How does PyTorch handle distributed training across multiple GPU nodes?

All answers to this question.

Distributed Data Parallel (DDP) is definitely the way to go. Unlike the older DataParallel, which is single-process and often suffers from GIL contention, DDP creates a separate process for each GPU. This avoids the overhead of replicating the model in every forward pass and significantly speeds up the communication of gradients via NCCL. In our last project using 32 A100 GPUs across 4 nodes, we achieved almost linear scaling. The setup is slightly more complex, requiring a proper launch script, but the performance benefits for large-scale PyTorch training are undeniable.

   Answered 2025-11-24 by Lisa Anderson

Is it worth looking into Fully Sharded Data Parallel (FSDP) instead of DDP if our model parameters don't fit into the memory of a single GPU?

   Answered 2025-11-27 by Matthew Kelly

• Matthew, absolutely. If you're hitting OOM errors because of model size, FSDP is the modern standard in PyTorch. It shards the model parameters, gradients, and optimizer states across the GPUs, which is essential for training LLMs. DDP is great for speed when the model fits, but FSDP is what makes those "billion-parameter" models actually possible on standard hardware.

     Commented 2025-11-29 by Daniel Wright

I found that using PyTorch Lightning on top of DDP simplifies the boilerplate code immensely. It handles the distributed backend setup for you automatically.

   Answered 2025-12-01 by Brandon Cooper

• Lightning is a lifesaver, Brandon. It lets you focus on the architecture while it handles all the distributed engineering headaches in the background.

     Commented 2025-12-02 by Steven Parker