How does TensorFlow handle distributed training across multiple GPUs?

TensorFlow uses strategies like MirroredStrategy to synchronize gradients across GPUs.

Distributed training splits data across GPUs and computes gradients in parallel. Gradients are then aggregated (all-reduce) and synchronized. TensorFlow ensures model consistency using synchronous training strategies. Asynchronous training is also possible but can lead to stale gradients.