2026年6月3日
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3 分钟阅读
Gemma 4 12B 旨在将高性能多模态智能直接带到你的笔记本电脑上，融合了移动优先的效率与高级推理能力。
Olivier Lacombe
Google Deepmind 产品管理总监
Gus Martins
Google DeepMind 产品经理
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此内容由 Google AI 生成。生成式 AI 为实验性技术。
今天，我们推出 Gemma 4 12B，这是我们最新的模型，旨在将智能体多模态智能直接带到笔记本电脑上。Gemma 4 12B 填补了边缘友好的 E4B 与更先进的 26B 混合专家模型之间的空白，在更小的内存占用内封装了强大的能力。这也是我们首款支持原生音频输入的中型模型。
得益于开发者社区，Gemma 4 模型的下载量现已突破 1.5 亿次。你们用它构建了从可穿戴机械臂（用于物理辅助）到企业级 AI 安全的各种应用。我们很期待看到你们用这个最新版本能创造出什么。
以下是 Gemma 4 12B 独特之处的概述：
- 新颖的统一架构： 无需多模态编码器。视觉和音频输入直接流入 LLM 主干网络。
- 高级推理： 基准测试性能接近我们的 26B 模型，解锁强大的多步推理和智能体工作流。
- 笔记本电脑就绪： 体积小巧，仅需 16GB VRAM 或统一内存即可本地运行。
- 开放且可访问： 在 Apache 2.0 许可下发布，并得到整个开发者生态系统的支持。
- 草稿模型就绪： Gemma 4 12B 配备多令牌预测草稿器，可降低延迟。
这些特性共同将先进的多模态能力带到了日常硬件上，同时不牺牲速度或推理能力。现在，让我们更深入地了解 Gemma 4 12B 是如何实现这一点的。
在本地运行最先进的智能体
Gemma 4 12B 在标准基准测试中性能接近我们更大的 26B MoE 模型，但总内存占用不到其一半。它体积小巧，可在配备 16GB RAM 的消费级笔记本电脑上本地运行，从而在你的机器上解锁强大的多模态和智能体体验。
体验独特高效的统一架构
Gemma 4 12B 的突出之处在于其处理视觉和音频输入的简化方法。传统的多模态模型通常依赖独立的编码器来翻译图像和音频，然后将这些表示传递给语言模型。由于这些分离的编码器会增加延迟和内存使用，我们采用无编码器架构训练了 Gemma 4 12B，以直接集成音频和视觉输入。
以下是 Gemma 4 12B 原生处理多模态输入的方式：
- 视觉： 我们将 Gemma 4 的视觉编码器替换为一个轻量级嵌入模块，该模块仅包含一次矩阵乘法、位置嵌入和归一化。这使得 LLM 主干网络能够接管视觉处理。
- 音频： 我们进一步简化了音频处理。我们完全移除了音频编码器，并将原始音频信号投影到与文本令牌相同的维度空间。
对于想要详细了解的开发者，请访问我们的配套 Gemma 4 12B 开发者指南。
观看原生音频处理的实际应用：观看 Gemma 4 12B 使用 Google AI Edge Eloquent 应用，完全离线转录、格式化并翻译语音输入。
立即开始使用
- 亲自尝试： 在 LM Studio、Ollama、Google AI Edge Gallery 应用、Google AI Edge Eloquent 应用和 LiteRT-LM CLI 中，只需点击几下即可进行实验。
- 下载权重： 直接从 Hugging Face 和 Kaggle 下载预训练和指令微调检查点。
- 集成与学习： 查看开发者文档和快速入门笔记本。
- 使用你喜爱的开发工具： 使用 Hugging Face Transformers、llama.cpp、MLX、SGLang 和 vLLM 实现本地推理管道，或使用 Unsloth 高效微调。
- 使用 Gemma Skills 解锁智能体开发： 为支持智能体利用最新的 Gemma 进展进行构建，我们发布了官方的 Skills 仓库。这是一个专门为让智能体能够使用 Gemma 模型进行构建而设计的技能库。
- 按需部署： 使用 Google Cloud 在生产环境中启动端点。通过 Gemini Enterprise Agent Platform Model Garden、Cloud Run 和 GKE 按需部署。
Jun 03, 2026
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3 min read
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
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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 fromwearable robotic arms for physical assistance toenterprise-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.
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.
See native audio processing in action: Watch Gemma 4 12B transcribe, format, and translate voice inputs entirely offline using the Google AI Edge Eloquent app.
Get started today
- Try it yourself: Experiment with a couple of clicks in LM Studio, Ollama, Google AI Edge Gallery App, the Google AI Edge Eloquent app and the LiteRT-LM CLI
- Download the weights: Download the pre-trained and instruction-tuned checkpoints directly from Hugging Face and Kaggle.
- Integrate & learn: Review the developer documentation and the quick start notebook.
- Use your favorite development tools: Implement local inference pipelines with Hugging Face Transformers, llama.cpp, MLX, SGLang, and vLLM, or fine-tune with efficiency using Unsloth.
- Unlock Agentic Development with Gemma Skills: To support agents to build with the latest Gemma advancements, we are releasing our official Skills Repository. This is a library of skills designed specifically to enable agents to build with Gemma models.
- Deploy your way: Spin up endpoints in production using Google Cloud. Deploy your way through Gemini Enterprise Agent Platform Model Garden, Cloud Run and GKE.
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