Skip to Content

Running Claude Code with Local Models Using Ollama

Learn what Claude code and Ollama are, how to set up and run Claude code using local models with Ollama, and why using local models enhances privacy, performance, and control.
4 February 2026 by
Suraj Barman

What is Claude Code and Ollama?

Claude code refers to scripts or prompts designed for Anthropic's Claude family of large language models (LLMs). Ollama is an open‑source runtime that lets you download, host, and interact with LLMs on your own hardware.

  • Claude code: Prompt‑oriented programs that leverage Claude's conversational and reasoning abilities.
  • Ollama: A lightweight server that manages model binaries, provides a REST‑like API, and abstracts hardware details.

How to Run Claude Code Locally with Ollama

Follow these steps to set up a local environment and execute Claude code.

  • 1. Install Ollama
    • Download the appropriate installer for your OS from .
    • Run the installer and verify the service is running (e.g., ollama serve).
  • 2. Pull a Claude‑compatible model
    • Ollama supports community‑built Claude‑compatible models (e.g., claude-2.0-ollama).
    • Execute ollama pull claude-2.0-ollama to download the model.
  • 3. Prepare your Claude code
    • Create a file my_prompt.claude containing the prompt or script.
    • Ensure the prompt follows Claude's formatting guidelines (system, user, assistant messages).
  • 4. Invoke the model via Ollama CLI
    • Run ollama run claude-2.0-ollama -f my_prompt.claude.
    • The CLI streams the model’s response to your terminal.
  • 5. Use the HTTP API (optional)
    • Send a POST request to with JSON payload:
    • {"model":"claude-2.0-ollama","prompt":""}
    • Parse the JSON response to retrieve the generated text.
  • 6. Automate with scripts
    • Wrap the CLI or API call in a shell or Python script for batch processing.
    • Example (Python):
    • import requests
payload={"model":"claude-2.0-ollama","prompt":"Explain quantum entanglement."}
resp=requests.post('
print(resp.json()['response'])

Why Use Local Models with Ollama?

Running Claude code locally offers several strategic advantages.

  • Privacy and Data Security: Sensitive prompts never leave your hardware, reducing exposure to third‑party data collection.
  • Performance and Latency: Local inference eliminates network round‑trips, delivering faster response times, especially for large prompts.
  • Cost Control: No per‑token API fees; you only incur hardware and electricity costs.
  • Customization: Combine Claude‑compatible models with your own fine‑tuned checkpoints or adapters.
  • Reliability: Operate offline or in isolated environments where internet access is restricted.