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  "text": "New research: FlashAttention-4\n\nFlashAttention-4 achieves up to 1.3x speedup over cuDNN 9.13 and 2.7x over Triton on B200 GPUs with BF16.\n\nFlashAttention-4 co-designs algorithms and kernel pipelines for Blackwell GPUs, where tensor core throughput doubles but memory bandwidth and exponential units scale more slowly.\n\nThe techniques include fully asynchronous MMA operations, software-emulated exponential rescaling, and leveraging tensor memory to reduce shared memory traffic.\n\nFlashAttention-4 achieves up to 1.3x speedup over cuDNN and 2.7x over Triton on B200 GPUs, reaching 1613 TFLOPs/s at 71% utilization.\n\nImplemented entirely in Python via CuTe-DSL with 20-30x faster compile times compared to C++ templates.\n\nPaper: https://t.co/wBiS51m8Bm\n\nLearn to build effective AI agents in our academy: https://t.co/LRnpZN7deE",
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