OpenClaw + Ollama Local Offline Deployment Complete Guide (2026)

February 21, 2026

Want a fully offline AI assistant that doesn’t depend on any external APIs? The OpenClaw + Ollama combo gives you:

✅ Zero monthly costs — No Anthropic/OpenAI API key needed
✅ Complete data privacy — All conversations processed locally, no third-party servers
✅ Offline capable — Works without internet, perfect for air-gapped environments
✅ Model flexibility — Switch between LLaMA, Qwen, DeepSeek, Gemma at will

This guide walks you through building this system from scratch, suitable for users with basic Linux knowledge.

I. Environment Requirements

Hardware Requirements

Model Size	Minimum VRAM	Recommended VRAM	Recommended CPU	RAM
7B models (e.g. Qwen2.5:7b)	8GB	12GB+	8 cores	16GB
14B-34B models	16GB	24GB+	16 cores	32GB
70B+ models	48GB	80GB+	32 cores	64GB+

💡 No GPU? Small models (7B) can run on CPU, but 5-10x slower.

System Requirements

Ubuntu 22.04+ / Debian 12+ / macOS 12+
Docker (optional, recommended for isolation)
At least 50GB available disk space (model files are large)

II. Installing Ollama

Method 1: One-line Install Script (Recommended)

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

# Verify installation
ollama --version

Method 2: Docker Deployment

# Create data directory
mkdir -p ~/.ollama

# Start Ollama service (GPU version)
docker run -d \
  --name ollama \
  --gpus all \
  -v ~/.ollama:/root/.ollama \
  -p 11434:11434 \
  ollama/ollama

# CPU version: remove --gpus all
docker run -d \
  --name ollama \
  -v ~/.ollama:/root/.ollama \
  -p 11434:11434 \
  ollama/ollama

Verify Service

curl http://localhost:11434/api/tags

JSON response = successful installation.

III. Download and Configure Models

Recommended Model Catalog

Model	Parameters	Strengths	Download Command
qwen2.5:7b	7B	Chinese dialogue, code	`ollama pull qwen2.5:7b`
llama3.3:70b	70B	Reasoning, long context	`ollama pull llama3.3:70b`
deepseek-r1:7b	7B	Reasoning chains	`ollama pull deepseek-r1:7b`
codellama:13b	13B	Code generation	`ollama pull codellama:13b`
gemma2:9b	9B	General dialogue	`ollama pull gemma2:9b`

Download Models

# Example: Download Qwen2.5 7B (great for Chinese)
ollama pull qwen2.5:7b

# Multiple models (choose as needed)
ollama pull deepseek-r1:7b
ollama pull codellama:13b

First download takes time (~4-5GB for 7B models), please be patient.

Test Model

# Interactive test
ollama run qwen2.5:7b

# When prompt appears, type:
Hello, introduce yourself

# Exit: Ctrl+D or type /bye

IV. OpenClaw Integration

1. Edit OpenClaw Config

vim ~/.openclaw/openclaw.json

2. Add Ollama as Provider

Add to providers section:

providers:
  # Keep existing providers (e.g. anthropic)...
  
  # Add Ollama
  - id: ollama
    kind: openai-compatible     # Ollama is OpenAI API compatible
    baseUrl: http://localhost:11434/v1
    apiKey: ollama               # Any string works, Ollama doesn't validate

3. Configure Default Model

models:
  default: ollama/qwen2.5:7b    # Use local model as default
  fallbacks:
    - ollama/deepseek-r1:7b     # Fallback option
    - anthropic/claude-sonnet-4-5  # Cloud fallback if Anthropic configured

4. Complete Config Example

gateway:
  mode: local
  port: 18789

providers:
  - id: ollama
    kind: openai-compatible
    baseUrl: http://localhost:11434/v1
    apiKey: ollama

models:
  default: ollama/qwen2.5:7b
  fallbacks:
    - ollama/deepseek-r1:7b

plugins:
  allow:
    - web_search
    - web_fetch
    - exec

channels: []

5. Restart OpenClaw

openclaw gateway restart

# Check logs to confirm connection
openclaw gateway logs | grep ollama

V. Verification & Testing

CLI Test

# OpenClaw CLI interactive mode
openclaw chat

# Send test message
Generate a Python script to read CSV files

API Test

curl -X POST http://localhost:18789/v1/messages \
  -H "Content-Type: application/json" \
  -d '{
    "model": "ollama/qwen2.5:7b",
    "messages": [{"role": "user", "content": "Hello"}],
    "max_tokens": 100
  }'

VI. Troubleshooting

1. `connect ECONNREFUSED 127.0.0.1:11434`

Cause: Ollama service not running.

