> 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.

# Deploy NVIDIA Cloud Functions (NVCF) in NVIDIA Run:ai

NVIDIA Cloud Functions (NVCF) is a serverless API platform designed to deploy and manage AI workloads on GPUs. Through its integration with NVIDIA Run:ai, NVCF can be deployed directly onto NVIDIA Run:ai-managed GPU clusters. This allows users to take advantage of NVIDIA Run:ai's scheduling, quota management, and monitoring features. See [Supported features](/run-ai/self-hosted/documentation/workloads-in-nvidia-run-ai/using-inference/nvcf#supported-features) for more details.

This guide provides the required steps for integrating NVIDIA Cloud Functions with the NVIDIA Run:ai platform.

## Workload Priority

By default, inference workloads in NVIDIA Run:ai are assigned a priority of `very-high`, which is non-preemptible. This behavior ensures that inference workloads, which often serve real-time or latency-sensitive traffic, are guaranteed the resources they need and will not be disrupted by other workloads. For more details, see [Workload priority control](/run-ai/self-hosted/documentation/platform-management/runai-scheduler/scheduling/workload-priority-control).

<Note>

**Note**

Changing the priority is not supported for NVCF workloads.

</Note>

## Setup

Follow the official instructions provided in the [NVIDIA Cloud Functions](https://docs.nvidia.com/cloud-functions/user-guide/latest/cloud-function/overview.html) documentation.

### Setting up a Cluster in NVCF

Cloud Functions administrators can install the NVIDIA Cluster Agent to enable existing GPU clusters as deployment targets for NVCF functions. Once installed, the cluster appears as a deployment option in the API and Cloud Functions menu, allowing authorized functions to deploy on it. See [Cluster Setup & Management](https://docs.nvidia.com/cloud-functions/user-guide/latest/cloud-function/cluster-management.html) to register, configure and verify the cluster.

### Setting up a Project in NVIDIA Run:ai

Once the cluster is registered to NVCF and appears as ready, create a project in the NVIDIA Run:ai UI with an **NVCF namespace**:

1. Follow the instructions detailed in [Projects](/run-ai/self-hosted/documentation/platform-management/manage-ai-initiatives/organization/projects) to create a new project.
2. When setting the **Namespace**, choose **"Enter existing namespace from the cluster"** and enter **`nvcf-backend`**.
3. Assign the necessary resource quotas to the project.

<Note>

**Note**

* This is the designated NVIDIA Run:ai project for all NVCF functions; other projects will not be used.
* NVCF is assigned to a specific project and adheres to the project's resource quota. Ensure that your project has sufficient quota allocated to accommodate.

</Note>

## Deploying a Function

In NVCF, function creation defines the code and resources while deployment registers the function to a GPU cluster, making it available for execution:

1. Create the function as detailed in [Function Creation](https://docs.nvidia.com/cloud-functions/user-guide/latest/cloud-function/function-creation.html#function-creation).
2. Deploy the function as detailed in [Function Deployment](https://docs.nvidia.com/cloud-functions/user-guide/latest/cloud-function/function-deployment.html).

<Note>

**Note**

Using a custom Helm chart when creating a [Cloud Function](https://docs.nvidia.com/cloud-functions/user-guide/latest/cloud-function/function-creation.html) is not supported.

</Note>

## Managing and Monitoring

After the NVCF function is deployed, it is added to the [Workloads](/run-ai/self-hosted/documentation/workloads-in-nvidia-run-ai/workloads) table, where it can be managed and monitored:

* Monitor resource usage, performance, and execution status.
* Manage workload lifecycle, including scaling, logging, and troubleshooting.

## Supported Features

| NVIDIA Run:ai Functionality                                                                                                                    | NVCF |
| ---------------------------------------------------------------------------------------------------------------------------------------------- | :--: |
| [Fairness](/run-ai/self-hosted/documentation/platform-management/runai-scheduler/scheduling/concepts-and-principles#fairness-fair-resource-distribution)                |   v  |
| [Priority and preemption](/run-ai/self-hosted/documentation/platform-management/runai-scheduler/scheduling/concepts-and-principles#priority-and-preemption)             |   v  |
| [Over quota](/run-ai/self-hosted/documentation/platform-management/runai-scheduler/scheduling/concepts-and-principles#over-quota)                                       |   v  |
| [Node pools](/run-ai/self-hosted/documentation/platform-management/manage-ai-initiatives/resources/node-pools)                                            |   v  |
| [Bin packing / Spread](/run-ai/self-hosted/documentation/platform-management/runai-scheduler/scheduling/concepts-and-principles#placement-strategy-bin-pack-and-spread) |   v  |
| [Multi-GPU fractions](/run-ai/self-hosted/documentation/platform-management/runai-scheduler/resource-optimization/gpu-fractions/memory-fractions)                                            |      |
| [Multi-GPU dynamic fractions](/run-ai/self-hosted/documentation/platform-management/runai-scheduler/resource-optimization/gpu-fractions/dynamic-memory-fractions)                            |      |
| [Gang scheduling](/run-ai/self-hosted/documentation/platform-management/runai-scheduler/scheduling/concepts-and-principles#gang-scheduling)                             |   v  |
| [Monitoring](/run-ai/self-hosted/documentation/installation-and-configuration/operate/observability/system-monitoring)                                                                       |   v  |
| [RBAC](/run-ai/self-hosted/documentation/platform-management/authentication/overview#role-based-access-control-rbac-in-run-ai)                                         |   v  |
| Workload awareness                                                                                                                             |   v  |
| [Workload submission](/run-ai/self-hosted/documentation/workloads-in-nvidia-run-ai/workloads)                                                                                                         |      |
| [Workload actions (stop/run)](/run-ai/self-hosted/documentation/workloads-in-nvidia-run-ai/workloads)                                                                                                 |      |
| [Workload Policies](/run-ai/self-hosted/documentation/platform-management/policies/native-workload-policies)                                                                   |      |
| [Scheduling rules](/run-ai/self-hosted/documentation/platform-management/policies/scheduling-rules)                                                                     |      |