> 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

> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/run-ai/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/run-ai/_mcp/server.

# How to Allocate GPU Resources Across Teams

## Resource Management Across Teams

Learn how NVIDIA Run:ai helps teams share GPU resources efficiently across departments and projects.

<iframe width="700" height="394" src="https://www.youtube.com/embed/UFArO8_aAYw" 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 2.24.18. 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:
* Configure quotas for departments and projects
* Set workload priorities across teams
* Allocate GPU resources fairly
* Improve cluster utilization
* Maintain governance across shared infrastructure
* Support operational flexibility for AI workloads