***

title: Running Agents on Brev
description: >-
Deploy AI agents on NVIDIA Brev CPU instances for sandboxed code execution,
web tasks, and more.
--------------------

Deploy AI agents on Brev CPU instances for sandboxed execution environments. CPU instances provide isolated, cost-effective compute for agent workloads that don't require GPU acceleration.

## Prerequisites

* A Brev account ([sign up](https://brev.nvidia.com))
* Brev CLI installed ([Quickstart](/docs/getting-started/quickstart))

## Deploy an Agent Sandbox

### Option A: Deploy from a Launchable

Use a pre-built Launchable to get started quickly:

* **NemoClaw Agent** — the NVIDIA agent framework for tool-use and code execution
* **Generic Agent Sandbox** — a blank CPU sandbox for custom agent deployments

Browse available Launchables at [brev.nvidia.com](https://brev.nvidia.com/environment/new/public). Select a CPU instance type and deploy.

### Option B: Provision Manually

Create a CPU instance through the [Brev Console](https://brev.nvidia.com) or CLI:

```bash
brev create my-agent
```

Select a CPU instance type (no GPU) when prompted, or specify one in the [Brev Console](https://brev.nvidia.com).

### Connect and Verify

<Steps>
  <Step title="Connect to your instance">
    After the instance is running, connect with the CLI:

    ```bash
    brev shell my-agent
    ```
  </Step>

  <Step title="Verify the agent is running">
    Check that your agent process is active:

    ```bash
    # Check running processes
    ps aux | grep agent

    # Check Docker containers if using containerized agents
    docker ps
    ```
  </Step>
</Steps>

## CPU vs GPU for Agents

| Use Case                    | Recommended  | Why                               |
| --------------------------- | ------------ | --------------------------------- |
| Code execution / sandboxes  | CPU instance | No GPU needed, lower cost.        |
| Web browsing / scraping     | CPU instance | Network-bound, not compute-bound. |
| Tool use / API calls        | CPU instance | Minimal compute requirements.     |
| LLM inference (self-hosted) | GPU instance | Models require GPU acceleration.  |
| Vision / image processing   | GPU instance | GPU accelerates image operations. |
| Training / fine-tuning      | GPU instance | Requires GPU compute.             |

## What's Next?

<CardGroup cols={1}>
  <Card title="CPU Instances" icon="server" href="/concepts/cpu-instances">
    Learn about CPU instance lifecycle, supported types, and billing.
  </Card>

  <Card title="Launchables" icon="rocket" href="/concepts/launchables">
    Create and share one-click deployable agent environments.
  </Card>
</CardGroup>