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OpenClaw with Groq, Ollama & Open Models: Complete Self-Hosted AI Setup

February 26, 2026 · AI Tools · Open Source

One of OpenClaw's superpowers is flexibility: you can connect it to premium APIs like Claude or GPT-4, or go fully open-source with free models from Hugging Face. This guide explores the best open-source language models for OpenClaw, how to run them locally via Ollama or Groq, and when to use each approach. Whether you're a student on a budget, a researcher needing full model transparency, or a developer building AI infrastructure, self-hosted OpenClaw with open models gives you unprecedented control and privacy.

Why Open Models Matter for OpenClaw

Open-source LLMs like Mistral 7B and Llama2 have three massive advantages over commercial APIs:

  • Cost: Zero API fees. Download once, run infinitely on your hardware.
  • Privacy: Conversations never leave your machine. No telemetry, no data collection.
  • Control: Fine-tune models on your data, modify behavior, audit training.

The downside: smaller open models (7B–13B parameters) are less intelligent than Claude or GPT-4 (100B+). But for many tasks—customer support, documentation, brainstorming—they're more than sufficient.

Top Open Models for OpenClaw 2026

Mistral 7B Instruct

Fastest, most practical for production.

• Parameters: 7B • Memory: 4–6 GB • Speed: 50–100 tokens/sec • Cost: Free

Best for: Real-time Telegram bots, customer service, code assistance. Mistral 7B is remarkably capable for its size and runs on almost any hardware.

Llama2 13B Chat

Meta's balanced open model.

• Parameters: 13B • Memory: 8–10 GB • Speed: 30–60 tokens/sec • Cost: Free

Best for: Conversational AI, long-form content, structured tasks. Larger than Mistral, slower but more capable. Requires decent GPU or large CPU system.

Neural Chat 7B

Intel's fast conversational model.

• Parameters: 7B • Memory: 4–6 GB • Speed: 80–120 tokens/sec • Cost: Free

Best for: Chatbots, lightweight deployments, resource-constrained devices like Raspberry Pi.

Code Llama 13B

Specialized for code generation.

• Parameters: 13B • Memory: 8–10 GB • Speed: 25–50 tokens/sec • Cost: Free

Best for: Developers using OpenClaw as a coding assistant. Trained on programming patterns, understands git diffs and PRs.

Dolphin Mixtral (MoE)

Mixture-of-Experts for advanced reasoning.

• Parameters: 56B (effective 12B) • Memory: 20 GB • Speed: 40–80 tokens/sec • Cost: Free

Best for: Complex reasoning, analysis, creative tasks. Mixture-of-Experts uses less compute than dense models of equivalent quality.

1 Set Up Ollama for Local Model Running

Ollama is the easiest way to run open models locally. Download it for Mac, Linux, or Windows:

# macOS
brew install ollama
# Or download from ollama.ai

Start the Ollama server:

ollama serve

In another terminal, download a model:

ollama pull mistral:7b-instruct

Ollama runs a local API on http://localhost:11434. Your models are ready instantly.

2 Connect OpenClaw to Your Local Model

During OpenClaw onboarding, specify your Ollama endpoint:

openclaw onboard
# When prompted for model provider, enter: ollama
# When prompted for model URL, enter: http://localhost:11434
# When prompted for model name, enter: mistral:7b-instruct

OpenClaw will now route all AI requests to your local Ollama instance. All conversations stay on your machine—zero data leaves.

3 Using Groq for Ultra-Fast Inference

Groq is a specialized AI chip company offering free API access to open models with extreme speed. While not local, Groq provides privacy and speed superior to commercial APIs. Create a Groq account and get a free API key:

openclaw onboard
# Select provider: groq
# Enter your Groq API key
# Choose a model (mixtral-8x7b, llama2-70b, etc.)

Groq delivers Llama2 70B and Mixtral responses in under 50ms—faster than many GPU systems. Ideal if you want speed without managing local hardware.

Choosing Between Ollama and Groq

Use Ollama (Local): Maximum privacy, zero dependencies on external services, ideal for sensitive data or offline operation. Trade-off: requires adequate hardware (Mac mini, gaming PC, or VPS with GPU).

Use Groq (Cloud): Ultra-fast inference, no hardware investment, free tier for development. Trade-off: some data leaves your machine (though Groq is privacy-respecting), requires internet.

Use Both: Run Mistral 7B locally for everyday tasks, use Groq for complex reasoning or when performance matters. Scale dynamically.

Hardware Requirements for Ollama

Mistral 7B: 6 GB RAM minimum (works on Raspberry Pi 5 or MacBook M1+)

Llama2 13B: 10 GB RAM (requires Mac mini M2+ or desktop GPU)

Mixtral MoE: 20 GB RAM (needs Mac mini M4 or high-end GPU)

GPU Acceleration: NVIDIA (CUDA), AMD (ROCm), or Apple Silicon (Metal) dramatically speeds up inference. Without GPU, CPU-only inference is 5–10x slower.

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FAQ

Can I mix local Ollama and Groq models?

Yes. You can configure OpenClaw to route simple tasks to Ollama (fast, local, free) and reserve Groq for complex reasoning. This optimizes cost and latency across your AI workflows.

How do I fine-tune an open model for my domain?

Download a base model like Llama2, use tools like Unsloth or Hugging Face transformers to fine-tune on your data, then run it locally via Ollama. This is advanced but gives you a fully personalized AI assistant.

Is Groq really free?

Groq offers a free tier with reasonable rate limits (perfect for personal use and small bots). Enterprise tiers exist for production workloads. Check their pricing for your use case.

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