在共享GPU基础设施上运行隔离租户Kubernetes集群的教程
原标题：How to Run Isolated Tenant Kubernetes Clusters on Shared GPU Infrastructure
AI 摘要
NVIDIA技术博客发布了一篇教程，介绍如何在共享GPU基础设施上运行隔离的租户Kubernetes集群。该方案使用单一控制平面集群和GPU池，通过KAI Scheduler和vCluster两个开源工具，为每个团队提供独立的Kubernetes控制平面（包括API服务器、控制器、数据存储、同步器和调度器），同时共享底层GPU硬件。教程以一块NVIDIA L40S GPU和三个团队为例，展示了如何实现团队自治和资源配额管理，并验证各团队只能看到自己的工作负载。该模式可扩展到数百个GPU节点和数十个团队。
正文节选
Running a dedicated Kubernetes cluster per team often results in more isolation than an organization requires. While one cluster can be successfully shared across many teams, the coordination costs increase as the number of teams grows. Challenges include conflicting CRD versions, overlapping RBAC, and no clean way to carve GPU capacity into team-level budgets. At a certain scale, teams might start asking for their own clusters just to regain autonomy. This post provides a pattern that preserves