Training deep learning models on multiple GPUs requires specialized parallelization techniques. Distributed Data Parallelism (DDP) serves as the foundation for scaling neural network training, enabling efficient use of GPU clusters by splitting workloads across devices. This approach combines with gradient accumulation to handle massive batch sizes that exceed single-GPU memory limits.

Gradient accumulation allows training with larger effective batch sizes by splitting mini-batches into smaller micro-batches that fit in memory. Each micro-batch runs forward and backward passes, accumulating gradients before a single optimization step. While not inherently parallel, this technique becomes powerful when combined with DDP, where multiple GPUs process micro-batches simultaneously and synchronize gradients using All-Reduce operations.

The practical implementation involves wrapping models in a DDP wrapper that ensures parameter synchronization across devices and averages gradients after each batch. By combining DDP with gradient accumulation, practitioners achieve global batch sizes equal to `num_gpus × micro_batch_size × grad_accum_steps`, significantly reducing communication overhead. This approach enables training of massive models on limited hardware while maintaining training stability and convergence speed.