Build AI Apps That Work Offline: Google's Antigravity SDK Now Runs Local Models

Google's Antigravity SDK now supports local AI models, including Gemma 4 26B, running entirely on your own hardware with no internet connection required. If you've been building or prototyping with AI tools and worrying about API costs, data privacy, or flaky connections, this update speaks directly to those frustrations.

What the Antigravity SDK Actually Is

Before digging into the new stuff, a quick grounding. The Antigravity SDK is Google's toolkit for building "agentic" apps, meaning apps where an AI doesn't just answer a question but takes a sequence of actions, makes decisions, and completes multi-step tasks on your behalf. Think of it as the scaffolding that lets you wire up an AI brain to actually do things inside a product, not just chat.

Until now, that brain had to phone home to a cloud server every time it thought. This update changes that.

What "Local Model Support" Means for a Designer

Running a model locally means the AI processing happens on your own computer, using your own GPU and memory, with no data sent anywhere. For a designer building a vibe-coded app, three things get immediately easier.

Cost goes closer to zero. Cloud AI APIs charge per token, which is roughly per word processed. A local model has no per-use fee. You can iterate, test, and break things without watching a billing meter tick up.

Sensitive work stays private. If you're prototyping something for a client with strict data rules, or exploring ideas you're not ready to share, nothing leaves your machine. The article's demo makes this concrete: in a hybrid workflow that audited three code files, 97.2% of all AI processing ran locally, and no source code was ever uploaded to a cloud server.

Offline actually works. Cafes, planes, client sites with locked-down networks: your app keeps functioning. That's a real design constraint that usually gets ignored until it bites you.

The Hybrid Pattern Is the Interesting Part

The most practically useful idea in this release isn't pure local AI. It's what Google calls the Architect-Builder pattern, and it's worth understanding because it maps nicely onto how designers already think about systems.

The idea: use a small, fast cloud model (here, Gemini 3.8 Flash) as a planner that figures out what needs to happen, then hand the actual heavy work to local models running on your GPU. The cloud model only sees high-level task descriptions, not your actual content. The local models do the detailed, private work.

In Google's own demo, the cloud planner spent just 95 tokens making decisions, while local models handled thousands of tokens of detailed work completely on-device. For a designer, the mental model is simple: the cloud model is the art director sketching a brief; the local model is the production team executing it, without ever seeing confidential client files.

This pattern lets you keep costs low, protect sensitive data, and still reach for bigger cloud models when a task genuinely needs their power.

What You Actually Need to Get Started

Be honest with yourself about the hardware requirement before getting excited. Google recommends a machine with more than 24GB of VRAM or unified memory (unified memory is the shared pool in Apple Silicon Macs). That's a high-end M3 Max, M4 Max, or a workstation-class GPU. A standard MacBook Pro or mid-range PC won't cut it for the Gemma 4 26B model specifically.

If you do have the hardware, the workflow is: install the SDK and a tool called LiteRT-LM (which handles running the model efficiently on your device), download the Gemma 4 26B model, and write a short script that points your agent at it. No cloud credentials needed for the local parts.

The SDK also supports other popular local inference tools, including Ollama and LM Studio, via a plug-and-play configuration. If you've already been experimenting with those tools, you can connect them to the Antigravity agent layer without rebuilding your setup.

A Practical Way to Try This Right Now

If you have compatible hardware, the lowest-friction starting point is the CLI resource monitor demo mentioned in the release. You give the agent a single plain-language prompt, and it builds a working terminal app that tracks CPU and memory usage, writes the necessary files, and tests the result, all locally. It's a contained, low-stakes task that lets you feel how local agentic AI behaves before you try to wire it into something you're actually shipping.

The example project for the hybrid security-audit demo is also publicly available and worth pulling apart, not because auditing code is your job, but because the Architect-Builder pattern it demonstrates is a structure you can reuse for almost any multi-step workflow in your own app.

What to Keep in Mind

The hardware bar is the real gating factor here. "Local AI" sounds democratising, and the direction genuinely is, but a 26-billion-parameter model needs serious memory to run well. If your machine doesn't meet the threshold, you're not blocked from building with the Antigravity SDK; you'd just be using cloud models, which is how it worked before.

It's also worth noting this is an early release. The README and GitHub issue tracker are the active support channels, which signals this is still finding its shape. Good time to experiment, not necessarily to bet a production feature on it just yet.

The trajectory here is clear, though. Local AI is getting more capable, more accessible, and now better supported in the tools designers are already reaching for. Getting familiar with the pattern now puts you ahead of the curve.