How does distributed unsupervised optimization work in multi-GPU systems?

It synchronizes gradients or embeddings across GPUs using data parallelism or model parallelism strategies.

In unsupervised learning, distributed optimization is challenging because there are no labels to stabilize gradients. Systems use synchronized data parallel training (DDP), where each GPU computes embeddings and gradients on mini-batches, followed by all-reduce communication. In contrastive learning, embeddings must be shared across devices for negative sampling, requiring cross-GPU communication (all-gather). Advanced systems use gradient compression and asynchronous updates to reduce bottlenecks.