Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities

TL;DR

This paper discusses the potential and challenges of deploying large language models at the 6G mobile edge, highlighting innovative solutions for efficient edge training and inference to enable applications like robotics and healthcare.

Contribution

It provides a comprehensive analysis of the motivations, challenges, and technical approaches for deploying LLMs at the 6G edge, including novel design considerations and techniques.

Findings

Identifies key challenges for LLM deployment at the 6G edge.

Proposes architectural solutions for edge training and inference.

Discusses techniques like split learning, quantization, and parameter sharing.

Abstract

Large language models (LLMs), which have shown remarkable capabilities, are revolutionizing AI development and potentially shaping our future. However, given their multimodality, the status quo cloud-based deployment faces some critical challenges: 1) long response time; 2) high bandwidth costs; and 3) the violation of data privacy. 6G mobile edge computing (MEC) systems may resolve these pressing issues. In this article, we explore the potential of deploying LLMs at the 6G edge. We start by introducing killer applications powered by multimodal LLMs, including robotics and healthcare, to highlight the need for deploying LLMs in the vicinity of end users. Then, we identify the critical challenges for LLM deployment at the edge and envision the 6G MEC architecture for LLMs. Furthermore, we delve into two design aspects, i.e., edge training and edge inference for LLMs. In both aspects, considering the inherent resource limitations at the edge, we discuss various cutting-edge techniques, including split learning/inference, parameter-efficient fine-tuning, quantization, and parameter-sharing inference, to facilitate the efficient deployment of LLMs. This article serves as a position paper for thoroughly identifying the motivation, challenges, and pathway for empowering LLMs at the 6G edge.