OpenClaw with Ollama on a VPS: Build Your Own AI Assistant

An AI assistant becomes more useful when it can work with your files, follow a repeatable process and remain available after you close your laptop. Running OpenClaw with Ollama on a VPS gives you a way to build that setup on infrastructure you control.

OpenClaw manages the assistant and its tools. Ollama runs the language model that interprets your instructions and produces responses. A virtual private server provides an always-available environment for the software.

However, there is an important decision to make before installing anything: will your VPS run the AI model itself, or will it connect to a cloud-hosted model?

That choice affects memory requirements, response times, costs and where your prompts are processed. This guide explains the difference and walks through a straightforward installation with OpenClaw and Ollama running directly on the same Linux VPS.

What Can You Use This Setup For

Start with a small, clearly defined task. For example, you might want an assistant that reads a folder of approved project notes and prepares a daily summary.

Other useful starting points include:

• Turning meeting notes into organised action lists.
• Drafting documentation from selected project files.
• Summarising development logs that contain no sensitive credentials.
• Preparing content outlines from your own research.
• Helping you understand code within a limited project workspace.

These are proposed workflows, rather than features that become reliable simply because you install the software. Each workflow needs suitable tools, permissions and testing.

For your first project, choose a task where you can easily check the answer. An assistant that summarises three known documents is much easier to evaluate than one given unrestricted access to an entire business.

Choose Where Your AI Model Will Run

Running the model locally on your VPS

With a local model, Ollama performs inference—the process of generating an answer—on your server.

This gives you control over the model files and the computing environment. However, your server must have enough memory and processing capacity for both the model and its working context.

A model’s download size is not its complete memory requirement. Longer conversations, larger documents and simultaneous requests can require additional resources.

Connecting to a cloud model

A cloud model runs on a provider’s infrastructure while your VPS hosts the assistant.

This can reduce the local hardware needed for inference. It also means prompts are processed outside your VPS, and the provider’s usage limits and charges may apply.

Ollama supports both local and cloud-backed workflows. Select the route deliberately rather than assuming that installing Ollama automatically makes every request local.

What Should You Look for in a VPS

For this guide, use a supported Ubuntu installation with SSH access, a non-root account with sudo privileges, and systemd.

Before choosing a plan, consider the following:

• Memory: Capacity for the selected model, context window, operating system and OpenClaw.
• CPU: Response speed under your actual workload.
• Storage: Space for model downloads, logs, updates and backups.
• GPU access: Whether a compatible GPU is available when your workload needs acceleration.
• Administration: SSH access and permission to install background services.

DigitalBerg’s VPS hosting page lists Linux options and root access. Use those capabilities as a starting point, then confirm that the selected configuration suits your intended model.

You can also explore OpenStack KVM cloud VPS hosting when comparing infrastructure options.

Do not choose an AI hosting plan on storage capacity alone. Having enough disk space to download a model does not establish that the server can run it at an acceptable speed.

Install OpenClaw and Ollama on the Same VPS

The following approach uses a native installation of both applications on one server. Keeping them together avoids introducing container networking or a second server during your first deployment.

1 Prepare your Linux environment

Connect through SSH using your server’s address and your own account:

ssh your_user@YOUR_VPS_IP

Replace both placeholders before running the command.

On Ubuntu, update the package index and install the download tools:

sudo apt update

sudo apt install -y curl ca-certificates

Check your available resources:

free -h

df -h

nproc

Record these values before installing the model. You can compare them with resource usage during your first test.

2 Install Ollama

Use the installation command published in Ollama’s official Linux documentation:

curl -fsSL https://ollama.com/install.sh | sh

This downloads and executes the official installer. Review the script first if your server administration policy requires it.

Check the installation and service:

ollama –version

sudo systemctl status ollama –no-pager

If the installed service is stopped, start it:

sudo systemctl start ollama

Then check whether its local API responds:

curl http://127.0.0.1:11434/api/tags

A JSON response confirms that the endpoint is reachable. An empty model list is expected before you download a model. Ollama documents its Linux installation and service management procedures separately, which is useful if your environment needs additional configuration.

3 Install OpenClaw without starting a separate setup wizard

Install OpenClaw through its official installer:

curl -fsSL https://openclaw.ai/install.sh | bash -s — –no-onboard

The –no-onboard option postpones onboarding. You will configure the connection through Ollama in the next step.

Verify that the command is available:

openclaw –version

Follow any dependency or shell-path instructions printed by the installer before continuing. The official installer documentation confirms the skip-onboarding option.

4 Launch the integration

Run:

ollama launch openclaw

Follow the prompts and select a model suitable for your server. For inference on your own VPS, choose a local model rather than a cloud model.

The launcher configures the provider, selects the primary model, installs the gateway daemon and opens the terminal interface.

Review the web-search settings as well. Ollama’s launcher automatically enables OpenClaw’s bundled Ollama web search. Local inference does not mean that web searches or connected external services remain inside your server.

