Architectures · advanced · concept 105 of 311 · Updated Nov 4, 2026
FlashAttention and IO-Aware Attention
An exact attention algorithm that is fast because it moves less data. Standard attention writes an N by N score matrix to GPU memory, which dominates cost at long sequence lengths. FlashAttention tiles the computation so each block stays in fast on-chip SRAM and recomputes scores in the backward pass. Same answer, no approximation. The first paper reported a 3x speedup on GPT-2 at sequence length 1K; later versions target Hopper GPUs and FP8.
Top AI contributors on this
- Dario AmodeiAnthropic
- Sam AltmanOpenAI
- Jeff DeanDiscovery Loop
- Noam ShazeerOpenAI
- Alec RadfordIndependent / Thinking Machines Lab (adviser)
- Oriol VinyalsDiscovery Loop
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Default attention kernel in PyTorch, vLLM and Hugging Face training and serving, long-context LLM training, video and protein models
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