Running Claude Code with Ollama Locally on Windows: A Step-by-Step Guide

### Running Claude Code with Ollama Locally on Windows: A Step-by-Step Guide

AI-powered coding assistants are becoming useful tools for software developers. In this tutorial, I will explain how I installed Ollama, configured Claude Code to use a local language model, and tested it on a simple programming task.

The goal was to explore local AI-assisted coding and understand its benefits and limitations.

I downloaded and installed Ollama from its official website.

I installed Claude Code by following the official installation instructions.

4. Downloading the Local Model I selected a lightweight coding model suitable for my computer.

This integration allows Claude Code to use the selected Ollama model instead of relying on a hosted Claude model for inference.

To test the local Ollama model, I asked it to generate a simple Python program.

The model generated the following Python code:

I reviewed the generated code and tested the program to verify its output.

During this experiment, I encountered an API error when attempting to use Claude Code with Ollama. However, I was able to interact directly with the local Qwen3.5:4b model and obtain a valid code example.

The experiment highlighted the difference between running a local language model directly and integrating it with a coding agent. It also showed that generated code should be saved, executed, and tested before being considered verified.

The performance and usability of local AI tools depend on the model, available hardware resources, and configuration.

For this experiment, I chose Ollama with the Qwen3.5:4b model because I wanted to explore how AI-assisted coding could work without relying entirely on a cloud-based service.

My computer has 8 GB of RAM and an Intel Core i5-8350U processor, so I selected a relatively small model suitable for experimenting on limited hardware.

The main reasons for choosing a local model were:

Privacy: Local prompts and responses can stay on my computer when using local inference.

Cost: I could experiment without paying a cloud API fee for each local request.

Learning: I wanted to understand model installation, execution, and integration with development tools.

Offline use: Once the model is downloaded, basic local inference does not require an internet connection.

However, local execution also has limitations. My computer has limited memory and no powerful dedicated GPU, so larger models and complex coding-agent tasks may be slow or impractical.

For my use case, local AI was the best starting point for learning and experimentation, while cloud AI would be more suitable for demanding development tasks when stronger models and hardware are needed.

This project allowed me to explore local AI-assisted coding using Ollama and Qwen3.5:4b. I learned how to run a language model locally, prompt it to generate a simple program, and identify the additional configuration needed to connect a model to a coding agent.

The comparison between local and cloud AI also showed me that there is no universally better option. The right choice depends on privacy requirements, available hardware, cost, performance, and the complexity of the task.

Although the Claude Code integration encountered an API error during my experiment, this challenge provided a useful learning opportunity and highlighted the importance of checking compatibility, configuration, and generated code.

https://docs.ollama.com/integrations/claude-code

https://code.claude.com/docs/en/overview