Does OpenClaw Work with Local LLMs Like Ollama?

Mar 23, 2026

Six days. That’s how long I spent trying to make OpenClaw work with my local LLM setup before I finally got it running smoothly.

The promise was enticing: free AI inference, complete privacy, no API tokens to manage. The reality? A gauntlet of hardware compatibility issues, memory crashes, and configuration headaches that nearly made me give up entirely.

But I did eventually get it working. And if you’re considering this path, here’s everything I wish I had known before starting.

The Problem: Why Local LLMs Matter for OpenClaw

OpenClaw is an AI-powered scheduling and automation tool. Run it 24/7, and those cloud API tokens add up fast. We’re talking $50-300+ per month if you’re using GPT-4 or Claude extensively.

Plus, there’s the privacy angle. Every piece of data you send to OpenClaw flows through external APIs. For businesses handling sensitive information, that’s a non-starter.

Local LLMs solve both problems. Zero per-token costs. Complete data privacy. The catch? You’ll need to wrestle with hardware and configuration first.

My First Attempt: The Intel Graphics Disaster

I started with what I had: a laptop with Intel integrated graphics.

CPU: Intel Core i7-12700H
GPU: Intel Iris Xe (integrated)
RAM: 32GB DDR5
OS: Windows 11 + WSL2

I installed Ollama, pulled Qwen 2.5 7B, and pointed OpenClaw at http://localhost:11434.

It crashed within 10 minutes.

Ollama would segfault randomly. Memory usage would spike to 100% and stay there. OpenClaw would timeout waiting for responses. I spent days updating drivers, tweaking configuration files, and trying different model versions.

The root cause? Intel GPU support for LLM inference is still experimental and unstable. The community knows this, but the information is scattered across forums and GitHub issues.

The Solution: Proper Hardware Matters

After my Intel disaster, I switched to a dedicated NVIDIA setup. The difference was night and day.

┌─────────────────┬──────────────────┬───────────────────┬──────────────────┐
│ Hardware Level  │ Chat Model       │ Coding Model      │ Quality Level    │
├─────────────────┼──────────────────┼───────────────────┼──────────────────┤
│ Budget (3060)   │ qwen2.5:7b       │ qwen2.5-coder:7b  │ Simple tasks     │
│ Mid (3070/3080) │ qwen2.5:7b       │ qwen3.5:9b        │ Balanced         │
│ High (4090)     │ qwen2.5:14b      │ qwen2.5-coder:14b │ Near-cloud       │
└─────────────────┴──────────────────┴───────────────────┴──────────────────┘

Minimum requirements I’d recommend:

GPU: NVIDIA 3060 12GB (VRAM is the bottleneck, not compute)
RAM: 32GB system memory
Storage: 50GB+ SSD for models and cache

The 12GB VRAM on a 3060 lets you run 7B models comfortably with some headroom for context. 8GB cards will struggle with longer conversations.

Setting Up OpenClaw with Ollama

Once you have proper hardware, the setup is straightforward.

Step 1: Install and Configure Ollama

# Install Ollama (Linux/macOS)
curl -fsSL https://ollama.ai/install.sh | sh

# Pull a recommended model
ollama pull qwen2.5:7b

# Verify it's running
ollama list

Ollama starts a local API server at http://localhost:11434 by default.

Step 2: Configure OpenClaw

In your OpenClaw configuration, point it to your local Ollama instance:

{
  "provider": "ollama",
  "baseUrl": "http://localhost:11434",
  "model": "qwen2.5:7b",
  "temperature": 0.7,
  "maxTokens": 4096,
  "timeout": 120000
}

Key settings explained:

baseUrl: Points to Ollama’s local API
model: Use the exact name from ollama list
timeout: Increase this! Local inference is slower than cloud APIs

Step 3: Test the Connection

# Quick test to verify Ollama responds
curl http://localhost:11434/api/generate -d '{
  "model": "qwen2.5:7b",
  "prompt": "Hello, respond with just OK"
}'

If you get a response, OpenClaw should work.

The Hybrid Approach: Best of Both Worlds

Local models have limitations. They struggle with complex reasoning, architecture decisions, and tasks requiring extensive context. But they’re great for routine operations.

A smarter approach: use cloud APIs for complex tasks and local models for everything else.

