ZeRO Memory Optimizations: Toward Training Trillion Parameter Models
Systems and Performance
Deep Learning
Natural Language Processing
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About this paper
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.