Faster and Lighter LLMs: A Survey on Current Challenges and Way Forward
Arnav Chavan, Raghav Magazine, Shubham Kushwaha, Merouane Debbah, Deepak Gupta
Abstract
Despite the impressive performance of LLMs, their widespread adoption faces challenges due to substantial computational and memory requirements during inference. Recent advancements in model compression and system-level optimization methods aim to enhance LLM inference. This survey offers an overview of these methods, emphasizing recent developments. Through experiments on LLaMA(/2)-7B, we evaluate various compression techniques, providing practical insights for efficient LLM deployment in a unified setting. The empirical analysis on LLaMA(/2)-7B highlights the effectiveness of these methods. Drawing from survey insights, we identify current limitations and discuss potential future directions to improve LLM inference efficiency. We release the codebase to reproduce the results presented in this paper at https://github.com/nyunAI/Faster-LLM-Survey
BibTeX
@inproceedings{ijcai2024p883,
title = {Faster and Lighter LLMs: A Survey on Current Challenges and Way Forward},
author = {Chavan, Arnav and Magazine, Raghav and Kushwaha, Shubham and Debbah, Merouane and Gupta, Deepak},
booktitle = {Proceedings of the Thirty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-24}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Kate Larson},
pages = {7980--7988},
year = {2024},
month = {8},
note = {Survey Track},
doi = {10.24963/ijcai.2024/883},
url = {https://doi.org/10.24963/ijcai.2024/883},
}