8951d74936
Analysis: CUDA graph eliminates kernel launch overhead (~10-20% for decode). At 32768, sequences >32K skip graph capture. At 65536, most competition workload sequences get graph acceleration. Memory: CUDA graph capture allocates one copy of all intermediate tensors at the max captured batch size. With max-num-seqs=1, this is one sequence's worth of tensors — small relative to model weights. Combined with V2 enabled for seq>8192 and threshold raised to 65536, the decode path is now: seq <= 8192: V1 compiled kernel (fastest) 8192 < seq <= 65536: V2 pytorch (single-bmm, good) seq > 65536: PyTorch fallback (rare at competition workload)