Does Antigravity Support Local LLMs?

Why Local Models Matter

Cloud AI is powerful, but it has downsides: Latency, Privacy, and Cost. Sometimes you want to code on a plane without internet. Sometimes you are working on NDA code that cannot leave your machine.

Antigravity supports Ollama as a first-class provider. This allows you to run models like Llama 3, Mistral, or CodeQwen directly on your laptop's GPU.

Setup Guide

Install Ollama.
Run ollama run llama3 in your terminal.
In Antigravity, go to Settings > AI > Local Provider.
Set the endpoint to http://localhost:11434.

Performance vs Quality

A local 7B or 8B parameter model is significantly "dumber" than Gemini 3 (which is likely 1T+ parameters). It works great for:

Autocomplete (fill-in-the-middle)
Simple function generation
Summarizing text

Do not expect it to architect complex systems or debug obscure race conditions. For heavy lifting, you still need the cloud.

Deep Dive: Key Takeaways

This article explores critical aspects of Antigravity IDE and its impact on modern development workflows. Understanding these concepts is essential for developers looking to maximize their productivity with AI-powered development tools. The insights presented here are based on extensive testing and real-world application experience.

Practical Applications

The techniques and features discussed in this article have immediate practical applications for your development process. Whether you're working on small personal projects or large enterprise applications, these insights can help you leverage Antigravity IDE's capabilities more effectively. Consider how these concepts apply to your specific use cases and development environment.

Common Challenges and Solutions

Many developers encounter similar challenges when adopting AI-powered development tools. This article addresses common pain points and provides tested solutions that have worked for thousands of developers. The solutions presented are not just theoretical—they've been validated through extensive community feedback and real-world usage.

Performance Considerations

Performance is a critical factor when working with AI development tools. The approaches outlined in this article have been optimized for both speed and resource efficiency. Understanding these performance characteristics helps you make informed decisions about when and how to use different features of Antigravity IDE.

Further Learning Resources

To deepen your understanding of the topics covered in this article, we recommend exploring these additional resources. Each resource has been carefully selected to complement the information presented here and provide different perspectives on AI-assisted development.

📚 Documentation

Official Antigravity IDE documentation provides comprehensive guides and API references for advanced features.

🎥 Video Tutorials

Community-created video tutorials demonstrate practical implementations and workflow optimizations.

💬 Community Forums

Join discussions with other developers to share experiences and discover new techniques.

Implementation Best Practices

Getting Started

Implementing the concepts from this article requires a systematic approach. Start with small, controlled experiments in non-critical projects before applying these techniques to production code. This allows you to understand the nuances and adjust your workflow accordingly.

Integration Strategies

Successful integration of Antigravity IDE into your existing workflow depends on gradual adoption. Rather than attempting to revolutionize your entire development process overnight, focus on integrating one feature at a time. This measured approach ensures team buy-in and minimizes disruption to ongoing projects.

Monitoring and Optimization

Regular monitoring of your AI-assisted development metrics helps identify areas for improvement. Track metrics like code generation accuracy, time saved, and bug reduction rates. Use these insights to continuously refine your approach and maximize the benefits of AI-powered development.