OpenClaw + Ollama: Run Free Local AI Models (Setup Guide)
Run OpenClaw with free local models using Ollama. No API costs, full privacy, unlimited messages.
Why Run OpenClaw with Local Models?
Cloud AI models charge per message, and costs add up fast for heavy users. Running local models through Ollama gives you unlimited AI coding assistance at zero marginal cost. You also get full privacy (your code never leaves your machine), zero latency from network roundtrips, and offline capability. The trade-off is that local models require decent hardware and may be slower or less capable than top cloud models.
Hardware Requirements for Local Models
For small models (7B parameters), you need at least 8GB RAM and a modern CPU. For medium models (13B-34B), 16GB+ RAM is recommended, and a GPU with 8GB+ VRAM significantly speeds up inference. For large models (70B+), you need 32GB+ RAM or a powerful GPU. Apple Silicon Macs (M1/M2/M3/M4) run local models exceptionally well thanks to unified memory architecture.
See the real cost per message for 350+ models in our live comparison table.
Compare prices →Installing Ollama
Ollama is a lightweight tool that downloads, manages, and serves local AI models. Install it from the official website or via your package manager. Once installed, pulling a model is one command. Ollama handles model downloading, quantization, and serving through a local API endpoint that OpenClaw connects to automatically.
Best Local Models for OpenClaw
For coding tasks, CodeLlama and DeepSeek Coder are the top choices for local inference. Llama 3.1 (8B and 70B) offers strong general-purpose capabilities. Mistral provides a good balance of speed and quality at smaller sizes. Qwen 2.5 Coder excels at code generation with multilingual support. Start with the 7B or 8B versions for testing, then scale up if your hardware allows.
Not sure which model fits your budget? Compare prices side by side.
Compare prices →Configuring OpenClaw for Ollama
Point OpenClaw to your local Ollama instance by setting the model provider to Ollama and specifying the model name. OpenClaw automatically detects running Ollama instances on the default port. You can switch between local and cloud models on the fly, using cheap local models for simple tasks and cloud models for complex ones.
Local vs Cloud: When to Use Which
Use local models for: unlimited iteration on code changes, privacy-sensitive projects, offline development, and simple to medium coding tasks. Use cloud models for: complex architecture decisions, multi-file refactoring, advanced reasoning, and when speed matters more than cost. Many OpenClaw power users combine both: Ollama for daily grunt work and a cloud model for heavy lifting. When you need to pick a cloud model, LLM Bench shows you the exact cost per message for 350+ models, updated hourly. Filter by "Popular for OpenClaw" to see community favorites, and sort by msgs/$ to find the cheapest option that meets your quality needs.
Ready to find the best model for your budget? Compare 350+ AI models ranked by messages per dollar, updated every hour.
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