KubeCon + CloudNativeCon + Open Source Summit + AI_dev China 2024

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.

对于大型语言模型（LLM）推理，单个数据中心或云区域内的GPU资源通常无法满足所有用户需求。此外，对于最终用户来说，跨多个地理区域部署是为了提供最佳用户体验。然而，在多个地区管理模型分发、同步和一致性会带来新的挑战。为了解决这个问题，OCM和Fluid社区合作，通过OCM的多集群应用部署能力和Fluid的数据编排能力自动化实现推理应用的多地区分发。这种自动化促进了大型模型的跨地区分发和预热，提高了模型部署和升级的效率。