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.

中文翻译：

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NetGPT：用于提供超越个性化生成服务的 AI 原生网络架构


大型语言模型（LLMs ）在通过生成信息增强我们的日常生活方面取得了巨大的成功。个性化LLMs由于更好地符合人类意图，可以进一步为其应用做出贡献。对于个性化生成服务，协作云边缘方法是有前途的，因为它有助于有效编排异构分布式通信和计算资源。在本文中，我们提出 NetGPT 来有效地协同适当的LLMs基于其计算能力的边缘和云。另外，边LLMs可以有效地利用基于位置的信息进行个性化提示完成，从而有利于与云的交互LLM。特别是，我们通过利用开源的低阶自适应微调来展示 NetGPT 的可行性LLMs（即基于GPT-2的模型和LLaMA模型），并与替代的云边协作或纯云技术进行全面的数值比较，以证明NetGPT的优越性。随后，我们强调了人工智能 (AI) 原生网络架构向 NetGPT 所需的本质变化，重点是通信和计算资源的更深入集成以及逻辑 AI 工作流程的仔细校准。此外，我们还展示了 NetGPT 的几个好处，这些好处是作为副产品、作为边缘LLMs预测趋势和推断意图的能力有望为智能网络管理和编排提供统一的解决方案。我们认为 NetGPT 是一种很有前景的人工智能原生网络架构，可提供超越个性化生成服务的服务。