This reviews the paper which introduced ZeRO-Offload, a novel technology designed to democratize large-scale deep learning model training by making it accessible even with limited GPU resources. It achieves this by strategically offloading data and computations to the CPU, thereby significantly increasing the size of models that can be trained on a single GPU—up to 13 billion parameters. The paper highlights ZeRO-Offload's efficiency, scalability, and usability, demonstrating superior throughput compared to existing methods like PyTorch and L2L, and near-linear scaling across multiple GPUs. Furthermore, it details optimizations such as a highly efficient CPU Adam optimizer and a one-step delayed parameter update to maximize performance without sacrificing model accuracy. This innovation aims to enable more data scientists to leverage truly massive deep learning models.

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