Antigravity SDK Adds Local AI Model Support with Gemma 4 26B
Developers can now build agentic applications offline using optimized local models.
1Local model support added
The Antigravity SDK now supports local workflows with initial optimization for Gemma 4 26B A4B using Google AI Edge's LiteRT. This enables completely offline agentic assistance, leveraging local GPU and RAM resources. Developers can maintain privacy while accessing powerful AI capabilities without cloud dependency. The SDK efficiently utilizes local hardware to maximize performance potential.
2Hybrid architecture benefits
The update enables an Architect-Builder pattern combining cloud-scale planning with local execution. In demonstrations, Gemini 3.8 Flash acts as cloud architect while Gemma 4 26B handles on-device processing. This hybrid approach maintains data privacy while optimizing token utilization. The system successfully audited and patched vulnerable modules (auth.py, billing.py, database.py) entirely locally.
3Practical system utility example
The SDK built a live-updating terminal resource monitor autonomously from a single prompt. Using psutil and rich libraries, it created a Python script tracking CPU/memory usage and top processes. The agent generated requirements.txt, tested the code, and ensured functionality - all running locally on Gemma 4 26B. This demonstrates practical on-device AI application development capabilities.
4Getting started requirements
Developers need machines with 24GB VRAM or unified memory for optimal performance. Setup involves creating a Python virtual environment, installing the Antigravity SDK and LiteRT-LM, then downloading the Gemma 4 26B model. The process includes configuring model paths and workspace directories for local file operations. Sample code demonstrates basic chat functionality using local inference.
5OpenAI-compatible flexibility
The SDK offers plug-and-play support for OpenAI-compatible local servers like Ollama or vLLM via LocalOpenAIAgentConfig. This allows experimentation with different inference backends while maintaining consistent agent orchestration. Developers can switch local model providers without rewriting workflows or tools. The architecture preserves existing agent configurations across different execution environments.
6Performance optimization details
LiteRT provides specialized optimization for Gemma 4 26B on local hardware. The workflow minimizes memory overhead while maximizing GPU utilization. Local inference times vary based on hardware but typically take several minutes per task. The SDK manages resource allocation to prevent system overload during extended local model operations.
7Development workflow advantages
Local execution enables rapid iteration without cloud latency or costs. Developers can test agent behaviors with sensitive data securely on-premises. The SDK maintains all tool integrations and policy controls when switching to local models. Error debugging occurs in the same environment as final deployment, reducing configuration drift issues.
8Future ecosystem implications
This update bridges cloud-scale AI with edge computing capabilities. It enables new privacy-sensitive applications in healthcare, finance, and enterprise environments. The hybrid approach could redefine how developers balance performance with data governance requirements. As local models improve, more complex workflows may shift entirely on-device.