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Free Claude Code? Here's How I Run It Locally - Free Playbook

Every builder should learn how to run claude code for free

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  • Claude Code

Run Claude Code for Free with Ollama (2026 Guide)

July 2026 · 5 min read

TL;DR

You can use Claude Code with local AI models through Ollama, meaning you don't pay API costs for every prompt. It takes about 10 minutes to set up, runs on your own machine, and works great for everyday coding tasks. It's not a replacement for frontier models, but it's an excellent option for bug fixes, refactoring, code explanations, and offline development.

Why are people talking about "Free Claude Code"?

Normally, Claude Code sends every request to Anthropic's servers.

With Ollama, you can point Claude Code to a local model running on your own computer instead. The Claude Code interface stays the same, but the AI generating the responses runs on your laptop.

That means:

  • ✅ No API costs
  • ✅ Works offline
  • ✅ Your code stays on your machine
  • ✅ Perfect for experimenting with local AI

One important thing to know: you're not running Anthropic's Claude model locally. You're using Claude Code with a compatible local model like Qwen Coder.

Should you use it?

It's a great choice if you:

  • Want to avoid API costs
  • Build AI agents or automations
  • Travel or work without reliable internet
  • Want to keep your code private
  • Want to learn how local AI works

You should probably stick with cloud models if you:

  • Regularly work on massive codebases
  • Need the strongest reasoning for architecture decisions
  • Already rely on Claude for complex multi-file development

For most everyday coding tasks, local models are surprisingly capable. For the hardest engineering problems, frontier models still have the edge.

What you'll need

Before you start, make sure you have:

  • A Mac (this guide is written for macOS)
  • Claude Code installed
  • Around 10 minutes
  • 8GB+ RAM (16GB or more is recommended for larger models)

Step 1 — Install Ollama

Download Ollama from:

https://ollama.com

Install it like any other application and launch it once.

Ollama runs quietly in the background and hosts AI models locally.

Step 2 — Download a coding model

If you have a newer Mac with plenty of RAM:

ollama pull qwen3-coder:30b

If you're using an older Mac or have limited memory:

ollama pull qwen2.5-coder:1.5b

The download only happens once.

Step 3 — Point Claude Code to Ollama

Open Terminal and run:

export ANTHROPIC_BASE_URL=http://localhost:11434
export ANTHROPIC_AUTH_TOKEN=ollama
export ANTHROPIC_API_KEY=""

These environment variables tell Claude Code to send requests to your local Ollama server instead of Anthropic.

Step 4 — Increase the context window

Run:

export OLLAMA_CONTEXT_LENGTH=32768

This allows the local model to remember much more of your conversation and code while you work.

Step 5 — Improve response speed

Run:

export CLAUDE_CODE_ATTRIBUTION_HEADER=0

Without this, some users notice slower responses because of additional attribution headers being sent with each request.

Step 6 — Make the settings permanent

Open your shell configuration:

open ~/.zshrc

Paste the following at the bottom:

export ANTHROPIC_BASE_URL=http://localhost:11434
export ANTHROPIC_AUTH_TOKEN=ollama
export ANTHROPIC_API_KEY=""
export OLLAMA_CONTEXT_LENGTH=32768
export CLAUDE_CODE_ATTRIBUTION_HEADER=0

Save the file and restart your terminal.

Step 7 — Start Claude Code

Simply run:

claude

If everything is configured correctly, Claude Code will now use your local model instead of making API requests.

How good is it?

Great for

  • Fixing bugs
  • Refactoring code
  • Writing helper functions
  • Explaining code
  • Learning new frameworks
  • Automation scripts
  • Offline development

Not ideal for

  • Complex architecture decisions
  • Large multi-file projects
  • Deep reasoning across huge codebases
  • Tasks where accuracy is more important than cost

Think of local models as an excellent everyday developer tool—not a complete replacement for the latest frontier models.

Why this matters

The biggest takeaway isn't that you can code for free.

It's that you now have options.

Instead of sending every coding task to an expensive cloud model, you can decide which tasks are worth paying for and which ones can run locally.

Many companies already mix different AI models depending on the task. Simpler jobs like code explanation, classification, and repetitive automation often don't need the most expensive model available.

Learning how to run AI locally gives you more flexibility, better privacy, and lower costs as your projects grow.

Final thoughts

If you've been seeing people talk about "Free Claude Code," this is what they mean.

You're using the familiar Claude Code interface while running a local AI model through Ollama. It won't replace frontier models for every task, but it's a fantastic setup for everyday coding, learning, and experimenting without worrying about API costs.

If you're a developer or builder, it's worth trying—even if you eventually switch back to cloud models for more demanding work.