Unlimited Coding AI: How to Run Claude Code with Local Models for Free
In the world of IT and Cybersecurity, the "Cloud" is often brings up privacy concerns. While tools like Claude Code have revolutionized how we write code, the costs and privacy concerns of sending your personal or proprietary logic to a third-party server can be a significant barrier.
But what if you could have the best of both worlds? By pairing Claude Code (Anthropic's command-line interface) with Ollama (An open source LLM user interface), you can leverage the power of high-performance local models like Qwen2.5-Coder and GLM-4 without spending a dime on tokens and keeping your code and ideas local to your device.
Whether you’re looking to air-gap your development environment for better security or simply want to maximize your hardware's VRAM, this guide breaks down how to set up a localized coding powerhouse on your own machine.
The Hardware Check
Before diving in, take a look at your specs. The beauty of Ollama is that it scales to your machine. For context, if you’re running on a system with 16GB of RAM, you'll want to stick to models in the 7B to 14B parameter range for the smoothest experience. If you have 24GB+ of VRAM, you can start looking at the heavy hitters like the 30B+ models.
Step 1: Get Ollama Running
First, you need the engine. Head over to
ollama --version
Step 2: Pull Your Coding Models
You’ll need a model that "speaks" code fluently. Based on current benchmarks, here are the top picks to pull:
For 16GB Systems:
ollama pull qwen2.5-coder:7b(Great balance of speed and logic).For High-End Systems:
ollama pull glm4orollama pull deepseek-coder-v2.
Step 3: Launching Claude Code
Once your models are downloaded, navigate to your project folder. Instead of the standard cloud launch, use the Ollama bridge:
ollama launch claude
If you want to toggle between different local models you’ve downloaded, use the config flag to select your "driver" for the session:
ollama launch claude --config
Step 4: Pro-Tip – The "Micro-Tasking" Workflow
Local models are powerful, but they can occasionally lose the "big picture" in massive files. To get the best results:
Isolate Components: Use a tool like Storybook to break your UI into tiny pieces.
Use Plan Mode: Type
/planbefore asking for code. This forces the model to think through the logic before it starts typing, which significantly boosts accuracy.Visual Refinement: If a local model struggles with a complex CSS layout, you can temporarily switch to a cloud tier (like GLM-4.7's free tier) for a "high-resolution" polish, then go back to local for the heavy lifting.
