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ZeRO Memory Optimizations: Toward Training Trillion Parameter Models

ZeRO Memory Optimizations: Toward Training Trillion Parameter Models

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 The paper introduces ZeRO, a novel approach to optimize memory usage when training massive language models. ZeRO-DP and ZeRO-R components effectively reduce memory redundancy and allow for training models with up to 170 billion parameters efficiently. The technique shows superlinear scalability, user-friendly implementation, and has the potential to democratize large model training in AI research. Read full paper: https://arxiv.org/abs/1910.02054 Tags: Systems and Performance, Deep Learning, Natural Language Processing

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