Fix:

# Check if Ollama is running
ps aux | grep ollama

# Start manually (non-Docker)
ollama serve &

# Docker method
docker start ollama

2. Slow Inference Speed

Cause: CPU mode or insufficient VRAM.

Optimization:

# Check GPU usage
nvidia-smi

# Switch to smaller model
ollama pull qwen2.5:1.5b   # Only 1.5B params, CPU-friendly

# Update OpenClaw config
models:
  default: ollama/qwen2.5:1.5b

3. Poor Response Quality

Cause: Sub-7B models have limited capabilities.

Solutions:

Use 14B+ models (e.g. qwen2.5:14b)
Configure cloud fallback for complex tasks:

models:
  default: ollama/qwen2.5:7b
  fallbacks:
    - anthropic/claude-sonnet-4-5  # Cloud fallback for complex tasks

4. `model not found`

Cause: Model not downloaded or name mismatch.

Fix:

# List downloaded models
ollama list

# Verify exact name (case-sensitive)
# Config model name must match exactly what ollama list shows

VII. Advanced Configuration

Multi-Model Load Balancing

If you have multiple servers:

providers:
  - id: ollama-gpu1
    kind: openai-compatible
    baseUrl: http://192.168.1.100:11434/v1
    apiKey: ollama

  - id: ollama-gpu2
    kind: openai-compatible
    baseUrl: http://192.168.1.101:11434/v1
    apiKey: ollama

models:
  default: ollama-gpu1/qwen2.5:7b
  fallbacks:
    - ollama-gpu2/qwen2.5:7b

Custom Model Parameters

Create a Modelfile to adjust temperature, top_p, etc.:

cat > ~/qwen-creative.Modelfile << 'EOF'
FROM qwen2.5:7b

PARAMETER temperature 0.9
PARAMETER top_p 0.95
PARAMETER num_ctx 8192
EOF

ollama create qwen-creative -f ~/qwen-creative.Modelfile

Use in OpenClaw:

models:
  default: ollama/qwen-creative

VIII. Production Environment Tips

1. Systemd Service

# Create Ollama service
sudo tee /etc/systemd/system/ollama.service << 'EOF'
[Unit]
Description=Ollama Service
After=network.target

[Service]
Type=simple
ExecStart=/usr/local/bin/ollama serve
Restart=always
User=ollama
Environment="OLLAMA_HOST=0.0.0.0:11434"

[Install]
WantedBy=multi-user.target
EOF

sudo systemctl daemon-reload
sudo systemctl enable ollama
sudo systemctl start ollama

2. Performance Monitoring

# Real-time GPU monitoring
watch -n 1 nvidia-smi

# View Ollama logs
journalctl -u ollama -f

3. Backup & Restore

# Backup downloaded models
tar czf ollama-models-backup.tar.gz ~/.ollama/models

# Restore
tar xzf ollama-models-backup.tar.gz -C ~/

IX. Cloud Server Deployment

While this is “local” deployment, cloud servers offer better hardware and network:

🔥 Tencent Cloud GPU Instances — T4/V100 GPUs, pay-as-you-go for testing
🌍 Vultr High-Frequency CPU — Can run small models without GPU, hourly billing
💧 DigitalOcean — $200 free credit, great for long-term deployment

💡 Tip: GPU instances are expensive. For personal use, 7B-14B models + high-frequency CPU is often sufficient.

Summary

Solution	Cost	Performance	Privacy	Use Case
OpenClaw + Ollama	One-time hardware	Medium-High (depends on hardware)	⭐⭐⭐⭐⭐	Internal networks, sensitive data
OpenClaw + Anthropic	Pay-per-token	⭐⭐⭐⭐⭐	⭐⭐⭐	High-quality dialogue, complex reasoning
Hybrid	Low + on-demand	⭐⭐⭐⭐	⭐⭐⭐⭐	Local first, cloud fallback

Local deployment is ideal for long-term heavy use or sensitive data scenarios. If just experimenting, start with cloud APIs first before investing in hardware.

Related Guides:

This guide is continuously updated. Questions? Join the OpenClaw community.

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