recommendedadvanced25 min read•March 2026

$ cat local-ai-setup.md

export LocalAISetup

/** Run OpenClaw with 100% local AI - Zero API costs, Complete Privacy */

ollama-terminal.png

Ollama terminal showing OpenClaw integration

table-of-contents.ts

const sections = [

]

section_01_intro.md

🏠## Why Run AI Locally?

Running AI models locally represents the ultimate form of sovereign intelligence. Instead of sending your data to cloud servers, everything stays on your own hardware. This approach offers significant advantages for privacy-conscious users and developers who want complete control over their AI assistant.

With the rise of powerful open-source models like Llama 3, Mistral, and Gemma, running local AI has never been more accessible. Combined with OpenClaw's flexible architecture, you can build a personal AI assistant that rivals cloud-based solutions while maintaining complete privacy.

// Benefits of local AI:

• Zero API costs - No per-token charges, unlimited usage

• Complete privacy - Your data never leaves your machine

• Offline capable - Works without internet connection

• Faster response - No network latency for local inference

• Full customization - Fine-tune models for your use case

section_02_prerequisites.md

## 📦 Prerequisites

Before setting up local AI, ensure your system meets the minimum requirements. Local AI inference is computationally intensive, and having adequate hardware will significantly impact performance. The good news is that even a Mac Mini M2 with 16GB RAM can run impressive models.

✓macOS 12+ / Linux / Windows 11 (WSL2)

✓16GB+ RAM// 32GB+ recommended for larger models

✓Apple Silicon (M1/M2/M3) or NVIDIA GPU

✓20GB+ free storage// Models are 4-40GB each

✓OpenClaw installed// See getting-started guide

// 💡 Apple Silicon Macs are ideal for local AI - unified memory allows larger models

section_03_install.sh

## 🦙 Installing Ollama

Ollama is a powerful tool that makes running local LLMs incredibly simple. It handles model management, optimization, and provides a clean API that OpenClaw can connect to. The installation process takes just a few minutes and works seamlessly across all major platforms.

macOS / Linux Installation

$ curl -fsSL https://ollama.com/install.sh | sh

Verify Installation

$ ollama --version

ollama version 0.5.7

$ ollama serve

✓ Ollama is running on http://localhost:11434

Once installed, Ollama runs as a background service. It automatically manages model loading and unloading based on available memory, making it perfect for systems with limited resources. The service starts automatically on boot, so your AI assistant is always ready.

section_04_models.md

## 🎯 Choosing Your Model

Choosing the right model depends on your hardware and use case. Larger models offer better reasoning and knowledge, but require more memory and are slower. For most personal assistant tasks, a 7B or 13B parameter model provides an excellent balance of quality and speed.

Model	Size	RAM	Best For
llama3.2:3b	2GB	8GB	Quick tasks, low-resource systems
llama3.3:8b	4.7GB	16GB	General assistant, coding
mistral:7b	4.1GB	16GB	Fast responses, multilingual
codellama:13b	7.4GB	24GB	Programming, code review
llama3.3:70b	40GB	64GB+	Maximum capability

Download Your Model

# Download the recommended model (4.7GB)

$ ollama pull llama3.3:8b

# Test it works

$ ollama run llama3.3:8b "Hello, introduce yourself"

section_05_integrate.sh

## 🔗 Integrating with OpenClaw

OpenClaw provides native integration with Ollama through the LiteLLM provider system. This allows you to seamlessly switch between local and cloud models, or even use them together. The configuration is straightforward and can be done through the onboarding wizard or manually.

Option 1: Interactive Setup

$ openclaw onboard

# When prompted for AI provider, select:

> Ollama (local)

# Enter Ollama endpoint:

> http://localhost:11434

# Select model:

> llama3.3:8b

Option 2: Manual Configuration

# ~/.openclaw/config.yaml

ai_provider:

type: "ollama"

endpoint: "http://localhost:11434"

model: "llama3.3:8b"

context_length: 8192

Test the Integration

$ openclaw agent --message "What model are you running on?"

🦞 I'm currently running on Llama 3.3 8B locally via Ollama...

section_06_claude_code.md

## ✨ One-Prompt Setup with Claude Code

For users who prefer a guided approach, Claude Code (or similar AI coding assistants) can automate the entire setup process. Simply describe what you want, and the AI will handle installation, configuration, and testing. This method is ideal for beginners or those who want a quick setup.

The Magic Prompt

"Help me set up OpenClaw with local Ollama models. I want to connect it to WhatsApp and iMessage. Use Llama 3.3 as the default model. Configure security settings and set up automatic startup. Guide me step by step."

Claude Code will walk you through each step, automatically generating configuration files, testing the setup, and troubleshooting any issues. This approach combines the best of both worlds: the power of local AI with the convenience of guided setup.

// What Claude Code will do:

1. Check system requirements and prerequisites

2. Install Ollama and download your chosen model

3. Configure OpenClaw with optimal settings

4. Set up messaging channel bridges

5. Configure security and access controls

6. Create startup scripts for automatic launch

section_07_messaging.sh

## 💬 Configuring Messaging Channels

Once your local AI is running, connect it to your favorite messaging platforms. OpenClaw supports WhatsApp, Telegram, Discord, Slack, Signal, and iMessage. Each channel can be configured with different models or settings for specialized use cases.

WhatsApp Setup

$ openclaw channel add whatsapp

# Scan the QR code with your phone

✓ WhatsApp connected successfully

Telegram Setup

$ openclaw channel add telegram --token YOUR_BOT_TOKEN

✓ Telegram bot @YourOpenClawBot is now active

// 💡 Pro tip: Use different models for different channels. Code tasks can use CodeLlama while general chat uses Llama 3.3 for faster responses.

section_08_optimize.md

## ⚡ Performance Optimization

Getting the best performance from local AI requires some tuning. These optimizations can significantly reduce response latency and memory usage, especially on systems with limited resources.

Use Quantized Models

Q4_K_M quantization reduces model size by 75% with minimal quality loss. Use ollama pull llama3.3:8b-q4_K_M

Enable GPU Acceleration

Ollama automatically uses Apple Metal or CUDA. Verify with ollama info

Adjust Context Length

Lower context (4096 vs 8192) reduces memory and speeds up responses for simple tasks

Pre-load Models

Keep frequently used models warm with OLLAMA_KEEP_ALIVE=24h

section_09_troubleshoot.md

## 🔧 Troubleshooting

Error: Connection refused to localhost:11434

Ollama service is not running. Start it with: ollama serveor check launchctl list | grep ollama

Error: Out of memory

Model is too large for your RAM. Try a smaller model or quantized version: ollama pull llama3.2:3b

Slow response times

First response is slower due to model loading. Subsequent responses are faster. Use OLLAMA_KEEP_ALIVE=24h to keep models warm.

section_10_security.md

## 🔒 Security Best Practices

⚠️ Security Warning

A phishing site openclawd.ai (with a 'd') has been identified. Only use official sources: openclaw.ai or github.com/openclaw/openclaw

// Security checklist:

✓ Bind Ollama to localhost only (default)

✓ Use strong authentication for messaging channels

✓ Enable rate limiting to prevent abuse

✓ Regularly update OpenClaw and Ollama

✓ Review permissions granted to the AI agent

next_steps.md

## 🎉 Congratulations!

You now have a fully local, private AI assistant running on your own hardware! Explore these tutorials to extend your setup:

export CustomSkills

// Build your own skills

export BrowserAutomation

// Automate web tasks

Was this tutorial helpful?

// Join our community on Discord for more help