FSDP, PP, CP, and EP: Four Parallel Dimensions in Large-Scale Training
This is a sample English post for testing bilingual navigation and comments. 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. You can replace this sample with a real article later. ...