Fully Sharded Data Parallel: faster AI training with fewer GPUs Engineering at Meta -

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Training AI models at a large scale isn’t easy. Aside from the need for large amounts of computing power and resources, there is also considerable engineering complexity behind training very large …

By Myle Ott , Sam Shleifer , Min Xu , Priya Goyal , Quentin Duval , Vittorio Caggiano Training AI models at a large scale isn’t easy. Aside from the need for large amounts of computing power and resources, there is also considerable engineering complexity behind training very large models. At Facebook AI Research (FAIR) Engineering, we have been working on building tools and infrastructure to make training large AI models easier. Our recent work in areas such as intra-layer model parallelism , pipeline model parallelism , optimizer state+gradient sharding , and mixture of experts is just part

Fully Sharded Data Parallel: faster AI training with fewer GPUs Engineering at Meta -

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