Generative artificial intelligence (GenAI) offers various services to users through content creation, which is believed to be one of the most important components in future networks. However, training and deploying big artificial intelligence models (BAIMs) introduces substantial computational and communication overhead. This poses a critical challenge to centralized approaches, due to the need of high-performance computing infrastructure and the reliability, secrecy and timeliness issues in long-distance access of cloud services. Therefore, there is an urging need to decentralize the services, partly moving them from the cloud to the edge and establishing native GenAI services to enable private, timely, and personalized experiences. In this paper, we propose a brand-new bottom-up BAIM architecture with synergetic big cloud model and small edge models, and design a distributed training framework and a task-oriented deployment scheme for efficient provision of native GenAI services. The proposed framework can facilitate collaborative intelligence, enhance adaptability, gather edge knowledge and alleviate edge-cloud burden. The effectiveness of the proposed framework is demonstrated through an image generation use case. Finally, we outline fundamental research directions to fully exploit the collaborative potential of edge and cloud for native GenAI and BAIM applications.

中文翻译：

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通过协同大云模型和小边缘模型提供生成式人工智能服务的边缘-云协作框架


生成人工智能（GenAI）通过内容创作为用户提供各种服务，这被认为是未来网络中最重要的组成部分之一。然而，训练和部署大型人工智能模型 (BAIM) 会带来大量的计算和通信开销。由于需要高性能计算基础设施以及远程访问云服务的可靠性、保密性和及时性问题，这对集中式方法提出了严峻的挑战。因此，迫切需要分散服务，将其部分从云端转移到边缘，并建立原生 GenAI 服务，以实现私密、及时和个性化的体验。在本文中，我们提出了一种全新的自下而上的BAIM架构，具有协同的大云模型和小边缘模型，并设计了分布式训练框架和面向任务的部署方案，以高效提供原生GenAI服务。所提出的框架可以促进协作智能、增强适应性、收集边缘知识并减轻边缘云负担。通过图像生成用例证明了所提出框架的有效性。最后，我们概述了充分利用边缘和云原生 GenAI 和 BAIM 应用程序的协作潜力的基础研究方向。