All-Reduce vs. All-to-All: Why AI Network Traffic Patterns Differ
All-Reduce combines corresponding values and gives the combined result to every participant. All-to-All redistributes different chunks to different participants without doing that reduction. They solve different problems. A network that handles one well can still struggle with the other because message sizes, destinations, synchronization and the physical routes differ. Start with the operation, not the port speed Before comparing AI cluster bandwidth numbers, identify the collective, group size, payload size, data type and rank placement. Keep the workload fixed. A faster All-Reduce score is not evidence of faster expert routing, and fewer bytes in an example do not make All-to-All a replacement for All-Reduce. Evidence checked October 26, 2026, Korea time: NCCL 2.32.3 documentation, PyTorch 2.14 documentation and the public Megatron Core implementation. The diagrams and numbers are original synthetic examples. Fourteen local Node.js assertions checked their arithmetic and data la...