Figure 7d 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 128.

Figure 7d 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 128. · 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	40 TFLOPs/s	—	尚未评估
method: FlashAttention · sequenceLength: 512	98 TFLOPs/s	—	尚未评估
method: FlashAttention-2 · sequenceLength: 512	163 TFLOPs/s	—	尚未评估
method: PyTorch · sequenceLength: 1k	50 TFLOPs/s	—	尚未评估
method: FlashAttention · sequenceLength: 1k	109 TFLOPs/s	—	尚未评估
method: FlashAttention-2 · sequenceLength: 1k	221 TFLOPs/s	—	尚未评估
method: PyTorch · sequenceLength: 2k	57 TFLOPs/s	—	尚未评估
method: FlashAttention · sequenceLength: 2k	108 TFLOPs/s	—	尚未评估
method: FlashAttention-2 · sequenceLength: 2k	265 TFLOPs/s	—	尚未评估
method: PyTorch · sequenceLength: 4k	61 TFLOPs/s	—	尚未评估
method: FlashAttention · sequenceLength: 4k	126 TFLOPs/s	—	尚未评估
method: FlashAttention-2 · sequenceLength: 4k	294 TFLOPs/s	—	尚未评估

Figure 7d 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 128.

报告指标与观测值

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测量结果