  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.
