ZeRO Memory Optimizations: Toward Training Trillion Parameter Models

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