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inference.academy

glossary/scale/all-to-all

All-to-all

also dispatch and combine

The collective in which every GPU sends a different piece of data to every other GPU. Expert parallelism needs two per MoE layer: one to dispatch each token's activations to the cards holding its experts, one to bring the results back. Unlike an all-reduce it is not a sum, so it cannot be tree-reduced, and its cost is set by the slowest link in the group. It is the communication that decides how wide expert parallelism can go.


two per MoE layer

Dispatch and combine, each crossing the interconnect for every token in the step.


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