Linear attention’s prefix-sum trick still enables fixed-size all-gather scaling
stochasticchasm · x · 2026-07-28
A short post points out that the prefix-sum property of linear attention is still very useful, and the attached excerpt explains why.
- The image describes a method where each rank computes local fragments, then exchanges tensors with a single all-gather.
- After synchronization, the next rank reconstructs state by processing preceding fragments in order.
- The key claim is that this enables fixed-size all-gather for recurrent-state synchronization and achieves linear compute scaling.
- One reply adds that work like this is increasingly being accelerated by agents, but the core content is the linear-attention / linear-scaling technique itself.
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