How to Run OpenClaw Fully Local with Ollama

A user on r/clawdbot has shared a method for running the OpenClaw agent framework entirely on a local machine, eliminating the need for paid cloud APIs.
Setup Process
The described process involves several specific steps:
- Use LLMFit to benchmark and find the best-performing language model your local hardware can handle. The source links to the tool's GitHub repository: https://github.com/AlexsJones/llmfit.
- Install Ollama.
- Pull your selected model locally using Ollama.
- Link Ollama to OpenClaw.
- Restart the OpenClaw Gateway.
Reported Benefits
According to the source, this setup provides several advantages:
- No API keys required.
- No token limits.
- No per-request billing.
- Fully self-hosted.
- Useful for experimentation and automation.
The post suggests this method is particularly applicable for building internal agents, automation workflows, or for aggressive testing scenarios. The original author also solicits community feedback, asking what models others are running locally with Ollama and agent frameworks, what hardware they use, and how the performance is.
📖 Read the full source: r/clawdbot
👀 See Also

Research Shows Effective AI Prompting Is Cooperative Communication, Not Engineering
Peer-reviewed research indicates that effective prompting with AI models follows the same cooperative communication principles humans use, with Lakera's analysis showing most prompt failures stem from ambiguity rather than model limitations.

Canary Instance Setup for Safe OpenClaw Upgrades
A Reddit user shares a detailed canary methodology for testing OpenClaw upgrades before production: isolated config root, separate port, smoke test matrix, and a structured upgrade report format.

Mastering Backup: Safeguarding Your OpenClaw Agent
In an era dominated by automation and AI, ensuring the safety of your OpenClaw agent through robust backup strategies is paramount. Learn the essential steps to secure your digital assistant.

DeepSeek-V4-Flash W4A16+FP8 with MTP Self-Speculation: 85 tok/s on 2x RTX PRO 6000 Max-Q
DeepSeek-V4-Flash quantized to W4A16+FP8 achieves 85.52 tok/s at 524k context on 2× RTX PRO 6000 Max-Q using a patched vLLM with retrofitted MTP head, up from 52.85 tok/s baseline.