Abstract: Large-scale models have gained significant attention in a wide range of fields, such as computer vision and natural language processing, due to their effectiveness across various applications. However, a notable hurdle in training these large-scale models is the limited memory capacity of graphics processing units (GPUs). In this paper, we present a comprehensive survey focused on training large-scale models with limited GPU memory. The exploration commences by scrutinizing the factors that contribute to the consumption of GPU memory during the training process, namely model parameters, model states, and model activations. Following this analysis, we present an in-depth overview of the relevant research work that addresses these aspects individually. Finally, the paper concludes by presenting an outlook on the future of memory optimization in training large-scale language models, emphasizing the necessity for continued research and innovation in this area. This survey serves as a valuable resource for researchers and practitioners keen on comprehending the challenges and advancements in training large-scale language models with limited GPU memory.

Key words: Training techniques; Memory optimization; Model parameters; Model states; Model activations

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