# Pseudo-code for hybrid model selection
def select_model(task_complexity):
    if task_complexity in ["architecture", "complex_reasoning", "large_context"]:
        return "gemini-pro"  # Or your preferred cloud API
    else:
        return "ollama:qwen2.5:7b"

One Reddit user shared their setup:

“I have mine set to use Gemini free until it exhausts tokens then my local qwen9b model.”

This approach maximizes your free cloud tier while falling back to local inference when needed.

Real Performance Comparison

I ran benchmarks comparing local Qwen 2.5 7B against GPT-4o-mini on typical OpenClaw tasks.

┌─────────────────────┬─────────────────┬──────────────────┬─────────────────┐
│ Metric              │ GPT-4o-mini     │ Qwen 2.5 7B      │ Difference      │
├─────────────────────┼─────────────────┼──────────────────┼─────────────────┤
│ Response time       │ 1-2 seconds     │ 8-15 seconds     │ 8x slower       │
│ Quality (simple)    │ Excellent       │ Good             │ Noticeable      │
│ Quality (complex)   │ Excellent       │ Struggles        │ Significant     │
│ Cost per 1M tokens  │ $0.15           │ $0 (electricity) │ ~$0.15 saved    │
│ Privacy             │ External        │ Complete         │ Full control    │
└─────────────────────┴─────────────────┴──────────────────┴─────────────────┘

My takeaway: For OpenClaw’s scheduling and routine tasks, local Qwen performs adequately. For anything requiring nuanced reasoning, I switch to a cloud API.

Lessons Learned

1. Don’t Fight Intel Graphics

I wasted six days on this. If you only have Intel integrated graphics, either:

Use a smaller model (Qwen 2.5 3B or Phi-3)
Accept cloud API costs
Rent a GPU instance (RunPod, Lambda Labs)

2. Monitor Memory Aggressively

Local LLMs are memory hogs. Set up monitoring:

# Watch GPU memory usage
watch -n 1 nvidia-smi

# Watch system RAM
htop

If you see memory creeping up without releasing, you have a leak. Restart Ollama.

3. Temperature Matters for Quality

For OpenClaw’s structured outputs, lower temperature helps:

{
  "creative_tasks": 0.7,
  "scheduling_planning": 0.3,
  "code_generation": 0.2,
  "factual_qa": 0.1
}

4. Use Ollama for Embeddings Too

OpenClaw can use local embeddings for memory, not just chat:

# Pull an embedding model
ollama pull nomic-embed-text

# Configure in OpenClaw for local memory

This keeps all your data local, including the memory/context storage.

When to Stick with Cloud APIs

Be honest with yourself about your use case:

Stick with cloud APIs if:

You need consistent, high-quality outputs
Your time is worth more than the API costs
You’re doing complex, multi-step reasoning
You don’t have a dedicated NVIDIA GPU

Go local if:

Privacy is non-negotiable
You’re running 24/7 automation
You have proper hardware
You can tolerate occasional quality dips

Final Configuration That Works

After all the trial and error, here’s my production setup:

┌──────────────────────────────────────────────────────────────────┐
│  OpenClaw Configuration                                           │
├──────────────────────────────────────────────────────────────────┤
│  Primary:   ollama:qwen2.5:7b (routine tasks)                     │
│  Fallback:  gemini-flash (complex tasks, free tier)              │
│  Embeddings: ollama:nomic-embed-text (local memory)               │
│  Timeout:   180 seconds (local is slower)                         │
│  Temperature: 0.3 (structured outputs)                            │
└──────────────────────────────────────────────────────────────────┘

This hybrid approach gives me:

Zero cost for ~70% of operations (local)
Quality fallback for complex tasks (cloud free tier)
Complete privacy for embeddings and memory

The Bottom Line

Yes, OpenClaw works with local LLMs. But “works” comes with caveats:

Hardware is non-negotiable. Skip Intel. Get NVIDIA with 12GB+ VRAM.
Expect a learning curve. Budget 2-3 days for initial setup and tuning.
Quality varies. Local models are adequate for routine tasks, not complex reasoning.
Hybrid is optimal. Combine local for cost savings with cloud for quality.

The promise of free, private AI inference is real. Just don’t expect to get there without investing in proper hardware and configuration time.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!