Google adds local model support to the Antigravity SDK with Gemma 4 on LiteRT
Google has given the Antigravity SDK the ability to drive agents with models running on the developer’s own machine, so a full agentic loop can execute without an API call. The featured configuration runs Gemma 4 26B A4B through LiteRT, and Sachin Kotwani and Taylor Mullen recommend a machine with more than 24 GB of VRAM or unified memory. Google names cost, privacy and offline resiliency as the reasons to keep the loop local.
Two configuration classes carry the feature. LiteRTAgentConfig points the agent at a local LiteRT model file, while LocalOpenAIAgentConfig connects to any OpenAI-compatible server, with Ollama, LM Studio and vLLM named as examples. The SDK repository installs both with pip install google-antigravity litert-lm, states that the setup works best with gemma-4-26B-A4B-it-gpu.litertlm, and suggests a 64K context size. That checkpoint is a mixture-of-experts model, where only part of the weights is active per token, with 26 billion parameters in total and roughly 4 billion active, and it ships at about 15.8 GB as a quantisation-aware trained int4 build with float activations.
A hybrid arrangement Google calls the Architect-Builder pattern splits the work between cloud and device. Gemini 3.8 Flash plans in the cloud and hands the steps to a local swarm of Gemma 4 26B instances, which Google demonstrates on a task that audits and patches three vulnerable Python modules. The run consumed 95 cloud tokens against 3,322 local ones, putting 97.2% of the tokens on the machine, with only filenames leaving it.
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Throughput depends heavily on the hardware. The LiteRT community model card reports 4,487 prefill and 86 decode tokens per second on an Nvidia RTX 4090 under Linux, 1,430 prefill and 77 decode on an M4 Max, and 1,676 prefill and 76 decode on an RTX 5080 under Windows, with the macOS run taking about 16.2 GB of GPU memory. The model is published under Apache 2.0, and a second example in the announcement has the local agent write a terminal dashboard with the psutil and rich libraries that tracks CPU load, memory use and the five most memory-hungry processes.