How to Run Isolated Tenant Kubernetes Clusters on Shared GPU Infrastructure
Quick Answer
NVIDIA's tutorial demonstrates how to run isolated Kubernetes clusters for multiple teams on a shared GPU infrastructure using KAI Scheduler and vCluster, allowing teams to maintain autonomy without hardware fragmentation.
Quick Take
This setup enables three teams to run their own workloads on a single NVIDIA L40S GPU while ensuring resource isolation and independent control planes.
Key Points
- KAI Scheduler optimizes GPU resource allocation for AI workloads in large-scale environments.
- vCluster provides fully isolated tenant clusters while sharing the same underlying nodes.
- Each team can manage their own CRDs and RBAC without interfering with others.
- The tutorial is designed for a single NVIDIA L40S GPU shared among three teams.
- The setup is scalable to larger clusters with hundreds of GPU nodes.
DeepSignal Analysis
What happened
NVIDIA's tutorial outlines a method for running isolated Kubernetes clusters on shared GPU infrastructure, specifically using KAI Scheduler and vCluster. This approach allows multiple teams to operate independently without the need for separate hardware, facilitating resource management and autonomy.
Key evidence
- The tutorial demonstrates how three teams can share a single NVIDIA L40S GPU while maintaining isolated Kubernetes control planes, ensuring that each team only accesses its own workloads.
- KAI Scheduler is designed for optimizing GPU resource allocation for AI workloads and can manage large-scale GPU clusters, dynamically allocating resources based on workload demands.
- vCluster provisions fully isolated tenant clusters that share underlying nodes and hardware, providing each team with its own API server, CRDs, and RBAC, similar to a dedicated cluster.
Why it matters
This setup addresses the common challenge of resource fragmentation in organizations that require multiple teams to run their workloads. By allowing teams to share GPU resources while maintaining control and isolation, organizations can optimize their infrastructure usage and reduce operational overhead.
Source Excerpt
Running a dedicated Kubernetes cluster per team often results in more isolation than an organization requires. While one cluster can be successfully shared…
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