Can OpenClaw Work Offline? Running Local AI Models

Introduction

Yes — OpenClaw can run entirely offline. No internet connection required for the AI inference itself, no data leaving your machine, no ongoing API costs. This is possible through Ollama, a tool that lets you run large language models locally on your own hardware, and through OpenClaw's model-agnostic architecture that treats local models as just another provider.

The ability to run fully offline is more than a party trick. For professionals handling sensitive data, for users in regions with unreliable internet, for anyone who refuses to send private conversations to third-party servers, or for those who simply want zero variable API costs, local model deployment with OpenClaw is a legitimate and increasingly practical option.

This guide walks you through every step of setting up a fully local OpenClaw deployment and helps you understand what you gain, what you sacrifice, and how to find the right balance for your needs.

Why Run Locally?

Three categories of users have compelling reasons to run OpenClaw with local models.

Privacy-first users have the most urgent motivation. When you send a message to OpenClaw using a cloud model like GPT-4, that message travels to OpenAI's servers for inference. OpenAI's privacy policies are clear and generally respected, but the fundamental fact remains: your words leave your hardware. For a lawyer processing client communications, a doctor discussing patient cases, a CFO analyzing unreleased financial data, or anyone with legitimate confidentiality obligations, even the best cloud provider introduces risk. Local models eliminate that risk entirely — inference happens on your machine, your words stay on your machine.

Cost-conscious users appreciate that local models have no per-token charges. A busy OpenClaw agent doing 50 heartbeat cycles per day with 1,000 tokens each generates 50,000 tokens daily. At frontier model pricing, that's $1–5 per day, or $30–150 per month. Over a year, that's real money. A locally-run model running on hardware you already own costs essentially zero per inference beyond electricity.

Reliability-focused users value independence from external services. API outages, rate limits, and provider pricing changes can disrupt a cloud-dependent agent at any time. A local model keeps your agent running regardless of what any cloud provider is doing.

Setting Up Ollama

Ollama is a tool that makes running local LLMs as simple as running any other application. It handles model downloading, quantization management, GPU acceleration, and provides an API endpoint that OpenClaw can communicate with exactly as it would with any cloud provider.

Installing Ollama is a single command on most platforms:

# macOS and Linux
curl -fsSL https://ollama.com/install.sh | sh

# Verify installation
ollama --version

On Windows, Ollama provides an installer available from ollama.com. Once installed, download a model. Start with something capable but not too large for your hardware:

# Pull a capable 8B parameter model (~5GB)
ollama pull llama3.2

# Or a smaller, faster model for constrained hardware (~2GB)
ollama pull phi4-mini

# Verify the model works
ollama run llama3.2 "Hello, are you working?"

Once you see a response from the last command, Ollama is functioning correctly. It will have started a local API server on http://localhost:11434.

Now configure OpenClaw to use Ollama in your config.yaml:

llm:
  default_provider: ollama
  providers:
    ollama:
      base_url: "http://localhost:11434"
      model: "llama3.2"

Restart OpenClaw and test with a message on Telegram. If it responds, your fully local, offline-capable deployment is working.

Best Local Models for OpenClaw

Not all local models perform equally for agentic tasks. OpenClaw's agent runtime requires a model that can follow complex instructions, use tool calls reliably, maintain coherent context, and reason through multi-step problems. Here are the best options across hardware categories:

For most OpenClaw deployments on a Mac Mini (8–16GB RAM), Llama 3.2 8B or Mistral 7B Instruct hit the best balance of capability and speed. If you have a machine with 32–64GB of RAM, Llama 3.1 70B delivers performance genuinely close to GPT-4 for many task categories.

Performance & Trade-offs

The honest truth about local models is that they trail the best cloud models in raw capability. GPT-5 and Claude Opus are trained on vastly more data, with vastly more compute, and they show it in complex reasoning, nuanced writing, and reliable tool use. If your OpenClaw agent primarily does complex strategic analysis or nuanced long-form writing, cloud models will produce noticeably better results.

However, for the most common OpenClaw use cases — monitoring conditions, parsing structured data, extracting information from documents, writing routine communications, running shell commands — capable local models perform very well. The gap is narrowing with each new model release, and for privacy-sensitive or cost-sensitive deployments, the trade-off is worth making.

Response latency is another consideration. Cloud APIs typically respond in 2–5 seconds for moderate prompts. A local model on M2 Mac hardware generates responses at roughly 15–40 tokens per second, which means moderate prompts take 3–8 seconds. On older or lower-powered hardware, this gets slower. For heartbeat tasks that run in the background without you watching, latency matters less. For interactive conversations, it can feel sluggish.

GPU acceleration helps significantly. If your hardware has a discrete GPU (or an Apple Silicon chip with unified memory), Ollama will use it automatically, often 3–5x faster than CPU-only inference.

Hybrid: Local + Cloud

Many experienced OpenClaw users settle on a hybrid approach that gets the best of both worlds. The basic pattern:

• Heartbeat tasks: Use a local model. These are often structured, repetitive tasks where a capable 8B model performs fine. Zero API cost for the most frequent source of token consumption.
• Sensitive tasks: Use a local model. Legal documents, health data, financial analysis — any task involving confidential information routes to the local model regardless of quality considerations.
• Complex interactive tasks: Use a cloud model. When you need the best reasoning, nuanced writing, or complex code generation, route those requests to GPT-5 or Claude Opus.

OpenClaw supports this pattern through per-session model configuration and Skills that can route requests to different providers based on configurable rules. You can define a "sensitive_topics" pattern that automatically switches to the local model when certain keywords appear in a conversation.

Hardware You'll Need

Local model performance scales with hardware. Here's a practical guide:

For casual use / testing: Any modern laptop with 8GB RAM can run Phi-4 Mini or Mistral 7B. Don't expect enterprise-grade responses, but it works for getting familiar with local model getting it running.

For daily personal use: A Mac Mini M4 with 16GB unified memory is the community's most-recommended dedicated hardware. It runs Llama 3.2 8B comfortably, handles 24/7 uptime without thermal issues, consumes under 10W at idle, and costs around $600. Excellent ROI for a dedicated AI appliance.

For high-quality local inference: A machine with 32–64GB RAM (another Mac Mini, a used workstation, or a mini PC with a capable GPU) can run 70B parameter models that approach frontier model quality. This is the configuration for users who need both quality and privacy.

For extreme constraint (IoT / edge): A Raspberry Pi 5 with 8GB RAM can run Phi-4 Mini. It's slow — 3–5 tokens per second — but it works. More suitable as a lightweight relay node that handles simple tasks locally and routes complex requests to a more capable machine elsewhere on the network.

Wrapping Up

Running OpenClaw with local models via Ollama is not just possible — for many users, it's the right choice. The combination of zero variable API costs, complete data sovereignty, and hardware-independent capability makes local deployment a compelling option for privacy-conscious professionals, cost-sensitive power users, and anyone who wants their AI infrastructure to be entirely under their own control. The performance gap with cloud models is real but narrowing. The freedom is immediate.