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.

大型语言模型的部署面临一些挑战，云部署方式会有长时间响应、高带宽成本和数据隐私问题。文章探讨了在 6G 边缘部署语言模型的潜力，介绍了多模态语言模型的关键应用，并提出了 6G 移动边缘计算的架构。此外，讨论了边缘训练和边缘推理的设计方面，提出了一些有效的技术以促进语言模型的高效部署。