---
title: "Gemma 4 12B: The Developer Guide"
description: "Gemma 4 12B is a dense multimodal model with a unified, encoder-free architecture, native audio input, and a size intended for local execution on dedicated GPU laptops with 16GB VRAM or unified memory. Instead of separate vision and audio encoders, it uses a 35M-parameter vision embedder that projects raw 48x48 patches and a linear projection for raw 16 kHz audio sliced into 40ms frames; both feed a decoder-only transformer with shared weights. That design reduces multimodal latency and fragmented memory footprints, while allowing downstream adapters such as LoRA or full tuning to update the multimodal token loop in one pass. Capabilities include automatic speech recognition, agentic reasoning, diarization, video understanding, and coding, with examples covering a locally built image-processing app and five minutes of video processed at 1 FPS with audio; LiteRT-LM adds offline macOS apps, sandboxed Python execution, and an OpenAI-compatible local API server with stateless prefix caching."
---

# Gemma 4 12B: The Developer Guide

[Google](https://yomu.fyi/company/google) · André Susano Pinto, Andreas Steiner, Karolis Misiunas, Karsten Roth, Michael Tschannen, Omar Sanseviero · Jun 3, 2026

**Type:** Announcement

## Summary

Gemma 4 12B is a dense multimodal model with a unified, encoder-free architecture, native audio input, and a size intended for local execution on dedicated GPU laptops with 16GB VRAM or unified memory. Instead of separate vision and audio encoders, it uses a 35M-parameter vision embedder that projects raw 48x48 patches and a linear projection for raw 16 kHz audio sliced into 40ms frames; both feed a decoder-only transformer with shared weights. That design reduces multimodal latency and fragmented memory footprints, while allowing downstream adapters such as LoRA or full tuning to update the multimodal token loop in one pass. Capabilities include automatic speech recognition, agentic reasoning, diarization, video understanding, and coding, with examples covering a locally built image-processing app and five minutes of video processed at 1 FPS with audio; LiteRT-LM adds offline macOS apps, sandboxed Python execution, and an OpenAI-compatible local API server with stateless prefix caching.

## Context

Traditional multimodal models use separate frozen vision and audio encoders before passing inputs to the language model, increasing latency and creating fragmented memory footprints. The release targets local AI, including medium-sized models that can natively process audio and run on consumer-grade devices.

## Approach / What changed

Gemma 4 12B uses a unified decoder-only transformer, a 35M-parameter vision embedder for raw 48x48 patches, and a linear projection for raw 16 kHz audio frames. The release also provides LiteRT-LM integrations, offline macOS applications, sandboxed Python execution, and an OpenAI-compatible local API server with stateless prefix caching.

## Takeaways

- Gemma 4 12B is the first medium-sized model in the Gemma family described as capable of natively ingesting audio.
- Raw 16 kHz audio is sliced into 40ms frames containing 640 floats and projected linearly into the LLM input space.
- The litert-lm serve command runs the model as an OpenAI-compatible local API server and supports integrations such as Continue, Aider, OpenClaw, Hermes, and OpenCode.

**Tags:** [LiteRT-LM](https://yomu.fyi/topic/litert-lm), [Model Serving](https://yomu.fyi/topic/model-serving), [Multimodal](https://yomu.fyi/topic/multimodal)

- Source: [Google](https://developers.googleblog.com/gemma-4-12b-the-developer-guide)
- Source URL: https://developers.googleblog.com/gemma-4-12b-the-developer-guide
- Ingested by Yomu: 2026-09-09T14:29:52.192Z

[Read original post](https://developers.googleblog.com/gemma-4-12b-the-developer-guide)