Dao-AILab/flash-attention

Health v2 · maintenance only

Adoption not part of the score

24787 stars · 3010 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Official implementation of FlashAttention, FlashAttention-2, -3, and -4: fast and memory-efficient exact attention kernels for GPUs. It accelerates transformer training and inference on NVIDIA (and ROCm) hardware with IO-aware CUDA kernels.

Use cases

• speed up transformer training on GPUs
• reduce memory usage of attention in LLM training
• run attention efficiently on H100 GPUs
• train large language models faster
• use exact attention instead of approximations
• benchmark attention kernel performance

When to choose

• you train or fine-tune transformers on NVIDIA GPUs and need faster, memory-efficient attention
• you need exact attention with long sequences that would otherwise OOM
• you target Hopper/Blackwell GPUs and want optimized FP16/BF16/FP8 kernels

When to avoid

• you are not using GPU-accelerated PyTorch with CUDA or ROCm
• you need Windows support or non-CUDA hardware
• you only need a high-level API and prefer frameworks that bundle attention kernels already

Facets

library · maturity active

machine-learning deep-learning gpu-computing benchmarking deep-learning large-language-models gpu-computing machine-learning python attention transformers cuda-kernels pytorch memory-efficiency flash-attention linux gpu cuda

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