Private GPU cloud for an AI research lab
Stood up a secure, multi-tenant private GPU cloud so research teams could train on demand instead of queuing for public-cloud capacity.
An AI research lab was losing momentum waiting on scarce public-cloud GPU capacity, and paying premium rates for it. They needed their own secure, multi-tenant GPU cloud — high utilization, strong isolation between teams, and an MLOps layer that let researchers start training in days, not after a procurement cycle.
We designed the cluster — accelerators, fabric, and storage — as one system so GPUs stayed fed, then layered orchestration and MLOps on top with policy-based isolation between research teams. The result is a private GPU cloud that sustains high utilization and lets researchers self-serve capacity instead of queuing.
What we delivered
Private GPU cloud
Secure, multi-tenant GPU-as-a-service in the lab's own facility.
Fabric & storage
Interconnect and storage tiers sized to keep accelerators fed.
Orchestration & MLOps
Scheduling and policy-based isolation across research teams.
Utilization telemetry
Observability that turns installed capacity into sustained throughput.
The result
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