English

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

Distributed, Parallel, and Cluster Computing 2025-04-15 v5 Machine Learning

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.

Keywords

Cite

@article{arxiv.2409.01990,
  title  = {Designing Large Foundation Models for Efficient Training and Inference: A Survey},
  author = {Dong Liu and Yanxuan Yu and Yite Wang and Jing Wu and Zhongwei Wan and Sina Alinejad and Benjamin Lengerich and Ying Nian Wu},
  journal= {arXiv preprint arXiv:2409.01990},
  year   = {2025}
}
R2 v1 2026-06-28T18:32:48.099Z