For large language model (LLM) inference, GPU resources within a single data center or cloud region often cannot meet all user demands. Additionally, for the end-users, deploying across multiple geographic regions is necessary to provide an optimal user experience. However, managing model distribution, synchronization, and consistency across multiple regions presents new challenges. To address this, the OCM and Fluid communities have collaborated to automate the multi-region distribution of inference applications through OCM's multi-cluster application deployment capabilities, combined with Fluid's data orchestration capabilities. This automation facilitates the cross-regional distribution and pre-warming of large models, enhancing the efficiency of model deployment and upgrades.