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Google DeepMind：Blog·· 2026-06-09精选AI 评分61

Google DeepMind 发布 Gemma 4 12B，无编码器统一架构多模态模型

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

文章要点
  • 1Google DeepMind 发布 Gemma 4 12B，视觉与音频输入不经过编码器，直接进入 LLM 主干。
  • 2官方称基准成绩接近自家 26B MoE 模型，总内存占用不到一半，16GB 内存笔记本可本地运行。
  • 3Gemma 4 12B 是官方首个支持原生音频输入的中等规模模型，并内置 MTP drafter 降低推理延迟。
  • 4Gemma 4 系列下载量已突破 150 million，新模型以 Apache 2.0 许可开放权重。
决策要点

多模态能力下探到 16GB 内存的消费级笔记本，开源许可让企业本地跑多模态智能体的硬件门槛进一步降低。

  • 影响：语音与图像等敏感数据的处理可评估在本地完成，减少数据外送环节；Apache 2.0 许可降低商用集成的授权顾虑；内存门槛下降让智能体试点的硬件预算更可控。
  • 参考：可考虑选语音质检或单据图像这类固定流程做本地试点，对比云端调用的时延与成本；值得先盘点现有设备中满足 16GB 内存的机型清单。
  • 依据：性能接近 26B、总内存占用不到一半、16GB 可本地运行等均为 Google DeepMind 官方口径，暂无独立验证；许可证与下载渠道以官方博客为准。
正文 · 原文

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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[[duration]] minutes

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.

Get started today

来源：Google DeepMind：Blog · deepmind.google

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