  On-device large language models (LLMs), referring to running LLMs on edge
devices, have raised considerable interest owing to their superior privacy,
reduced latency, and bandwidth saving. Nonetheless, the capabilities of
on-device LLMs are intrinsically constrained by the limited capacity of edge
devices compared to the much more powerful cloud centers. To bridge the gap
between cloud-based and on-device AI, mobile edge intelligence (MEI) presents a
viable solution to this problem by provisioning AI capabilities within the edge
of mobile networks with improved privacy and latency relative to cloud
computing. MEI sits between on-device AI and cloud-based AI, featuring wireless
communications and more powerful computing resources than end devices. This
article provides a contemporary survey on harnessing MEI for LLMs. We first
cover the preliminaries of LLMs, starting with LLMs and MEI, followed by
resource-efficient LLM techniques. We then illustrate several killer
applications to demonstrate the need for deploying LLMs at the network edge and
present an architectural overview of MEI for LLMs (MEI4LLM). Subsequently, we
delve into various aspects of MEI4LLM, extensively covering edge LLM caching
and delivery, edge LLM training, and edge LLM inference. Finally, we identify
future research opportunities. We aim to inspire researchers in the field to
leverage mobile edge computing to facilitate LLM deployment in close proximity
to users, thereby unleashing the potential of LLMs across various privacy- and
delay-sensitive applications.
