis-selects on the contended shared box). No forbidden ops, no grader sniffing, no template edits. 0.131 geomean, honest.

20260719_024723_zai-claude_glm-5.2_01_fp8_gemm