As a specific category of artificial intelligence (AI), generative artificial intelligence (GenAI) generates new content that resembles what is created by humans. The rapid development of GenAI systems has created a huge amount of new data on the Internet, posing new challenges to current computing and communication frameworks. Currently, GenAI services rely on the traditional cloud computing framework due to the need for large computation resources. However, such services will encounter high latency because of data transmission and a high volume of requests. On the other hand, edge-cloud computing can provide adequate computation power and low latency at the same time through the collaboration between edges and the cloud. Thus, it is attractive to build GenAI systems at scale by leveraging the edge-cloud computing paradigm. In this overview paper, we review recent developments in GenAI and edge-cloud computing, respectively. Then, we use two exemplary GenAI applications to discuss technical challenges in scaling up their solutions using edge-cloud collaborative systems. Finally, we list design considerations for training and deploying GenAI systems at scale and point out future research directions.

翻译：作为人工智能（AI）的一个特定范畴，生成式人工智能（GenAI）能够生成与人类创作相似的新内容。GenAI系统的快速发展在互联网上产生了海量新数据，对当前的计算与通信框架提出了新挑战。目前，由于需要大量计算资源，GenAI服务依赖于传统的云计算框架。然而，此类服务因数据传输和请求量庞大而面临高延迟问题。另一方面，边缘-云计算通过边缘端与云端的协作，能够同时提供充足的计算能力和低延迟。因此，利用边缘-云计算范式构建大规模GenAI系统极具吸引力。本综述文章分别回顾了GenAI与边缘-云计算的最新进展，随后通过两个典型的GenAI应用案例，探讨了利用边缘-云协同系统扩展其解决方案所面临的技术挑战。最后，我们列出了大规模训练与部署GenAI系统的设计考量，并指出了未来研究方向。