  The advancement of generative artificial intelligence (GAI) has driven
revolutionary applications like ChatGPT. The widespread of these applications
relies on the mixture of experts (MoE), which contains multiple experts and
selectively engages them for each task to lower operation costs while
maintaining performance. Despite MoE, GAI faces challenges in resource
consumption when deployed on user devices. This paper proposes mobile edge
networks supported MoE-based GAI. We first review the MoE from traditional AI
and GAI perspectives, including structure, principles, and applications. We
then propose a framework that transfers subtasks to devices in mobile edge
networks, aiding GAI model operation on user devices. We discuss challenges in
this process and introduce a deep reinforcement learning based algorithm to
select edge devices for subtask execution. Experimental results will show that
our framework not only facilitates GAI's deployment on resource-limited devices
but also generates higher-quality content compared to methods without edge
network support.
