Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities
This is a position paper that identifies challenges and opportunities for deploying LLMs at the 6G edge, which could benefit applications like robotics and healthcare by improving efficiency and privacy, but it is incremental as it builds on existing edge computing and LLM techniques.
The paper tackles the problem of deploying large language models (LLMs) at the 6G edge to address challenges like long response times, high bandwidth costs, and data privacy issues in cloud-based systems, proposing a vision and discussing techniques such as split learning and quantization for efficient edge deployment.
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.