What Is Distributed AI Training? Data, Tensor, Pipeline, and Expert Parallelism

Distributed AI training involves coordinating data, parameters, activations, gradients, and optimizer states across multiple accelerators to enable training of models or batches larger than a single device can handle. This process includes partitioning work, executing computations locally, communicating tensors, synchronizing updates, and managing checkpoints for recovery. The article clarifies the definition, boundaries, and operational stages of distributed AI training to distinguish it from a