Running Claude Code with a Local LLM: A Step-by-Step Guide

What if you want to run it using a local large language model (LLM) instead of relying on cloud-based APIs? By leveraging code-llmss, developers can set up a local LLM for enhanced privacy, offline use, and cost savings.

In this guide, we’ll walk through the process of integrating Claude Code with a local LLM, covering installation, configuration, and practical use cases.

Why Use a Local LLM with Claude Code?

Running a local LLM offers several advantages:

• Privacy – Keep your code and data on your local machine without sending queries to external servers.
• Reduced Costs – Avoid API call fees from cloud-based LLM services.
• Offline Capability – Work without an internet connection, making development more flexible.
• Customization – Fine-tune the model to better suit your coding needs.

Setting Up Claude Code with a Local LLM

To use Claude Code with a local LLM, we will be using code-llmss, an open-source project designed to provide local AI-assisted coding capabilities. Follow these steps to get started:

1. Install the Necessary Dependencies

First, make sure your system has the required dependencies installed. You’ll need:

• Python (3.8+ recommended)
• Git
• GPU support (optional, but recommended for performance)

Clone the repository:

git clone https://github.com/anders94/code-llmss.git
cd code-llmss

Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

Install dependencies:

pip install -r requirements.txt

2. Download a Local LLM

The next step is to download and set up a large language model. Code-llmss supports multiple local LLMs such as:

• Llama 2 – Open-source LLM from Meta.
• Mistral 7B – High-performance model optimized for coding.
• GPT-based local models – Various open-weight models.

To download a model:

python scripts/download_model.py --model mistral-7b

3. Configure the Local LLM

Once your model is downloaded, configure the system to use it for coding assistance:

Modify the config.yaml file:

llm:
  model: "mistral-7b"
  max_tokens: 2048
  temperature: 0.7
  enable_gpu: true

4. Running the Local LLM Server

Now, start the local LLM server that will process queries for Claude Code:

python scripts/start_server.py

This will launch the model and provide an API endpoint that can be used for coding assistance.

5. Connecting Claude Code to the Local LLM

Once the local LLM server is running, configure Claude Code to use it:

Modify the .env file:

LLM_API_URL=http://localhost:5000
LLM_MODEL=mistral-7b

Then restart Claude Code to ensure it picks up the new configuration.

How to Use Claude Code with a Local LLM

Now that Claude Code is running with a local LLM, here’s what you can do:

1. Generate Code Snippets

Ask Claude Code to generate specific code snippets:

“Write a function in Python that sorts a list of numbers using quicksort.”

2. Debug Code Locally

Paste in your code and ask:

“Why is this function returning None instead of a value?“

3. Optimize Performance

Run Claude Code against your local LLM to improve slow-running scripts:

“Optimize this SQL query for better performance.”

4. Automate Testing

Claude Code can generate and run tests for your code:

“Write a unit test for this function and fix any issues it finds.”

5. Work Offline Without an Internet Connection

Since everything runs locally, you can now code, debug, and optimize without needing access to the internet.

Advanced Use Cases

If you’re an advanced user, consider:

• Fine-tuning the LLM – Train the model on your own codebase for even better suggestions.
• Integrating with CI/CD Pipelines – Use Claude Code to automate bug fixes before deployment.
• Enhancing IDE Integration – Set up Claude Code with VS Code or JetBrains IDEs for seamless AI assistance.

The Future of Local AI Coding Assistants

Imagine a world where AI-assisted coding runs entirely on your local machine, automatically debugging, optimizing, and submitting pull requests overnight. With local LLMs, the future of development is shifting towards fully autonomous, on-device AI assistance.

This means:

• No more cloud dependencies – AI runs directly on your machine.
• Instant debugging – AI-powered suggestions improve coding in real time.
• Self-healing software – Future iterations could analyze error logs and generate fixes automatically.

Running Claude Code with a local LLM gives you control, flexibility, and enhanced privacy. Follow this guide to set up your own local AI coding assistant and start experimenting with its full capabilities.

For more details, check out code-llmss on GitHub.

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