How to Use Claude Code for free (Ollama Guide)
The ultimate guide to running Claude Code without the subscription. Learn how to pair Anthropic's powerful CLI infrastructure with Ollama to run high-performance models like Qwen2.5-Coder locally on your machine. Enjoy unlimited tokens, total privacy, and 100% offline access—completely free, forever.
How to Use Claude Code FREE Forever: A Step-by-Step Ollama Setup Guide
Claude Code
has made it easier for developers to use their terminal, offering a strong command-line tool that can write code, fix bugs, and run commands. But using it often means dealing with API credits and subscription limits.
Imagine using the same features
offline, for free, with no limits. By pairing Claude Code with Ollama, you can run powerful open-source models right on your own computer.
This guide will show you how to set up a local LLM environment so you can use Claude Code without restrictions.
Why Run Claude Code Locally?
Before we get started, let’s look at why this setup is worth it:
Zero Costs:
No Anthropic API keys or monthly subscriptions required.
Total Privacy:
Your code stays on your machine; nothing is sent to the cloud.
No Limits:
Forget about "rate limits" or "token exhausted" messages.
Offline Access:
Work on your projects even without an internet connection.
Step 1: Install Ollama
Ollama is the main tool for this setup. It lets you run open-source Large Language Models (LLMs) directly on your computer.
Visit Ollama.com.
Click Download for your specific operating system (Windows, Mac, or Linux).
Follow the standard setup prompts.
Once you have installed it, you can open Ollama’s interface, but for this guide, we’ll mostly use the terminal.
Step 2: Choose and Download Your Model
Not every model works the same way. For the best results with Claude Code, choose a model that’s designed for coding tasks.
Recommended Models:
Ollama’s official guidance for Claude Code suggests these top models:
Qwen2.5-Coder:
Currently one of the highest-performing open-source coding models.
GLM-4:
Another strong contender for logic and syntax.
A Note on Hardware:
Model "intA model’s intelligence usually depends on its parameter size, like 7B, 30B, or 400B.
Models:
Run smoothly on most modern laptops.
30B+ Models:
Require significant VRAM (Video RAM). Even high-spec MacBook Pros could struggle with models with more than 40B parameters.
How to Install:
Launch your Terminal.
Type the following command to pull the model (using Qwen2.5-Coder 30B as an example):
ollama pull qwen2.5-coder:30b
Wait for the download to finish. To check if it works, type
ollama run qwen2.5-coder:30b
and send a quick "Hello".
Step 3: Install Claude Code
Although you won’t use Anthropic’s cloud models, you still need the
Claude Code software
as your interface and setup.
Download Claude Code from the official Anthropic website or use the installation command from the documentation. In your terminal, enter:
npm install -g @anthropic-ai/claude-code
Step 4: Connecting Claude Code to Ollama
This is where the wonder occurs. Here’s the key step: launch Claude Code with a special flag so it uses your local Ollama models instead of the Anthropic API.erminal.
ollama launch claude --config.
The terminal will display a list of your locally installed models.
Select the model you just downloaded (e.g.,Qwen2.5-Coder).
When asked "Do you want to launch Claude Code now?", select Yes.
Step 5: Testing Your Local Setup
Once the environment starts, you’ll see a message confirming that the local model is running, such as "
Running Qwen 2.5 Coder 30B".
Security Note:
Claude Code may ask for permission to read or write files in your directory. This is necessary for programming tasks, but always ensure you are running it in a safe project folder.
To test your setup, try a simple question like
"What is 3 + 3?"
or ask it to
"Create a Python script for a basic calculator."
The Trade-off: Speed vs. This method is free and private, but there’s a trade-off. Running a large model on your own computer needs strong hardware. The model will be fast, but it might have trouble with very complex code.x code logic.
If you choose a bigger model, it will be smarter but might take longer to respond, depending on your computer’s CPU or GPU
Conclusion
With this Ollama setup, you avoid the limits of paid AI services. Now you have a powerful AI coding assistant that runs fully on your own computer. Whether you’re building software, working with databases, or connecting APIs, you can do it all for free, with no time limits.
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