How do large-scale NLP systems handle distributed training across thousands of GPUs?

They use data, tensor, and pipeline parallelism to split computation across GPUs.

Distributed training in large NLP models relies on multiple parallelism strategies. Data parallelism splits batches across GPUs, tensor parallelism splits matrix operations inside layers, and pipeline parallelism divides model layers across devices. Systems like DeepSpeed and Megatron-LM combine these strategies with gradient checkpointing and ZeRO optimization to reduce memory footprint and communication overhead.