Secure Multi-Tenant GPU-as-a-Service on Kubernetes: Architecture, Isolation, and Reliability at Scale

GPUs are a core feature of modern cloud platforms, used to support a wide range of machine learning training, inference, analytics, and simulation workloads. To support this diverse demand, GPUs can no longer be dedicated to a single team or application. Dedicated GPU solutions have quickly become infeasible and very expensive.

To meet this demand, organizations are increasingly looking to shared platforms, where many teams can directly consume GPU resources from a shared Kubernetes cluster. GPU-as-a-Service (GPUaaS) platforms provide this capability.