FlashAttention 4 improves LLM inference with faster attention kernels, reduced memory overhead, and better scalability for large transformer models.

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FlashAttention 4 is a memory-optimized attention kernel designed specifically for NVIDIA Blackwell GPUs that reduces memory bandwidth bottlenecks in transformer models. It uses a warp-specialized 5-stage pipeline, computes exponentials on CUDA cores instead of SFUs, and implements adaptive rescaling to minimize overhead. Currently, FA4 only supports forward pass (inference) on Blackwell architecture, lacking backward pass, variable-length sequences, and grouped-query attention support. For production use, FA3 remains recommended for Hopper GPUs and FA2 for Ampere/Ada, while FA4 should be tested incrementally for Blackwell inference workloads with fallback options enabled.

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