  Large language models (LLMs) have triggered tremendous success to empower our
daily life by generative information. 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 is promising, as it facilitates the effective orchestration of
heterogeneous distributed communication and computing resources. In this
article, we put forward NetGPT to capably synergize appropriate LLMs at the
edge and the cloud based on their computing capacity. In addition, edge LLMs
could efficiently leverage location-based information for personalized prompt
completion, thus benefiting the interaction with the cloud LLM. In particular,
we present the feasibility of NetGPT by leveraging low-rank adaptation-based
fine-tuning of open-source LLMs (i.e., GPT-2-base model and LLaMA model), and
conduct comprehensive numerical comparisons with alternative cloud-edge
collaboration or cloud-only techniques, so as to demonstrate the superiority of
NetGPT. Subsequently, we highlight the essential changes required for an
artificial intelligence (AI)-native network architecture towards NetGPT, with
emphasis on deeper integration of communications and computing resources and
careful calibration of logical AI workflow. Furthermore, we demonstrate several
benefits of NetGPT, which come as by-products, as the edge LLMs' capability to
predict trends and infer intents promises a unified solution for intelligent
network management & orchestration. We argue that NetGPT is a promising
AI-native network architecture for provisioning beyond personalized generative
services.
