FSDP, PP, CP, and EP: Four Parallel Dimensions in Large-Scale Training

Sun, 17 May 2026 01:10:00 +0800

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Large-scale model training is usually not solved by one parallelism strategy alone. Instead, several dimensions are composed:

FSDP shards parameters, gradients, and optimizer states to reduce model-state memory.
Pipeline Parallelism splits model depth across devices.
Context Parallelism shards long sequences and mainly reduces attention activation pressure.
Expert Parallelism distributes MoE experts across devices, which is useful for models such as Qwen, DeepSeek, and Mixtral.

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Context-Parallel on Yan Bai