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

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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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