OpenClaw GPU Deployment: Connect a Cloud GPU to Your AI Assistant

An OpenClaw gateway and its model can run on separate machines: an ordinary server hosts the gateway, while a GPU server handles inference. If you use a hosted model API, you do not need to rent your own GPU. This guide covers Ollama, remote connections and access to project files.

Does OpenClaw itself require a GPU?

Calling a hosted model API does not require your gateway to host that model or have its own GPU. Self-hosting model inference is a different decision. A GPU can accelerate suitable local models, but the required memory depends on the model, runtime and workload. OpenClaw's local-model guidance emphasizes testing full agent turns, including tools and history, rather than assuming that a short successful prompt proves the model is suitable.

Start with the simplest model connection

For a first local setup, install supported versions of OpenClaw and Ollama on the chosen machine, then use Ollama's documented launcher:

ollama launch openclaw

The launcher guides model selection and configuration. Its OpenClaw integration guide also documents a configuration-only mode:

ollama launch openclaw --config

Choose a model that fits the hardware and supports the capabilities your tasks require. A fluent chat response does not prove reliable tool use. Start with a small sandbox workspace and an inexpensive task, such as reading a sample document and producing a summary.

Move inference to a rented GPU

For a split deployment, prepare the GPU machine and start the model runtime there. Establish private network access from the gateway host, or use an authenticated tunnel. Keep the model endpoint and management interface off the unrestricted public internet. Check that the GPU is actually being used before troubleshooting the assistant layer.

OpenClaw provides an Ollama remote-host recipe. Set the Ollama provider's base URL to the endpoint reachable from the gateway, use the native Ollama API setting, and select the exact model ID reported by the server. Match the declared context budget to what the runtime is configured and able to support. A local placeholder API key is not network authentication.

Check the model catalog from the gateway host:

openclaw models list --provider ollama

If the list is empty, investigate the endpoint, connectivity and provider configuration first. If the model is listed but agent turns fail, inspect tool support, context limits, timeouts and model-server logs. Avoid changing all of these at once; one controlled change makes the failure easier to explain.

Keep the workspace and the model server distinct

Connecting a GPU inference endpoint does not automatically give the assistant access to your laptop's project directory. The assistant can work only with files and tools available through its configured workspace and authorized execution environment. Put a copy of the project in that environment, or configure an appropriate authorized connection, before asking it to inspect files.

For product planning, a useful first assignment is to read a small repository, identify user-facing flows and write a draft requirements document without modifying application code. Review whether the answer cites real project paths and describes the implementation accurately. Increase permissions only when the workflow needs them.

Validate the whole agent loop

1. Run a short request and a realistic long-context request.
2. Check that a permitted tool is actually called and its result is used.
3. Confirm that prohibited files and commands remain inaccessible.
4. Restart the GPU service and check how the gateway recovers.
5. Measure memory, response time and cost per completed task.

Run the documented security review before connecting real accounts or sensitive files:

openclaw security audit

OpenClaw's security model treats a gateway as a trust boundary. Separate unrelated users and credentials instead of assuming one shared assistant provides isolation between them.