ZeRO-Infinity and DeepSpeed: Unlocking unprecedented model scale for deep learning training

Since the DeepSpeed optimization library was introduced last year, it has rolled out numerous novel optimizations for training large AI models—improving scale, speed, cost, and usability. As large models have quickly evolved over the last year, so too has DeepSpeed. Whether enabling researchers to create the 17-billion-parameter Microsoft Turing Natural Language Generation (Turing-NLG) with state-of-the-art accuracy, achieving the fastest BERT training record, or supporting 10x larger model training using a single GPU, DeepSpeed continues to tackle challenges in AI at Scale with the latest advancements for large-scale model training. Now, the novel memory optimization technology ZeRO (Zero Redundancy Optimizer), included in DeepSpeed, is undergoing a further transformation of its own. The improved ZeRO-Infinity offers the system

 

 

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