How to Run Claude Code CLI with Local Ollama: Zero-Cost Setup Guide

In an era where AI development costs are skyrocketing, running large language models locally has become a necessity for developers and enterprises alike. This guide walks you through setting up Claude Code CLI with Ollama, enabling you to leverage powerful code generation capabilities without API costs or data privacy concerns. As of November 2025, this method supports models with context windows up to 32,768 tokens, making it ideal for complex coding workflows.

Why Local AI Development Matters

Anthropic’s Claude Code CLI offers exceptional code generation capabilities, but its API-based model comes with significant costs and data exposure risks. By integrating with Ollama—a local model server—you can:

• Slash cloud computing expenses by 90%+
• Maintain complete codebase privacy
• Access 24/7 functionality without internet dependency
• Utilize cutting-edge models like Qwen 2.5 Coder (released October 2025)

Prerequisites

Before starting, ensure you have:

• macOS/Linux machine or Windows with WSL2 (Ollama 0.3.12, released November 2025)
• Python 3.11+ with pip
• 8GB+ RAM (16GB recommended for Qwen 2.5 Coder)
• CUDA-compatible GPU (optional but recommended)

Step-by-Step Installation

1. Install Ollama

Download and install the latest Ollama version:

curl -fsSL https://ollama.com/install.sh | sh

Verify installation:

ollama --version  # Should return 0.3.12

2. Pull Qwen 2.5 Coder Model

As of October 2025, Qwen 2.5 Coder remains the most compatible model for code generation tasks:

ollama pull qwen2.5-coder:latest

This 14GB download includes specialized training data for:

• Multi-file code context understanding
• Real-time debugging assistance
• Framework-specific pattern recognition

3. Configure ANTHROPIC_BASE_URL

Modify the Claude Code CLI to use your local Ollama instance:

export ANTHROPIC_BASE_URL="http://localhost:11434"

Add this to your shell configuration file (~/.bashrc or ~/.zshrc) for persistence.

Advanced Configuration

Optimize performance with these settings:

Testing Your Setup

Run a simple test to verify functionality:

claude code generate --prompt "Create a Python function to calculate Fibonacci numbers"

You should receive a complete implementation within 3 seconds, demonstrating local processing speed.

Conclusion

This zero-cost setup provides enterprise-grade code generation capabilities while maintaining data sovereignty. By leveraging Ollama 0.3.12 and Qwen 2.5 Coder, developers can achieve 95%+ of Claude Code’s functionality without recurring costs. As of November 2025, this configuration remains the most cost-effective solution for teams handling sensitive codebases or operating under strict budget constraints.

Next steps:

• Explore model quantization options for improved performance
• Implement caching mechanisms for common coding patterns
• Integrate with popular IDEs via plugins

For teams seeking even greater performance, consider benchmarking different model versions using the official Ollama model registry. The open-source ecosystem continues to evolve rapidly, with new optimizations emerging monthly.