all to all

Every MoE layer routes each token to eight experts that live on other GPUs, twice per layer, forward and back. The bytes per rank per layer for DeepSeek-V3, why that all-to-all runs close to one-to-one against compute, and why a single hot expert stalls every rank waiting at the combine.

Expert parallelism reads fewer bytes and pays two all-to-alls, so it loses when the interconnect is wrong or when the routing is uneven. Three candidate causes, the measurement that separates them, and why the third one is the answer more often than the first two.

Four operations cover almost everything a distributed training step sends. What each one does to the data, the bytes each rank moves for a message of size S across N ranks, and which parallelism strategy generates which, worked through for a 70B model so the numbers are concrete rather than symbolic.

Two all-to-alls per layer across sixty layers is over a hundred collectives on the critical path of every decode step, each one small and latency-bound. The per-GPU volume worked out, the time on each link type, and why a single large NVLink domain changes the design rather than just improving it.

A trillion parameters with 32 billion active per token: the weights need a rack, the compute needs a fraction of it, and the all-to-all between them decides everything. The memory plan across 72 GPUs, the decode step from bandwidth and two all-to-alls, the compressed attention cache, and what imbalance costs.

The first question is whether 1T is total or active, because storage follows one and compute follows the other. The state budget, the layout that falls out of it, why expert parallelism belongs inside NVLink, and the two failure modes a dense-model plan does not have: router imbalance and all-to-all congestion.