Scaling training to multiple GPUs uses parallel processing to distribute computations, reducing training time. Frameworks like TensorFlow and PyTorch support multi-GPU training through data parallelism or model parallelism.
Data parallelism splits the dataset into batches, processing each on a separate GPU, and aggregates gradients during backpropagation. Model parallelism divides the model across GPUs, useful for large architectures like GPT models.
Tools like PyTorch’s DataParallel and DistributedDataParallel or TensorFlow’s tf.distribute.Strategy simplify implementation. Ensure synchronization and proper allocation of workloads to minimize overhead and maximize performance.