KAI-Scheduler provides a method for running isolated tenant Kubernetes clusters on shared GPU infrastructure. This approach allows multiple teams to operate within a single Kubernetes cluster while maintaining isolation. It addresses challenges such as conflicting CRD versions, overlapping RBAC, and the lack of a clean way to allocate GPU capacity to team-level budgets. This method reduces the need for separate clusters and lowers coordination costs.

The solution leverages vCluster to manage GPU resources efficiently. It enables teams to maintain autonomy without the overhead of managing separate Kubernetes clusters. This approach is particularly beneficial in environments where multiple teams require access to GPU resources but do not need full isolation. The use of vCluster ensures that each team can operate within its own namespace while sharing the same underlying infrastructure.

The demo environment uses a single NVIDIA L40S GPU with 40 vCPUs, 160 GiB RAM, and 256 GiB disk. This setup includes 48 GB VRAM and runs on Ubuntu 24.04.4. The tutorial prerequisites and notes emphasize the importance of using a reproducible environment for users with limited resources. This configuration ensures that the solution can be tested and implemented effectively in various settings.

The method has implications for cost, lock-in, and governance. By enabling shared GPU infrastructure, it reduces the overall cost of maintaining multiple Kubernetes clusters. However, it may introduce vendor lock-in if the solution is tightly coupled with specific GPU hardware or cloud providers. Governance becomes more complex as teams need to coordinate resource allocation and ensure compliance with organizational policies.

The solution is still under development and shows promise for improving efficiency in shared GPU environments. It provides a scalable approach for managing Kubernetes clusters while maintaining isolation. As the technology evolves, it may address current limitations and further reduce the coordination costs associated with managing multiple teams on shared infrastructure.