For 1.3B- and 2.7B-parameter GPT-style models at 2k and 8k context, FlashAttention-2 is summarized as up to 1.3× faster than FlashAttention and 2.8× faster than a baseline without FlashAttention, reaching 225 TFLOPs/s/GPU and 72% model-FLOPs utilization.

For 1.3B- and 2.7B-parameter GPT-style models at 2k and 8k context, FlashAttention-2 is summarized as up to 1.3× faster than FlashAttention and 2.8× faster than a baseline without FlashAttention, reaching 225 TFLOPs/s/GPU and 72% model-FLOPs utilization. · CiteArk

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指标与条件	论文报告	最近可用实测	证据判断
method: FlashAttention-2 · baseline: FlashAttention	1.3 ×	—	尚未评估
method: FlashAttention-2 · baseline: Without FlashAttention	2.8 ×	—	尚未评估
model: GPT3-2.7B · method: FlashAttention-2 · context: 8k	225 TFLOPs/s/GPU	—	尚未评估
gpu: A100 · method: FlashAttention-2	72%	—	尚未评估

For 1.3B- and 2.7B-parameter GPT-style models at 2k and 8k context, FlashAttention-2 is summarized as up to 1.3× faster than FlashAttention and 2.8× faster than a baseline without FlashAttention, reaching 225 TFLOPs/s/GPU and 72% model-FLOPs utilization.

报告指标与观测值

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测量结果