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Home/Design services/GPU as a service
One GPU pool, many teams, fair scheduling and chargeback
Design a shared GPU service across research groups, departments or customers. Quotas, time-slicing, job queues and per-team cost reporting.
What you get
Designed for your environment
MIG and time-slicing
Partition A100 or H100 nodes into slices for smaller workloads without wasting capacity.
Fair scheduling
Per-team quotas and fair-share scheduling. No team can starve another.
Chargeback reporting
Per-team GPU-hour and watt-hour consumption, exportable for billing.
Kubernetes or bare metal
Kubernetes with GPU operator for containerised workloads, or bare-metal Slurm.
Deliverables
What lands at the end of the engagement
Working environment
Built, commissioned and tested against the design.
Runbooks
Day-one operations and common failure procedures.
Evidence pack
Configuration, test results and controls for your audit team.
Named team
The engineers who built it, reachable afterwards.
FAQ
Common questions
Can different teams use different frameworks?
Yes. Container isolation means each team brings their own software stack.
How do you handle competing priorities?
Priority classes, preemption and guaranteed reservation quotas are all configurable.
Next step
Scope this service today.
Fixed price, defined scope, handed over working.