Scaling tests of PyTorch FSDP on AWS show it can scale up to train dense models with 1T parameters. Realized performance in our experiments reached 84 TFLOPS per A100 GPU for GPT 1T model and 159 TFLOPS per A100 GPU for GPT 175B model on AWS cluster. Native FSDP implementation also dramatically improved model initialization time compared to FairScale’s original when CPU offloading was enabled.
In future PyTorch versions, we’re going to enable users to seamlessly switch between DDP, ZeRO-1, ZeRO-2 and FSDP flavors of data parallelism, so that users can train different scales of models with simple configurations in the unified API.
How FSDP Works
FSDP is a type of data-parallel training, but unlike traditional data-parallel, which maintains a per-GPU copy of a model’s parameters, gradients and optimizer states, it shards all of these states across data-parallel workers and can optionally offload the sharded model parameters to CPUs. 
The figure below shows how FSDP works for 2 data-parallel processes: