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System DesignHard· Pipeline Parallelism· Cluster Topology· Distributed Training

A 400B+ training run spans multiple rack spine switches...

When training massive models across multiple racks, physical placement is critical. Pipeline Parallelism (PP) requires frequent point-to-point activation transfers, which should be kept on high-bandwidth leaf switch fabric by grouping consecutive stages in adjacent nodes. Data Parallelism (DP) involves gradient all-reduces that occur once per step and are more tolerant of the higher latency associated with traversing spine switches. Ignoring rack-locality is a common cause of unexplained throughput variance.

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