From Space and Power to Orchestrated Workloads: How Modern AI Infrastructure Is Built and Operated

Ask any team running a large GPU fleet two questions, what do we actually have, and who is using it right now? The answers usually come from two systems that don’t agree. The physical foundation, racks, power, cabling, and the IP fabric, lives in one place. The orchestration layer lives in another. That gap is where AI infrastructure projects stall.

This webinar, co-hosted by vCluster and NetBox Labs, traces the full path from space and power to orchestrated workloads in two parts. The first is the physical and network substrate every GPU depends on. NetBox serves as the system of record here, and an accurate model of that foundation becomes the ground truth everything else builds on. The second is the tenant isolation layer that sits on top. vCluster turns the fleet into isolated tenant clusters, giving each AI team its own Kubernetes control plane without multiplying the underlying infrastructure. Keeping those two layers in sync as the fleet grows is the thread that connects them.

The timing matters. Building a GPU fleet is hard; running it well on day two is harder. As GPU fleets scale into the thousands, the cost of a blurry picture compounds. Idle capacity hides in plain sight, tenants collide, and audits turn into guesswork. The teams that operate AI infrastructure well are the ones who treat the physical record and the orchestration layer as one system, not two.

Key Takeaways

A clear mental model of the AI infrastructure stack, from physical substrate to tenant-isolated Kubernetes
How an accurate system of record makes safe multi-tenancy and high GPU utilization possible
Where the physical and network layer ends and the orchestration layer begins, and why both must stay in sync
Practical patterns for operating a multi-tenant AI factory on day two, not just standing one up

This is for platform engineers, infrastructure architects, and anyone responsible for building or running GPU infrastructure at scale.