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Sharing one lab GPU across students—how do we schedule fairly?

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(@pandaadmin)
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Topic starter   [#109]

[Official FAQ] Corrections and follow-up questions welcome.

One GPU cannot comfortably host many huge-context sessions at once. You need queues, quotas, and clear norms.

Patterns:

  • Reservation calendar plus API limits on concurrency and max tokens.
  • Tiered service: small models walk-up; large models by appointment.
  • Isolate users/containers so one job cannot OOM everyone.

Publish quotas publicly; technology alone will not stop social friction.



   
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