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.

翻译：生成式人工智能（GenAI）通过内容创作为用户提供多样化服务，被视作未来网络中最关键的组成部分之一。然而，大人工智能模型（BAIM）的训练与部署带来了巨大的计算与通信开销。由于对高性能计算基础设施的需求，以及云端服务长距离访问中的可靠性、安全性与时效性问题，集中式方法面临严峻挑战。因此，亟需实现服务的去中心化，将部分服务从云端迁移至边缘，构建原生GenAI服务以实现私密、及时且个性化的用户体验。本文提出一种全新的自底向上BAIM架构，通过大云模型与小边缘模型的协同机制，设计分布式训练框架与面向任务的部署方案，以高效供给原生GenAI服务。该框架可促进协同智能、增强适应性、汇聚边缘知识并缓解边缘-云端负担。通过图像生成用例验证了所提框架的有效性。最后，我们概述了充分挖掘边缘与云端协作潜力以应用于原生GenAI与BAIM的基础研究方向。