How to Run Isolated Tenant Kubernetes Clusters on Shared GPU Infrastructure

A New Architecture for GPU Multi-Tenancy

A new operational pattern has emerged for managing shared GPU resources, combining the open-source tools KAI Scheduler and vCluster to address a persistent challenge in AI development: balancing team autonomy with infrastructure efficiency. This architecture allows multiple teams to operate in fully isolated Kubernetes clusters—each with its own control plane, RBAC, and CRDs—while consuming resources from a single, shared pool of physical GPUs. The approach directly tackles the high operational costs and underutilization that result from provisioning dedicated hardware clusters for individual teams, a common practice that often creates more isolation than is functionally necessary.

Technical Breakdown

The solution hinges on the distinct roles of the two core components. vCluster provisions the logically separate tenant environments, creating virtualized Kubernetes clusters that are indistinguishable to the end-user from a dedicated setup. This allows teams, such as NLP and computer vision groups using different toolchains, to have cluster-admin access and install conflicting CRD versions without impacting one another. Underneath this virtualization layer, KAI Scheduler manages the sophisticated, topology-aware scheduling of GPU resources across all tenants. It integrates with the NVIDIA GPU Operator to dynamically allocate GPU fractions based on a hierarchical queue system defined by custom resources.

• Virtualization Layer: vCluster creates isolated tenant clusters with their own API servers and datastores.
• Resource Management: KAI Scheduler handles topology-aware GPU scheduling with per-team quotas and dynamic, burstable allocation.
• Hardware Foundation: The system runs on a shared host cluster with the NVIDIA GPU Operator installed, enabling Container Device Interface (CDI) for GPU access.
• Scalability: While demonstrated on a single NVIDIA L40S GPU, the architecture is designed to scale to thousands of nodes and a high throughput of workloads.

Impact on MLOps Infrastructure

For platform engineering and MLOps teams, this model offers a significant improvement in managing expensive GPU hardware. By abstracting the physical infrastructure from the tenant environment, organizations can achieve higher resource density and utilization, driving down the total cost of ownership for their AI platforms. This effectively eliminates the need for teams to request entirely separate clusters just to regain control over their development environment. The pattern fosters a more scalable and cost-effective approach to building internal AI platforms, allowing infrastructure to be managed as a unified utility while still providing the isolated, self-service experience that development teams require to innovate quickly.

This combination of virtualized control planes with topology-aware GPU scheduling offers a pragmatic blueprint for enterprises to achieve high-density, multi-tenant AI infrastructure, moving beyond the false choice between team autonomy and hardware efficiency.