Designing Large Foundation Models for Efficient Training and Inference: A Survey
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866917983162466304 |
|---|---|
| author | Liu, Dong Yu, Yanxuan Wang, Yite Wu, Jing Wan, Zhongwei Alinejad, Sina Lengerich, Benjamin Wu, Ying Nian |
| author_facet | Liu, Dong Yu, Yanxuan Wang, Yite Wu, Jing Wan, Zhongwei Alinejad, Sina Lengerich, Benjamin Wu, Ying Nian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_01990 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Designing Large Foundation Models for Efficient Training and Inference: A Survey Liu, Dong Yu, Yanxuan Wang, Yite Wu, Jing Wan, Zhongwei Alinejad, Sina Lengerich, Benjamin Wu, Ying Nian Distributed, Parallel, and Cluster Computing Machine Learning 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. |
| title | Designing Large Foundation Models for Efficient Training and Inference: A Survey |
| topic | Distributed, Parallel, and Cluster Computing Machine Learning |
| url | https://arxiv.org/abs/2409.01990 |