Every organization running AI workloads on Kubernetes faces the same question: do we share GPUs across teams, or give each team dedicated resources? The answer isn’t obvious — and getting it wrong means either wasted GPUs or frustrated teams. This session provides a practical framework for deciding which GPU workloads can share infrastructure, which need isolation, and how to evolve your approach as AI adoption grows.