Training large-scale language models with limited GPU memory: a survey

Yu TANG; Linbo QIAO; Lujia YIN; Peng LIANG; Ao SHEN; Zhilin YANG; Lizhi ZHANG; Dongsheng LI

doi:10.1631/FITEE.2300710

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Training large-scale language models with limited GPU memory: a survey

Regular Papers | Updated：2025-04-03

- Training large-scale language models with limited GPU memory: a survey
  Enhanced Publication
- 有限GPU显存下的大语言模型训练技术综述
- Frontiers of Information Technology & Electronic Engineering Vol. 26, Issue 3, Pages: 309-331(2025)
- Affiliations：
  
  National Key Laboratory of Parallel and Distributed Computing, College of Computer, National University of Defense Technology, Changsha 410073, China
- Author bio：
  
  E-mail: tangyu14@nudt.edu.cn
  ‡Corresponding author
- Funds：
- DOI：10.1631/FITEE.2300710
  CLC： TP389.1
- Received：17 October 2023，
  
  Revised：31 March 2024，
  
  Published Online：17 March 2025，
  
  Published：2025-03
- Accepted：
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Yu TANG, Linbo QIAO, Lujia YIN, et al. Training large-scale language models with limited GPU memory: a survey[J]. Frontiers of information technology & electronic engineering, 2025, 26(3): 309-331.
DOI：

Yu TANG, Linbo QIAO, Lujia YIN, et al. Training large-scale language models with limited GPU memory: a survey[J]. Frontiers of information technology & electronic engineering, 2025, 26(3): 309-331. DOI： 10.1631/FITEE.2300710.

摘要

大模型凭借其在多领域应用中的卓越性能，已在计算机视觉、自然语言处理等领域获得广泛关注。然而，此类模型的训练面临图形处理器（GPU）显存容量的显著制约。本文系统梳理了有限GPU显存条件下大模型训练的优化技术体系。首先深入解析训练过程中GPU显存占用的三大核心要素——模型参数、模型状态和模型激活；继而从这三个维度对现有研究成果进行多角度评述；最后展望了该领域未来的发展方向，强调持续创新显存优化技术对推动大语言模型发展的重要性。本综述为研究人员理解大语言模型训练中的显存优化挑战与技术演进提供了系统参考。

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.

关键词

Keywords

references

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