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As deep learning models grow in size and complexity, efficient training strategies have become essential. Distributed training techniques such as data parallelism and model parallelism allow workloads to be split across multiple GPUs or nodes, significantly reducing training time. By leveraging frameworks like PyTorch’s DistributedDataParallel, developers can synchronize gradients efficiently while maintaining model consistency. In addition, techniques such as mixed precision training and gradient accumulation further improve performance and resource utilization, enabling faster experimentation and scalable deployment in production environments.