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.
Designing Large Foundation Models for Efficient Training and Inference: A Survey
This survey examines techniques for compressing large language models to reduce their size and computational requirements while maintaining performance, focusing on methods such as quantization, knowledge distillation, and pruning, as well as system-level optimizations.
- Year
- 2024
- Venue
- arXiv 2024
- Authors
- 8
- Hosting
- Abstract onlyARXIV-DEFAULT
Cite
Notes
Only stored in your browser.
Attribution
- Abstract & full text
- arxiv.org/abs/2409.01990v5ARXIV-DEFAULT
- TL;DR
- Semantic Scholar