Figure 6b reports backward attention throughput on A100 80GB SXM4; PyTorch is marked OOM at sequence length 16k. The panel covers non-causal attention with head dimension 128.

Figure 6b reports backward attention throughput on A100 80GB SXM4; PyTorch is marked OOM at sequence length 16k. The panel covers non-causal attention with head dimension 128. · CiteArk

测量结果

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保留同一结论下的完整结果组。已有评估排在前面；单项判断不替代整组结论。展开行查看条件、来源与历次证据。

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	59 TFLOPs/s	—	尚未评估
method: FlashAttention · sequenceLength: 512	78 TFLOPs/s	—	尚未评估
method: xFormers · sequenceLength: 512	76 TFLOPs/s	—	尚未评估
method: FlashAttention Triton · sequenceLength: 512	68 TFLOPs/s	—	尚未评估
method: FlashAttention-2 · sequenceLength: 512	136 TFLOPs/s	—	尚未评估
method: PyTorch · sequenceLength: 1k	73 TFLOPs/s	—	尚未评估
method: FlashAttention · sequenceLength: 1k	75 TFLOPs/s	—	尚未评估
method: xFormers · sequenceLength: 1k	84 TFLOPs/s	—	尚未评估
method: FlashAttention Triton · sequenceLength: 1k	74 TFLOPs/s	—	尚未评估
method: FlashAttention-2 · sequenceLength: 1k	159 TFLOPs/s	—	尚未评估
method: PyTorch · sequenceLength: 2k	86 TFLOPs/s	—	尚未评估
method: FlashAttention · sequenceLength: 2k	79 TFLOPs/s	—	尚未评估

Figure 6b reports backward attention throughput on A100 80GB SXM4; PyTorch is marked OOM at sequence length 16k. The panel covers non-causal attention with head dimension 128.

报告指标与观测值

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测量结果