Run a Local LLM with OpenClaw on Your Mac Mini

2026-06-16 · Source: Towards Data Science · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Robotics & Autonomous Systems · Depth: Intermediate, medium

Summary

This guide outlines a method for running a local Large Language Model (LLM) with OpenClaw on a Mac Mini, specifically to eliminate ongoing pay-per-token API expenses from providers like Anthropic or OpenAI. The process, tested on a Mac Mini with an M2 processor and 24GB unified memory, involves installing llama.cpp from source with Metal acceleration enabled, bypassing Ollama for a potential 70% inference speedup. It recommends using the quantized Qwen 3.5-9B parameter model, noted as a top performer as of June 2026, which fits on 16GB or 24GB Macs. The setup includes downloading an agent-compatible chat template, configuring llama-server to run as a launchd daemon, and updating OpenClaw's openclaw.json to use the local model. Verification steps involve testing model registration and a sample Python calculation skill, demonstrating speeds of 20-70 tokens per second.

Key takeaway

For AI Engineers managing OpenClaw agents on Mac Mini hardware, if you are seeking to eliminate recurring API costs, you should implement a local LLM setup. By configuring llama.cpp with a quantized model like Qwen 3.5-9B and running llama-server as a launchd daemon, you can achieve 20-70 tokens per second inference speeds. This approach avoids external API fees, making your agent operations more cost-effective and self-contained. Ensure your Mac Mini has at least an M2 processor and 24GB unified memory for optimal performance.

Key insights

Running a local, quantized LLM on a Mac Mini with llama.cpp significantly reduces OpenClaw API costs while maintaining performance for common agent tasks.

Principles

• Quantization enables larger models on limited hardware.
• llama.cpp with Metal accelerates Mac LLM inference.
• Agent-compatible templates are crucial for OpenClaw.

Method

Install llama.cpp with Metal flags, download a quantized LLM (e.g., Qwen 3.5-9B) and agent template, configure llama-server as a launchd daemon, then update OpenClaw's openclaw.json to use the local API endpoint.

In practice

• Use Qwen 3.5-9B for OpenClaw on 16GB/24GB Macs.
• Build llama.cpp with "-DGGML_METAL=ON".
• Configure launchd for automatic llama-server startup.

Topics

• Local LLMs
• OpenClaw Agents
• Mac Mini
• llama.cpp
• Model Quantization
• Qwen 3.5-9B

Code references

• ggml-org/llama.cpp

Best for: AI Engineer, Machine Learning Engineer, MLOps Engineer

Related on AIssential

• llama.cpp: Fast Local LLM Inference, Hardware Choices & Tuning — llama.cpp is the dominant open-source C/C++ framework enabling fast local large language model (LLM) inference, leveraging hardware advancements like NVIDIA's RTX 5090 and Apple's M4 Ultra for privacy…
• stop chasing one local model for openclaw — Optimize local LLM setups by routing task-specific models rather than seeking a single general-purpose solution.
• Hermes Agent with Qwen3.6 (Local) | AI App Template with LangChain, LangGraph, llama.cpp | 🔴 Live — Local LLMs like Qwen 3.6 face significant challenges in agentic coding tasks, especially with lower quantization.
• How to Run LLMs Locally (Great For Learning and Privacy) — Specialized tools make running powerful LLMs locally feasible for privacy, learning, and production needs.
• Gemma 4 with Pi Coding Agent & llama.cpp | Build LLM Resource Calculator with NextJS | 🔴 Live — PyCoding Agent with local LLMs can build simple apps but struggles with complex UI overhauls and skill integration.
• not much happened today — AI agent development is shifting from interactive chat to robust, verifiable, and automated production systems.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Towards Data Science.