Training is the deep learning phase that benefits most from a multi-node architecture. It involves compute- intensive operations-forward and backward passes, gradient computation, and synchronization-across large datasets and complex models. Distributing these tasks across multiple nodes with GPUs accelerates processing, reduces time to convergence, and enables handling models too large for a single node. While data augmentation and inference can leverage multiple nodes, their gains are less pronounced, as they typically involve lighter or more localized computation.
(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on Multi-Node Training)