Training large AI models often requires distributed strategies to overcome memory limitations. Distributed Data Parallel (DDP) replicates the model across GPUs, with each processing a data slice and then averaging gradients. This approach tackles compute time but doesn't solve memory constraints, as each GPU must hold the entire model, requiring around 87 GB of VRAM for a Mistral-7B model with Adam optimizer.

Fully Sharded Data Parallel (FSDP) addresses the memory issue by partitioning model parameters, gradients, and optimizer states across GPUs. This means no single GPU holds the complete model, enabling larger models to fit within limited VRAM. However, FSDP incurs higher communication overhead, reassembling model layers on demand for computation, which involves frequent all-gather and reduce-scatter operations.

Beyond software strategies like DDP and FSDP, the physical interconnectivity of GPUs significantly impacts training performance. The article highlights that GPU wiring can be as critical as the chosen distribution strategy. Inconsistent performance observed across nodes with identical hardware underscored the importance of the physical network topology. Evaluating both software distribution methods and hardware interconnects is essential for optimizing large-scale AI model training.