Faster and Lighter LLMs: A Survey on Current Challenges and Way Forward

Faster and Lighter LLMs: A Survey on Current Challenges and Way Forward

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 overvie...

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Bibliographic Details
Main Authors: Chavan, Arnav, Magazine, Raghav, Kushwaha, Shubham, Debbah, Mérouane, Gupta, Deepak
Format: Journal Article
Language:English
Published: 02-02-2024
Subjects:
Computer Science - Artificial Intelligence
Computer Science - Computation and Language
Computer Science - Learning
Online Access:Get full text
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Summary: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
DOI:10.48550/arxiv.2402.01799