> For the complete documentation index, see [llms.txt](https://run-ai-docs.nvidia.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://run-ai-docs.nvidia.com/self-hosted/2.25/resources/videos/over-quota-gpu-usage-with-fairness-and-preemption.md).

# 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.

{% embed url="<https://www.youtube.com/watch?v=i7ufKl6_8jc>" %}

{% hint style="info" %}
**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.
{% endhint %}

#### **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](https://run-ai-docs.nvidia.com/self-hosted/platform-management/runai-scheduler/scheduling/quick-starts/over-quota)


---

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