Distributed training splits the computation for training a large AI model across multiple GPUs, machines, or data centers, dramatically reducing training time. Hundreds of GPUs work in parallel, each handling a portion of data or model layers. Frameworks like PyTorch's DistributedDataParallel and DeepSpeed manage coordination. Training GPT-4-class models would be impossible without distributed techniques.