A Review on Edge Large Language Models: Design, Execution, and Applications

TL;DR

This survey reviews recent advancements in deploying large language models on resource-limited edge devices, focusing on design, optimization, and applications to bridge the gap between LLM capabilities and edge constraints.

Contribution

It provides a comprehensive overview of techniques for resource-efficient edge LLM deployment, covering design, optimization, and application domains, and identifies future research directions.

Findings

Summarizes state-of-the-art edge LLM techniques

Highlights challenges in resource constraints and hardware heterogeneity

Suggests future research directions for edge LLMs

Abstract

Large language models (LLMs) have revolutionized natural language processing with their exceptional understanding, synthesizing, and reasoning capabilities. However, deploying LLMs on resource-constrained edge devices presents significant challenges due to computational limitations, memory constraints, and edge hardware heterogeneity. This survey provides a comprehensive overview of recent advancements in edge LLMs, covering the entire lifecycle: from resource-efficient model design and pre-deployment strategies to runtime inference optimizations. It also explores on-device applications across various domains. By synthesizing state-of-the-art techniques and identifying future research directions, this survey bridges the gap between the immense potential of LLMs and the constraints of edge computing.