Multi-tenant Kubernetes with GPU sharing is a compelling model for AI infrastructure, but it requires careful design to balance performance with security. This session shows how to build a secure and scalable environment where multiple teams can run GPU workloads without compromising isolation or access control. We’ll cover open source options like KAI Scheduler and vCluster and demonstrate how to integrate external tools for secret management and dynamic access policies, all within an architecture that lets teams feel like they have their own cluster—while behind the scenes, resources are efficiently pooled and shared.