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

Kubernetes offers many ways to share GPUs, but a single, cluster-wide scheduler often forces trade-offs between utilization, stability, and team autonomy. This talk shows how vCluster makes the NVIDIA Kubernetes AI Scheduler (KAI) run as an opt-in service for each tenant—so platform teams can raise GPU density while keeping operations predictable.

What We’ll Cover

Problem statement – why mixed workloads leave GPUs under-used and complicate on-call.
vCluster fundamentals – lightweight control planes that isolate scheduling logic, not hardware.
KAI at a glance – fractional GPU allocation, gang queues, topology awareness.
Live demonstration – two vClusters on one host

Key Takeaways

A reproducible pattern for running different schedulers side-by-side.
Practical steps to increase GPU utilisation without adding more clusters.
An isolation model that lets teams experiment safely.