How does distributed training work for LLMs?

Distributed training splits model computation and data across multiple GPUs or machines to train massive LLMs efficiently.

Modern LLMs contain billions or trillions of parameters that cannot fit into a single GPU. Distributed training solves this using:

The system synchronizes gradients during backpropagation so all nodes converge consistently.

Distributed training introduces challenges such as communication overhead, synchronization bottlenecks, and fault tolerance.