You can review web configuration with:

openclaw configure –section web

The launcher’s behaviour and configuration commands are documented in the official Ollama integration guide.

Give the Model Enough Context

Context is the information a model can work with during a request. It includes instructions, conversation history, documents and tool results.

An assistant may need substantially more context than a short chatbot conversation. Ollama recommends at least 64,000 tokens for workloads such as agents and coding tools, but a larger context window also increases memory requirements.

For a systemd-managed Ollama installation, open the service override:

sudo systemctl edit ollama

Add:

[Service]

Environment=”OLLAMA_CONTEXT_LENGTH=65536″

Save the override, then apply it:

sudo systemctl daemon-reload

sudo systemctl restart ollama

This setting is appropriate only when the model supports it and your hardware can accommodate it. After sending a request, inspect the loaded model:

ollama ps

Check the reported context and processor usage. If memory pressure or response time becomes unacceptable, revisit the model and hardware choice before relying on the assistant.

Test a Real Task Before Connecting Business Accounts

A successful installation is only the first milestone. Your next goal is to establish whether the assistant completes a useful task correctly.

Try this small evaluation:

1. Create a test folder containing two short, non-sensitive documents.
2. Include a specific date, a project name and one unresolved question.
3. Give the assistant access only to that test workspace.
4. Ask it to summarise the documents and identify the unresolved question.
5. Compare the answer with the original files.

Check whether the assistant actually used the available file-reading tool. A plausible answer is not enough if it invented details or never opened the documents.

Repeat the task several times. Look for consistent accuracy, acceptable response times and stable resource usage.

This gives you a more meaningful basis for choosing a model than a single successful “hello” response.

Keep the First Deployment Private

For this same-server installation, Ollama’s default address is:

http://127.0.0.1:11434

The loopback address allows software on the same server to connect without making the endpoint directly available through the server’s public network interface.

Keep this default for the initial setup. Ollama documents the loopback binding and also provides an option to disable its cloud features if a local-only Ollama configuration is required.

Also keep OpenClaw’s administrative access private. Its documentation covers remote access through methods such as SSH tunnelling, allowing you to reach the gateway without broadly exposing it.

For your assistant’s permissions:

• Limit access to the files required for its task.
• Leave unnecessary command execution and account integrations disabled.
• Review proposed file changes and outgoing messages.
• Keep credentials outside the assistant’s ordinary working documents.

Expand access only when a specific workflow needs it.

Troubleshooting

Diagnose one layer at a time instead of reinstalling everything.

Ollama does not respond: Check sudo systemctl status ollama –no-pager.

No models appear: Run ollama list and confirm a model has been downloaded.

OpenClaw cannot find the selected model: Run openclaw models list –provider ollama and compare the model name.

Responses become slow during longer tasks: Check memory use and the active context with ollama ps.

The assistant invents file contents: Verify its file access, tool execution and the selected model’s behaviour.

If Ollama stops unexpectedly, inspect recent service logs:

sudo journalctl -u ollama -n 50 –no-pager

Look for a concrete error, such as insufficient memory, before changing the configuration.

When Does a Dedicated Server Make Sense

A VPS can be a useful place to develop and evaluate an assistant. As usage grows, you may need more memory, more predictable processing capacity or greater separation from other workloads.

At that stage, compare dedicated server hosting with your existing VPS configuration.

Base the decision on measurements:

• How long does a typical task take?
• What happens when two tasks run together?
• How much memory remains during the busiest period?
• Is the server spending substantial time swapping?
• Would the chosen model benefit from compatible GPU acceleration?

Dedicated hardware does not automatically include a GPU or guarantee fast model inference. Confirm the exact specification before ordering.

Similarly, unmetered dedicated server hosting addresses data-transfer billing, subject to the plan’s terms. It does not remove limits imposed by CPU performance, memory capacity or network port speed.

FAQs

Can I run OpenClaw and Ollama on one VPS

Yes. A native same-server installation lets OpenClaw reach Ollama through the local endpoint. The server must still have sufficient resources for the selected model and workload.

Will a small VPS run a large local AI model

Do not assume so. Hosting the assistant software is a different workload from running the model. Evaluate memory, context length and response time together.

Is this setup completely private

Local inference keeps that model’s processing on your server. Cloud models, web search and connected messaging services introduce external data flows. Review each integration separately.

Can I use shared web hosting

This guide requires permission to install software and manage background services. Check those capabilities with your host; a website-only hosting account may not provide them.

What should I do before using the assistant every day

Keep a record of software versions, the selected model, enabled tools and configuration. Establish a backup and recovery process, then repeat your test task after updates.

Build Around One Useful Workflow

Start with one task, a limited workspace and a model your server can run comfortably. Confirm accuracy and performance before adding more tools or connecting additional accounts.

Explore DigitalBerg VPS hosting and compare the available resources with your model’s requirements. For heavier workloads, review the dedicated server options and confirm the hardware needed for your intended deployment.