How Thousands of GPUs Train One AI: 5 Ways They Work Together

How do you train an AI model on thousands of GPUs when data parallelism alone is not sufficient? In this video, we will take a deep dive into five ways to split the workload along different axes. This includes data, tensor, context, pipeline, and expert parallelism. Chapters 0:00 5D parallelism: Five ways to cut the workload 0:33 Recap: data parallelism and ZeRO 4:49 The work cube: tokens × sequences × layers 6:00 Three limits of data parallelism 8:44 Tensor parallelism: column-linear and row-linear 10:24 The MLP and attention: one all-reduce each 14:10 Sequence parallelism 16:16 Context par