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Run Claude Code and OpenClaw in Ollama and Build a High-Performance Local AI Stack Fast

Run Claude Code and OpenClaw in Ollama gives you a clean way to build a full AI environment without touching cloud APIs.

This removes token fees, removes delays, and removes the complexity most teams struggle with when starting out.

It becomes the foundation for a reliable workflow that anyone can repeat and scale.

Watch the video below:

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Most tutorials leave out the practical steps that actually matter.

This SOP does the opposite.

This breaks everything down so you can follow it line-by-line, whether you run a solo operation or a team workflow.

By the end, you’ll have Claude Code and OpenClaw running directly on your machine through Ollama, with no cloud dependency holding you back.

Let’s start with a clean setup and build the entire system from the ground up.

Install Ollama Before You Run Claude Code and OpenClaw in Ollama

Ollama becomes the engine for the entire workflow.

The installation is simple.

Download the app from the official site.

Open it once to confirm it launches correctly.

This starts a lightweight local server that powers all future model calls.

Once Ollama is running, every other part of the SOP becomes possible.

This step sets the foundation for the specific model you will use with Claude Code and OpenClaw.

Pull the Model That Will Run Claude Code and OpenClaw in Ollama

Teams often ask which model works best.

The answer usually depends on hardware.

Most users start with GLM 4.7 Flash because it delivers strong performance without stressing most machines.

Use this command:

ollama pull glm-4.7-flash

This downloads the model into your local environment.

The process runs once.

After that, the model becomes available offline, ready for immediate use.

Pulling the model prepares Ollama to power both Claude Code and OpenClaw with zero cloud billing.

Verify Local Model Execution Before You Run Claude Code and OpenClaw in Ollama

Start the model with:

ollama run glm-4.7-flash

This tests the most important part of the setup.

The model should load without errors.

You should receive a prompt and be able to generate a simple reply.

This confirms that your system can handle local inference.

Testing early prevents downstream issues when linking Claude Code or OpenClaw.

Once this step works, your machine is officially ready.

Launch Claude Code Using Your Local Model for Run Claude Code and OpenClaw in Ollama

Claude Code can use the model through Ollama with a simple command.

Open a new terminal.

Launch Claude Code and point the environment to your local model.

This switches Claude Code from cloud mode into local mode.

Your development environment now runs without API keys or paid tokens.

Claude Code becomes faster because the calls stay on your machine.

Once you receive a generated response, Claude Code is fully integrated with Ollama.

Connect OpenClaw to the Same Environment to Run Claude Code and OpenClaw in Ollama Together

OpenClaw connects to Ollama in a similar way.

Start OpenClaw as you normally would.

When asked to select a model source, choose the Ollama gateway.

OpenClaw now draws all inference from your local model.

This makes every agent interaction faster because there is no network delay.

You also gain full stability because nothing depends on cloud uptime.

Once OpenClaw returns a response, the integration is complete.

Test Both Tools to Confirm Run Claude Code and OpenClaw in Ollama Works End-to-End

Testing avoids confusion later.

Ask Claude Code to generate a short web component.

Watch the response speed.

Ask OpenClaw to execute a simple multi-step task.

Look for smooth handling and consistent output.

Both tools should respond instantly.

If everything runs correctly, your AI development stack is officially set up.

This marks the moment when cloud dependence ends and performance becomes predictable.

Build a Simple Repeatable SOP After You Run Claude Code and OpenClaw in Ollama

Teams perform the same steps daily.

Creating an SOP eliminates friction.

A simple structure looks like this:

This keeps your environment consistent across projects and team members.

A predictable SOP also shortens onboarding time for new contributors.

Optimize the Setup for Speed After You Run Claude Code and OpenClaw in Ollama

Local performance scales with hardware.

More RAM usually improves stability.

A stronger GPU increases token throughput.

Faster storage helps with model loading.

Teams focused on heavy workflows often upgrade slowly over time.

This avoids cloud scaling costs and preserves long-term speed.

Because the system runs locally, optimization stays in your control, not a vendor’s.

Keep Notes and Command Lists to Support Run Claude Code and OpenClaw in Ollama

Create a document with all commands.

Add notes about performance, model behavior, and troubleshooting.

Record which models perform best for your tasks.

This document becomes part of your internal playbook.

Small details like these prevent mistakes during busy workloads.

They also make future iterations easier to maintain.

Once you’re ready to level up, check out Julian Goldie’s FREE AI Success Lab Community here:

👉 https://aisuccesslabjuliangoldie.com/

Inside, you’ll get step-by-step workflows, templates, and tutorials showing exactly how creators use AI to automate content, marketing, and workflows.

It’s free to join — and it’s where people learn how to use AI to save time and make real progress.

If you want to explore the full OpenClaw guide, including detailed setup instructions, feature breakdowns, and practical usage tips, check it out here: https://www.getopenclaw.ai/

FAQ

1. Where can I get templates to automate this setup?
Inside the AI Success Lab, including complete SOPs for Ollama, Claude Code, and OpenClaw.

2. Does this setup work on low-end hardware?
Yes. Lightweight models run on most machines without adjustments.

3. Can Claude Code and OpenClaw share the same model?
Yes. Both tools can access the same Ollama instance.

4. What happens during an internet outage?
Local inference keeps running because nothing depends on cloud servers.

5. Why do teams prefer this workflow?
The system becomes faster, cheaper, and more stable than cloud alternatives.

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