Guide
How teams share a GPU cluster without fighting over it
Every organization with more than one AI team runs into the same questions. These are the tools that answer them.
By the C-Gen.AI engineering team
Fixed slices leave GPUs idle
Splitting a cluster into a fixed slice per team is simple and feels fair. In practice, one team’s slice sits idle while another team’s jobs wait in the queue, and GPUs everyone is paying for go unused.
Quotas, priorities and fair share
Schedulers offer better tools. Quotas cap how much each team can hold at once. Priorities decide whose work goes first. Fair-share scheduling runs jobs from teams that have used less than their share first, while teams over their share still run whenever GPUs would otherwise sit idle.
Protect what can’t wait
A production endpoint can’t sit in a queue behind an experiment. Mark the capacity it depends on as guaranteed, and let every other workload share the rest according to its priority.
Make usage visible
Sharing works when people trust it. Reports of GPU hours by project, team and user replace arguments with data, show where capacity is really short and give finance a basis for chargeback.
