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Integrating Local Gemma 4 LLMs with Claude Code Setup and Practical Use

4 min read

Integrating Local Gemma 4 LLMs with Claude Code: Setup and Practical Use

Clip title: Claude Code with Gemma 4 (How I Use It) Author / channel: Zero to MVP URL: https://www.youtube.com/watch?v=sKNq4CqWkT4

Summary

This video demonstrates how to integrate and utilize local Large Language Models (LLMs) with Claude Code, focusing on Google’s Gemma 4 models. The presenter, a software developer with over 20 years of experience, emphasizes that local LLMs are not a full replacement for paid cloud-based services like Gemini or Claude but rather a complementary tool. They are particularly useful for delegating tasks, breaking down complex issues, and providing continuity when usage limits on paid models are reached.

The initial segment guides viewers through the setup process, beginning with the installation of Claude Code via a simple terminal command. To enable local LLM integration, the video explains the necessity of running a local API server (such as LM Studio, though Ollama is also mentioned as an alternative). The key configuration involves setting two environment variables: ANTHROPIC_BASE_URL to point to the local API server’s address and ANTHROPIC_AUTH_TOKEN for authentication. For the demonstration, the presenter uses a MacBook Pro (M4 Pro chip, 24GB RAM) for a smaller Gemma 4 model and a desktop PC (AMD Ryzen 7, 128GB RAM, Nvidia GeForce RTX 4060 Ti 16GB) for a larger variant.

The practical demonstration involves creating a basic HTML task manager page and progressively adding functionality. With the smaller Gemma 4 model (7.5 billion parameters) running locally on the MacBook, the first task of generating the basic HTML page with styling is completed successfully in about 1.5 minutes. However, when tasked with adding interactivity (an input field, an “Add Task” button, and JavaScript to dynamically add tasks), the smaller model struggles. It produces code with a JavaScript error, and despite Claude Code’s attempts to self-correct, the error persists, requiring manual intervention from the presenter to fix a missing HTML tag.

Switching to the larger Gemma 4 model (26 billion parameters) running on the more powerful desktop PC, the performance notably improves. While the initial task takes longer (~3 minutes) due to the model’s larger size and resource requirements, the subsequent interactive tasks, including adding new tasks and marking them as complete, are handled successfully without errors in approximately 8 minutes per task. The video highlights Claude Code’s agentic nature, explaining that the seemingly longer “cooking” times are due to multiple interactions, validations, and iterative refinements between Claude Code and the LLM, rather than a single prompt-response cycle.

In conclusion, the video effectively demonstrates that pairing Claude Code with locally run LLMs is a viable and flexible approach for developers. While smaller local models may face challenges with complex coding tasks, larger models on suitable hardware can deliver impressive results. The key takeaway is the flexibility and control offered by local models as a powerful addition to a developer’s workflow, allowing users to leverage their own computing resources and preferred models while complementing the capabilities of cloud-based LLMs.

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