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arXiv · 2409.01990

Designing Large Foundation Models for Efficient Training and Inference: A Survey

Dong Liu·Yanxuan Yu·Yite Wang·Jing Wu·Zhongwei Wan·Sina Alinejad·Benjamin Lengerich·Ying Nian Wu
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Abstract

This paper focuses on modern efficient training and inference technologies on foundation models and illustrates them from two perspectives: model and system design. Model and System Design optimize LLM training and inference from different aspects to save computational resources, making LLMs more efficient, affordable, and more accessible. The paper list repository is available at https://github.com/NoakLiu/Efficient-Foundation-Models-Survey.

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Dong Liu, Yanxuan Yu, Yite Wang, Jing Wu, Zhongwei Wan, Sina Alinejad, Benjamin Lengerich, Ying Nian Wu. 2024-09-03. Designing Large Foundation Models for Efficient Training and Inference: A Survey. https://arxiv.org/abs/2409.01990

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