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World Congress 2026 Europe • Jul 10, 2026 • Session details

Instant KAI Sandboxes with vCluster: Multi-Tenant, Multi-Scheduler GPU Sharing

Piotr Zaniewski

Stop choosing between GPU utilization and Kubernetes stability. Discover how combining vCluster and NVIDIA KAI creates instant, multi-tenant AI sandboxes. Run different schedulers safely and maximize hardware density.

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#1 about 4 min

Challenges of shared Kubernetes clusters for AI workloads

Shared global components complicate software upgrades and limit scheduling flexibility for AI workloads.

#2 about 2 min

How the Kubernetes AI scheduler enables fractional GPU sharing

The dynamic resource allocator divides graphics hardware between specific workloads to improve utilization.

#3 about 2 min

Isolating control planes with virtual Kubernetes clusters

Running the application programming interface server inside a pod reduces the operational blast radius.

#4 about 3 min

Navigating the spectrum of Kubernetes multi-tenancy models

Virtual clusters balance full infrastructure isolation against sharing global services like ingress controllers.

#5 about 1 min

Exploring the internal architecture of vcluster components

The sinker component synchronizes specific resources between virtual environments and the underlying host platform.

#6 about 2 min

Verifying hardware access and exploring AI inference scaling

Large-scale machine learning processing requires robust cluster management software to efficiently allocate computational power.

#7 about 2 min

Demonstrating GPU workloads by generating text haikus

Generating haikus via a deployed language model verifies that the cluster correctly handles GPU-accelerated workloads.

#8 about 2 min

Configuring virtual clusters to run custom AI schedulers

Defining custom configurations encapsulates specialized scheduling logic within an isolated tenant environment.

#9 about 3 min

Deploying and connecting to the sandboxed control plane

Using terminal applications verifies the correct installation and functionality of the isolated control plane pods.

#10 about 2 min

Allocating fractional GPU resources using the isolated scheduler

Applying custom configuration files allows individual pods to consume exact percentages of shared graphics hardware.

#11 about 2 min

Provisioning multiple virtual clusters for concurrent tenant configurations

Deploying simultaneous isolated environments satisfies conflicting software version requirements across different engineering teams.

#12 about 2 min

Scaling virtual clusters to maximize hardware cost savings

Running thousands of encapsulated sandboxes on shared host nodes drastically reduces organizational computing costs.

#13 about 3 min

Verifying concurrent versions of isolated AI schedulers

Checking cluster stateful sets confirms that multiple scheduler versions operate independently without software interference.

#14 about 1 min

Documentation and community resources for cluster management tools

Directing developers to official documentation and community channels provides support for troubleshooting complex integrations.

#15 about 3 min

Configuring virtual clusters for multi-node GPU parallelization

Targeting dedicated infrastructure allows specialized workloads to distribute execution across multiple discrete computing devices.

Matching moments

1:54 min

Speaker background and open source Kubernetes edge computing projects

Gaurav Gahlot Gaurav Gahlot · World Congress 2026 Europe

4:49 min

Structuring compute and data services for AI models

Radu Vunvulea Radu Vunvulea · World Congress 2025

2:04 min

Optimizing and deploying containerized AI inference workloads

Ankit Patel Ankit Patel · World Congress 2024

1:37 min

Unlocking direct GPU access within managed Kubernetes platforms

Kevin Klues Kevin Klues

5:07 min

Actions Runner Controller architecture and cluster isolation

Bassem Dghaidi · LIVE

1:30 min

Overlooked AI infrastructure and operational deployment barriers

Stan Girard Stan Girard · World Congress 2024

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