Figure 7c reports forward-plus-backward attention throughput on H100 80GB SXM5; PyTorch is marked OOM at sequence length 16k. The panel covers causal attention with head dimension 64.

Figure 7c reports forward-plus-backward attention throughput on H100 80GB SXM5; PyTorch is marked OOM at sequence length 16k. The panel covers causal attention with head dimension 64. · CiteArk

测量结果

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每行是一项测量，分别显示论文报告、最近可用实测和累计证据状态。
指标与条件	论文报告	最近可用实测	证据判断
method: PyTorch · sequenceLength: 512	26 TFLOPs/s	—	尚未评估
method: FlashAttention · sequenceLength: 512	104 TFLOPs/s	—	尚未评估
method: FlashAttention-2 · sequenceLength: 512	141 TFLOPs/s	—	尚未评估
method: PyTorch · sequenceLength: 1k	29 TFLOPs/s	—	尚未评估
method: FlashAttention · sequenceLength: 1k	123 TFLOPs/s	—	尚未评估
method: FlashAttention-2 · sequenceLength: 1k	192 TFLOPs/s	—	尚未评估
method: PyTorch · sequenceLength: 2k	31 TFLOPs/s	—	尚未评估
method: FlashAttention · sequenceLength: 2k	136 TFLOPs/s	—	尚未评估
method: FlashAttention-2 · sequenceLength: 2k	232 TFLOPs/s	—	尚未评估
method: PyTorch · sequenceLength: 4k	32 TFLOPs/s	—	尚未评估
method: FlashAttention · sequenceLength: 4k	138 TFLOPs/s	—	尚未评估
method: FlashAttention-2 · sequenceLength: 4k	257 TFLOPs/s	—	尚未评估

Figure 7c reports forward-plus-backward attention throughput on H100 80GB SXM5; PyTorch is marked OOM at sequence length 16k. The panel covers causal attention with head dimension 64.

报告指标与观测值

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测量结果