Abstract
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. Â In this overview paper, we review recent developments in
GenAI and edge-cloud computing, respectively. Â 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.Â