  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.
