Large language models (LLMs) have triggered tremendous success to empower
daily life by generative information, and the personalization of LLMs could
further contribute to their applications due to better alignment with human
intents. Towards personalized generative services, a collaborative cloud-edge
methodology sounds promising, as it facilitates the effective orchestration of
heterogeneous distributed communication and computing resources. In this
article, after discussing the pros and cons of several candidate cloud-edge
collaboration techniques, we put forward NetGPT to capably deploy appropriate
LLMs at the edge and the cloud in accordance with their computing capacity. In
addition, edge LLMs could efficiently leverage location-based information for
personalized prompt completion, thus benefiting the interaction with cloud
LLMs. After deploying representative open-source LLMs (e.g., GPT-2-base and
LLaMA model) at the edge and the cloud, we present the feasibility of NetGPT on
the basis of low-rank adaptation-based light-weight fine-tuning. Subsequently,
we highlight substantial essential changes required for a native artificial
intelligence (AI) network architecture towards NetGPT, with special emphasis on
deeper integration of communications and computing resources and careful
calibration of logical AI workflow. Furthermore, we demonstrate several
by-product benefits of NetGPT, given edge LLM's astonishing capability to
predict trends and infer intents, which possibly leads to a unified solution
for intelligent network management \& orchestration. In a nutshell, we argue
that NetGPT is a promising native-AI network architecture beyond provisioning
personalized generative services.

本篇文章提出了一种基于协作的云边协同方法，称为 NetGPT，它能够有效地编排异构分布式通信和计算资源，并应用于提供个性化的生成服务，使用代表性的开源 LLMs（例如 GPT-2-basic 和 LLaMA 模型）的可行性，重点强调了用于实现本机 AI 网络体系结构的实质性变化，并演示了 NetGPT 的几个副产品优势。