How to run a local coding agent with Gemma 4 and Pi | Patrick Loeber

Patrick Loeber, a prominent Google developer advocate, has released a tutorial on setting up a local coding agent using Google's Gemma 4 model and the Pi framework. The guide targets developers seeking privacy and control over their AI-assisted coding workflows, bypassing cloud dependencies. Loeber's approach uses llama.cpp for model inference, a lightweight C++ implementation optimized for running large language models on consumer hardware. The tutorial walks through installing Pi, downloading and quantizing Gemma 4, and integrating the agent with popular code editors like VS Code. Key highlights include the ability to run the agent entirely offline, with no API costs, and support for context-specific code completions and debugging suggestions.

The setup leverages Gemma 4's 7B parameter variant, which balances performance and resource efficiency, requiring approximately 8GB of VRAM for smooth operation. Loeber demonstrates how to configure the agent for tasks like refactoring, generating boilerplate, and explaining code snippets. The tutorial also covers optimizing inference speed through quantization techniques like Q4_K_M, reducing memory usage while maintaining accuracy. This local-first approach appeals to developers in regulated industries or those with strict data privacy requirements, as no code or prompts leave the machine. The community has responded positively, with many users sharing similar setups using tools like Ollama and LM Studio, highlighting the growing demand for self-hosted AI development tools.