# Gemma 4 12B: The Developer Guide

> Source: <https://developers.googleblog.com/gemma-4-12b-the-developer-guide/>
> Published: 2026-06-03 16:41:15.374738+00:00

Following the announcement in our [launch blog](https://blog.google/innovation-and-ai/technology/developers-tools/introducing-gemma-4-12B/), we are releasing **Gemma 4 12B**, a dense multimodal model with a **unified, encoder-free architecture**.

Gemma 4 12B introduces several milestones for local AI:

Traditional multimodal models rely on frozen, separate vision encoders (e.g., Gemma 4 uses a 150M parameter vision model for edge sizes and 550M for medium-sized models) and audio encoders (300M parameters for Gemma 4 E2B and E4B). Processing multimodal inputs with multiple separate encoders before feeding them to the LLM leads to increased latency and fragmented memory footprints.

Gemma 4 12B solves these issues by utilizing a single decoder-only transformer containing the same advanced decoder structure as the Gemma 4 31B Dense model.

For a more in-depth overview of how this encoder-free architecture works, check out [A Visual Guide to Gemma 4 12B](https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-gemma-4-12b).

Gemma 4 12B achieves outstanding performance, with capabilities such as automatic speech recognition, agentic reasoning, diarization, video understanding, coding, and more.

See below examples for a demonstration of the model's agentic and multimodal capabilities:

Thanks to its agentic and multimodal understanding capabilities, Gemma 4 12B can be easily used with existing agent harnesses like OpenCode. In this example, we served it locally using llama.cpp using the [gemma-skills](https://github.com/google-gemma/gemma-skills) to code a Gradio app that helped the user process images. This app was powered by the same Gemma 4 12B model that built it!

We used Gemma 4 12B to analyse a part from the [Google IO Keynote moment](https://www.youtube.com/watch?v=wYSncx9zLIU&start=932&end=1245) from May 19, specifically the 5 minutes between 00:15:32 and 00:20:45. To do that, we extracted all the frames of that segment (at 1 FPS), plus the prompt and the audio from the video:

[Link to Youtube Video](https://www.youtube.com/watch?v=wYSncx9zLIU)
(visible only when JS is disabled)

Prompt:

`"what happens when the man takes a selfie?"`

When the man takes a "selfie" or is shown holding a smartphone in front of his face in these demonstration videos, it is a clever way to visually represent how the AI models can take existing media—like a personal photo or a video clip—and "reimagine" it. In these specific clips, the model is taking a selfie and using it as a base to generate various scenarios (like a person in a space station, or walking through a forest).

Essentially, the man is not actually taking a selfie; rather, he is **acting out a visual metaphor for the AI's capability to take one specific input (a "selfie") and generate a whole world of new content based on it.** This is part of the "Swap" and "Build worlds" demonstrations of the Gemini Omni model, showing its ability to perform complex, multi-modal reasoning and creative generation.

In tandem with the Gemma 4 12B launch, we are officially introducing powerful on-device developer integrations powered by LiteRT-LM, bringing zero-latency local AI execution natively to standard desktop environments:

**1.Native MacOS Apps**: The mobile [ Google AI Edge Gallery](https://developers.google.com/edge/gallery) is officially expanding to desktop platforms, running Gemma 4 12B offline, natively on Apple Silicon GPUs. It comes with a secure sandboxed Python execution loop to write, execute, and plot scientific charts inside the chat bubble. In parallel, the

**2. Drop-in Local API Servers (litert-lm serve):** Run Gemma 4 12B as a local, OpenAI-compatible API server using the new litert-lm serve **CLI command****.** Seamlessly connect standard integrations (e.g., Continue, Aider, OpenClaw, Hermes or OpenCode), leveraging stateless prefix caching in memory to match context history and instantly bypass prefill latency.

```
litert-lm import --from-huggingface-repo=litert-community/gemma-4-12B-it-litert-lm  gemma-4-12B-it.litertlm gemma4-12b

# Start the OpenAI-compatible server
litert-lm serve
```

Find a deep dive about it on the Google AI Edge Gallery [blog](https://developers.googleblog.com/bringing-gemma-4-12b-to-your-laptop-unlocking-local-agentic-workflows-with-google-ai-edge).

Ready to build local multimodal agents with the first encoder-free architecture of the Gemma family? Here is how you can jump in today