To further enhance multi-GPU training, it is recommended to use mixed-precision training with `torch.cuda.amp`. This can significantly reduce memory usage and improve performance by utilizing tensor cores in compatible GPUs.

In addition to `nn.DataParallel`, PyTorch provides the `nn.DataParallelDistributed` module, which enables training on multiple machines with multiple GPUs. It leverages the `torch.nn.parallel.DistributedDataParallel` class for synchronized gradient updates across all devices.

Another technique is to use `torch.nn.DataParallel` along with `torch.nn.DataParallel(model, device_ids=[0, 1, 2, 3])`. This allows distributing the model across all available GPUs, ensuring efficient utilization of computational resources.

One approach to efficiently utilize multiple GPUs with PyTorch is to use the `nn.DataParallel` module. This automatically parallelizes the forward pass over multiple GPUs and aggregates the gradients during the backward pass.