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Kimi K3 now writes a H100 CUDA kernel 14.82x faster than optimized PyTorch, nearly matching Claude Opus 4.8. KernelBench tests hardware work, not just whether generated code successfully compiles. KernelBench gives an AI agent a readable PyTorch implementation and asks it to produce a custom GPU implementation that is both correct and faster. The code must compile, return the right numerical results across test inputs and beat a reference implementation on actual hardware. A kernel that is fast but wrong fails. A kernel that is correct but slow is not particularly useful.
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