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.

翻译：大型语言模型（LLM）通过生成式信息赋能日常生活已取得巨大成功，而LLM的个性化处理因更好地对齐人类意图可进一步推动其应用。为实现个性化生成服务，云边协同方法颇具前景，因其能有效编排异构分布式通信与计算资源。本文在探讨几种候选云边协同技术的利弊后，提出NetGPT架构，可根据边缘与云端计算能力合理部署相应规模的LLM。此外，边缘LLM能高效利用基于位置的信息进行个性化提示补全，从而有益于与云端LLM的交互。通过在边缘与云端部署代表性开源LLM（如GPT-2-base与LLaMA模型），我们基于低秩自适应轻量微调验证了NetGPT的可行性。随后，我们强调面向NetGPT的原生人工智能（AI）网络架构所需的关键变革，特别关注通信与计算资源的深度融合以及逻辑AI工作流的精心校准。进一步，我们展示了NetGPT的若干衍生优势——鉴于边缘LLM在预测趋势与推断意图方面的惊人能力，这可能为智能网络管理与编排提供统一解决方案。总之，我们认为NetGPT是一种超越个性化生成服务的、有前景的原生AI网络架构。