How to Use OpenClaw with Ollama for Local AI (No Cloud Required)

Running OpenClaw with Ollama means your AI agent operates entirely on your own hardware, with no data leaving your machine. No API costs, no privacy concerns, no dependence on external services. Here’s how to set it up.

What is Ollama?

Ollama is a tool for running large language models locally. It handles the technical complexity of loading and running models like Llama 3, Mistral, and Gemma on your hardware. For OpenClaw, it becomes the AI backend instead of cloud services like Claude or GPT.

Hardware Requirements

Local AI models need RAM — specifically:

• Llama 3.2 3B: ~4GB RAM (fast, capable for most tasks)
• Mistral 7B: ~8GB RAM (excellent quality/speed balance)
• Llama 3.1 8B: ~8GB RAM (strong reasoning)
• Llama 3.1 70B: ~40GB+ (best quality, needs high-end hardware)

A Mac Mini M4 with 16GB RAM handles Mistral 7B comfortably alongside OpenClaw. Mac Mini M4 on Amazon.

Installation

Step 1: Install Ollama

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

Step 2: Pull a Model

ollama pull mistral
ollama pull llama3.2

Step 3: Configure OpenClaw to Use Ollama

In your OpenClaw config, set the AI provider to Ollama and point it to the local endpoint (default: http://localhost:11434). OpenClaw handles the rest.

Performance Expectations

Local models are slower than cloud APIs on most hardware. Expect 5-20 seconds for responses on an 8B model, versus under 3 seconds with Claude API. For non-time-sensitive tasks (email summaries, research, content drafts), this is perfectly acceptable. For real-time conversation, cloud APIs are faster.

The Hybrid Approach

Many users run Ollama for routine tasks (cheaper, private) and fall back to Claude or GPT-4 for complex reasoning. OpenClaw supports this configuration. It’s the most cost-effective approach for heavy users.