DeepSpeed

Fully Sharded Data Parallel (FSDP) and ZeRO: How Memory Sharding Eliminates Redundant Model States in Distributed Training

Fully Sharded Data Parallel (FSDP) and ZeRO: How Memory Sharding Eliminates Redundant Model States in Distributed Training Training large language models across distributed GPU clusters introduces a fundamental memory bottleneck. In traditional Distributed Data Parallel (DDP) setups, every GPU maintains an identical copy of model weights, optimizer states, and gradients while processing independent data batches. As models scale from billions to hundreds of billions of parameters, static model s