> This page is for Self-Hosted, version v3.0 (default).
> For other versions, use one of these documentation indexes:
> - v3.0 (default): https://docs.nvidia.com/run-ai/self-hosted/v3.0/llms.txt
> - v2.26: https://docs.nvidia.com/run-ai/self-hosted/v2.26/llms.txt
> - v2.25: https://docs.nvidia.com/run-ai/self-hosted/v2.25/llms.txt
> - v2.24: https://docs.nvidia.com/run-ai/self-hosted/v2.24/llms.txt

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> For a complete documentation index, see https://docs.nvidia.com/run-ai/llms.txt.
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# Over-Quota GPU Usage with Fairness and Preemption

See how NVIDIA Run:ai manages over-quota GPU usage while maintaining fairness and resource guarantees across clusters.

<iframe width="700" height="394" src="https://www.youtube.com/embed/i7ufKl6_8jc" frameBorder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowFullScreen />

<Note>
**Note**

This video was recorded using NVIDIA Run:ai version 2.25.9. The user interface, features, and workflows may differ in newer releases. For the latest information, refer to the current documentation.
</Note>

## What You'll Learn:
* How teams can temporarily use extra GPU capacity when available
* How NVIDIA Run:ai restores fairness when guaranteed resources are needed
* How preemption works in shared GPU environments
* How over-quota scheduling helps maximize GPU utilization without compromising guarantees

## Related Documentation:
Follow the validated quickstart in the product documentation: [Over Quota, Fairness and Preemption](/run-ai/self-hosted/documentation/platform-management/runai-scheduler/scheduling/quick-starts/over-quota)