Training large AI models requires distributing work across multiple GPUs due to memory and compute constraints. Five parallelism strategies address these challenges: data parallelism splits batches across devices, model parallelism divides layers across GPUs, tensor parallelism partitions weight matrices, pipeline parallelism creates sequential stages with micro-batches, and hybrid parallelism combines all approaches for maximum scale. Supporting techniques like ZeRO optimizer sharding, activation checkpointing, and offloading further reduce memory pressure. Strategy selection depends on model size—data parallelism for small models, tensor/model parallelism for large models, and hybrid approaches for massive models on clusters.
Table of contents
IntroductionWhat parallelism solvesData parallelismModel parallelismTensor parallelismPipeline parallelismHybrid parallelismSupporting techniquesChoosing a strategy & common mistakesConclusion696 Impressions