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Large language models (LLMs) have triggered tremendous success to empower ourdaily life by generative information. The personalization of LLMs could furthercontribute to their applications due to better alignment with human intents.Towards personalized generative services, a collaborative cloud-edgemethodology is promising, as it facilitates the effective orchestration ofheterogeneous distributed communication and computing resources. In thisarticle, we put forward NetGPT to capably synergize appropriate LLMs at theedge and the cloud based on their computing capacity. In addition, edge LLMscould efficiently leverage location-based information for personalized promptcompletion, thus benefiting the interaction with the cloud LLM. In particular,we present the feasibility of NetGPT by leveraging low-rank adaptation-basedfine-tuning of open-source LLMs (i.e., GPT-2-base model and LLaMA model), andconduct comprehensive numerical comparisons with alternative cloud-edgecollaboration or cloud-only techniques, so as to demonstrate the superiority ofNetGPT. Subsequently, we highlight the essential changes required for anartificial intelligence (AI)-native network architecture towards NetGPT, withemphasis on deeper integration of communications and computing resources andcareful calibration of logical AI workflow. Furthermore, we demonstrate severalbenefits of NetGPT, which come as by-products, as the edge LLMs' capability topredict trends and infer intents promises a unified solution for intelligentnetwork management & orchestration. We argue that NetGPT is a promisingAI-native network architecture for provisioning beyond personalized generativeservices.

NetGPT: A Native-AI Network Architecture Beyond Provisioning Personalized Generative Services