Run a Local LLM with OpenClaw on Your Mac Mini
Run a Local LLM with OpenClaw on Your Mac Mini
https://towardsdatascience.com/run-a-local-llm-with-openclaw-on-your-mac-mini/
Publish Date: 2026-06-16 11:00:00
Source Domain: towardsdatascience.com
You bought the Mac Mini for Openclaw. Perfect.
late, Anthropic has pushed OpenClaw users toward its pay-per-token API1, turning what was once a one-time hardware purchase into an (large) ongoing expense2. Even if you use OpenAI, you’re still going to be paying quite a bit monthly.
💵💵 Running a local model eliminates the monthly cost for your OpenClaw agents, entirely. 💵💵
However, getting everything installed and configured can be confusing, especially if you’re new to local LLMs.
In this article, I’ll show you how to set up a local LLM (in the most pain-free way) on your Mac Mini that can power your agent for free.
You can use it even if you’re a beginner.
🤨 “I’ve heard that local LLMs don’t work as well, is that true?”
A local LLM (properly set up) will perform almost indistinguishably for tasks like emails, calendar management, reminders, home IoT automation and basic internet research (things you actually do with OpenClaw).
If you need to do something more advanced, like using OpenClaw for software engineering, there’s a link at the bottom which highlights how to set up a fallback model.
⚠️Note: This guide is not a full OpenClaw tutorial.
It’s intended to help you get your local LLM up and running with your agent(s) as quickly as possible.
Hardware
This article was tested on a Mac Mini with the following specs
| OS | macOS Tahoe |
| Version | 26.3.1 |
| Processor | M2 |
| Cores | 8 |
| Unified Memory | 24GB |
If you’re thinking about buying a Mac Mini, I’d recommend at least an M2+ processor with at least 24GB of RAM. You can get away with 16GB, however, things will be quite tight and you might run into errors with larger contexts.
Setting things up
First, install OpenClaw using the official guide. If you’ve already done this, skip this step.
1. Install llama.cpp
We’re going to skip using Ollama (the recommended local provider), and opt for llama.cpp. By using a quantized model along with…
