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Google DeepMind·· 2026-06-09精选AI 评分76

Google DeepMind 发布无编码器统一多模态模型 Gemma 4 12B

Introducing Gemma 4 12B: a unified, encoder-free multimodal model

AI 导读

Google DeepMind 发布面向笔记本的 Gemma 4 12B，采用无编码器统一架构，让视觉和音频直接进入 LLM 主干。它是该系列首个支持原生音频输入的中等尺寸模型，标准基准表现接近 26B MoE，总内存不到后者一半，16GB VRAM 或统一内存即可本地运行，以 Apache 2.0 许可发布并配备 MTP drafter。

推荐理由

它位于端侧友好的E4B与更先进的26B MoE之间，用无编码器设计把视觉和音频直接交给语言模型，并以不到一半内存接近更大模型的基准表现。

正文

Jun 03, 2026

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Gemma 4 12B is designed to bring high-performance multimodal intelligence directly to your laptop, combining mobile-first efficiency with advanced reasoning.

Olivier Lacombe

Director of Product Management, Google Deepmind

Gus Martins

Product Manager, Google DeepMind

Gemma 4 12B Unified Transformer

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This content is generated by Google AI. Generative AI is experimental

Today, we are introducing Gemma 4 12B, our latest model designed to bring agentic multimodal intelligence directly to laptops. Bridging the gap between our edge-friendly E4B and our more advanced 26B Mixture of Experts (MoE), Gemma 4 12B packages powerful capabilities inside a reduced memory footprint. It is also our first mid-sized model to feature native audio inputs.

Thanks to the developer community, Gemma 4 models have now crossed 150 million downloads. You’ve built everything from wearable robotic arms for physical assistance to enterprise-grade AI security. We're excited to see what you build with this latest addition.

Here’s an overview of what makes Gemma 4 12B unique:

  • Novel unified architecture: No multimodal encoders. The vision and audio inputs flow directly into the LLM backbone.
  • Advanced reasoning: Benchmark performance nearing our 26B model, unlocking powerful multi-step reasoning and agentic workflows.
  • Laptop ready: Small enough to run locally with just 16GB of VRAM or unified memory.
  • Open and accessible: Released under an Apache 2.0 license with support across the developer ecosystem.
  • Drafter-ready: Gemma 4 12B comes equipped with Multi-Token Prediction (MTP) drafters to reduce latency.

Together, these features bring advanced multimodal capabilities to everyday hardware without sacrificing speed or reasoning. Let's now take a closer look at how Gemma 4 12B achieves this.

Run state-of-the-art agents locally

Gemma 4 12B delivers performance nearing our larger 26B MoE model on standard benchmarks, but at less than half the total memory footprint. Small enough to run locally on consumer laptops with 16GB of RAM, it unlocks powerful multimodal and agentic experiences right on your machine.

Gemma 4 12B Benchmark

Experience a uniquely efficient, unified architecture

What makes Gemma 4 12B stand out is its streamlined approach to processing visual and audio inputs. Traditional multimodal models typically rely on separate encoders to translate images and audio before passing those representations to the language model. Because these split encoders add latency and increase memory usage, we trained Gemma 4 12B with an encoder-free architecture to integrate audio and vision input directly.

Here is how Gemma 4 12B processes multimodal inputs natively:

  • Vision: We replaced Gemma 4’s vision encoder with a lightweight embedding module consisting of a single matrix multiplication, positional embedding and normalizations. This allows the LLM backbone to take over visual processing.
  • Audio: We simplified audio processing even further. We removed the audio encoder entirely and projected the raw audio signal into the same dimensional space as text tokens.

For developers who want a breakdown, head over to our companion Gemma 4 12B Developer Guide.

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来源：Google DeepMind · deepmind.google

#模型发布#多模态#端侧#Agent#开源生态#DeepMind