Run OpenClaw with a Local LLM on macOS – Guide for 16–24GB RAM

A new guide walks through setting up OpenClaw with a local LLM on macOS, specifically targeting machines with 16–24GB RAM. The author tested a quantized version of Qwen 3.5 configured for OpenClaw, and includes a test skill to confirm everything is working.

Setup Overview

• Model: Qwen 3.5 (quantized) – chosen to fit within 16–24GB RAM while providing decent reasoning capability.
• Platform: macOS (tested on Mac Mini with 16–24GB).
• Key step: Configure OpenClaw to use the local model endpoint (typically via Ollama or llama.cpp). The guide provides specific config file edits.

Test Skill

To validate the setup, the author created a test skill that calls the local model and returns a known response. If the skill executes correctly, your local LLM is fully integrated with OpenClaw.

Why Local LLM?

Running an LLM locally avoids API costs and latency, keeps code and prompts on-device, and works offline. For OpenClaw users with Apple Silicon Macs, quantized models like Qwen 3.5 are a practical compromise between accuracy and memory.

Next Steps

If the test skill fails, check your model server (Ollama) is running and the OpenClaw config points to the correct URL (http://localhost:11434 for Ollama). Adjust context window size if needed to fit memory.