How thousands of GPUs train one AI: a breakdown of 5D parallelism
jbhuang0604 · x · 2026-10-26
ICML 2025 best paper honorable mention recipient jbhuang0604 published a walkthrough explaining that data parallelism alone can't scale training across thousands of GPUs — you need to split the workload along multiple axes.
He breaks down the five parallelism dimensions and how they combine:
- Data parallelism: full model replicas process different batches
- Tensor parallelism: slicing weight matrices within a layer across devices
- Context parallelism: splitting long sequences along the sequence axis
- Pipeline parallelism: different devices own different layers in a pipeline
- Experts parallelism: distributing MoE experts across devices
Full video explainer at the original link.
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