GPUs are expensive when idle and scarce when everyone needs them. Separate training and inference pools, set quotas, and autoscale with cool-downs that match job patterns.
Use gang scheduling or queues where multi-node jobs require it. Monitor utilization and wasted allocation as first-class FinOps metrics.
Prefer spot/preemptible where checkpoints make interruption safe. Keep proprietary models and data on secured storage with tight identity.
MLOps platforms earn trust when performance and cost are both visible.
Expose cost per job and per team in the same portal developers use to request GPUs.
Secure model artifacts and datasets with tight identity—GPUs often attract broad access habits.
Plan multi-instance GPU packing only when your scheduler and workload profiles support it safely.
Key takeaways
- Separate training and inference pools; set quotas and cool-downs that match jobs.
- Treat GPU utilization and idle allocation as FinOps SLIs.
- Use spot/preemptible where checkpoints make interruption safe.
FAQ
How do we stop idle GPU burn?
Autoscaling with appropriate cool-downs, idle detection, and team quotas. Always-on GPU nodes without owners are the usual culprit.
Should training share inference nodes?
Usually no. Noisy training jobs wreck latency SLOs. Isolate pools and schedule deliberately.