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.
Table of contents
IntroductionKey TakeawaysWhy attention kernels still dominate LLM costFlashAttention Evolution at a GlanceWhat’s New in FlashAttention 4Compatibility and current statusHow to Adopt FlashAttention – Decision GuideFAQsConclusionReferencesSort: