How to Run Claude Code Offline for Free Using Ollama

As AI becomes a core part of the development workflow, many developers are looking for ways to use powerful tools like Claude Code without constant internet connectivity or expensive API costs. By running models locally, you gain privacy, eliminate subscription limits, and can continue working even when you are offline.

This guide will show you how to leverage Ollama to run Claude Code infrastructure on your own machine. You will learn how to set up the necessary environment and choose the best open-source models to replicate the Claude experience locally.

What is Claude Code and Ollama

Claude Code

Claude Code is a command-line interface (CLI) tool that allows developers to interact with AI models directly within their terminal. It is designed to help with coding tasks, file manipulation, and project management. While typically used with Anthropic's hosted models, the CLI infrastructure can be adapted to work with local backends.

Ollama

Ollama is an open-source framework that allows users to run large language models (LLMs) locally on macOS, Linux, and Windows. It simplifies the process of downloading and managing models like Llama 3, Mistral, and Qwen, providing a simple API that other tools can connect to.

Hardware and Software Requirements

Processor: Modern CPU (Apple M-series or Intel/AMD with high core counts).
RAM: At least 16GB for small models; 32GB+ recommended for larger models.
VRAM: A dedicated GPU with at least 8GB of VRAM is ideal for smooth performance.
Storage: 10GB to 50GB of free space depending on the models you choose.
Software: A terminal emulator and basic familiarity with command-line interfaces.

Step-by-Step Guide to Setting Up Claude Code Locally

Step 1: Install Ollama

The first step is to download and install Ollama, which will act as the engine for your local models.

Navigate to ollama.com.
Click the Download button.
Follow the installation prompts for your specific operating system.
Launch the application to ensure the background service is running.

Step 2: Choose and Download a Model

You need a model specifically trained for coding tasks. In this guide, we recommend the Qwen2.5-Coder series, as it currently offers high performance for local code generation.

Open your terminal.
Search for the model on the Ollama library or use the following command to pull the recommended 7B or 32B version:

ollama pull qwen2.5-coder:32b

Note: If your computer has limited RAM, you may want to use a smaller version like qwen2.5-coder:7b.

Step 3: Install Claude Code CLI

Even though you are using a local model, you still need the Claude Code software to manage the interaction environment.

You can install it via npm or by following the instructions on the official repository. In your terminal, run:

npm install -g @anthropic-ai/claude-code

Step 4: Connect Claude Code to Ollama

Once both Ollama and Claude Code are installed, you need to launch the environment using a configuration that points to your local Ollama instance.

Run the following command to launch the interface:

ollama launch claude --config

From the resulting menu, select the model you downloaded (e.g., Qwen2.5-Coder 32B). The system will ask for permission to read and write files in your directory; confirm this to proceed.

Advantages of Using Local AI for Coding

Data Privacy: Your code and data never leave your local machine, which is critical for proprietary business projects.
No Costs: You do not need an Anthropic API key or a monthly subscription to use local models.
Offline Access: You can build software in environments without an internet connection.
No Rate Limits: Unlike cloud services, you can send as many queries as your hardware can handle without being throttled.

Limitations and Trade-offs

Performance Speed: Local models are often slower than cloud-based versions, especially if you are not using a high-end GPU.
Intelligence Gap: While open-source models like Qwen2.5-Coder are excellent, they may not yet fully match the reasoning capabilities of the hosted Claude 3.5 Sonnet.
Resource Intensive: Running these models will drain battery life quickly on laptops and may cause high fan noise due to heat generation.

Final Summary

Running Claude Code offline using Ollama is a powerful solution for developers who prioritize privacy and cost-efficiency. By following the steps above, you can transform your local machine into a private AI development environment. While there is a trade-off in terms of speed and raw intelligence compared to cloud models, the freedom of unlimited, offline use makes it a compelling choice for many workflows.