🎬AI Lectures15

This session explains how to speed up and scale training when one GPU or a simple setup is not enough. It reviews data parallelism (split data across devices) and pipeline parallelism (split model across devices), then dives into practical fixes for their main bottlenecks. The key tools are gradient accumulation, virtual batch size, and interleaved pipeline stages. You’ll learn the trade‑offs between memory use, communication overhead, and idle time.