谷歌推出了其开放 AI 模型系列的最新版本 Gemma 3，旨在为 AI 可访问性树立新的标杆。
Gemma 3 建立在该公司Gemini 2.0模型的基础上，具有轻量、便携和适应性强等特点，使开发人员能够在各种设备上创建 AI 应用程序。
此次发布正值 Gemma 一周年之际，Gemma 的采用指标令人印象深刻。Gemma 模型的下载量已超过 1 亿次，并催生了 60,000 多个社区构建的变体。这个生态系统被称为“Gemmaverse”，标志着一个致力于实现 AI 民主化的蓬勃发展的社区。
谷歌解释道：“Gemma 系列开放模型是我们致力于让有用的人工智能技术普及的基础。”
Google has launched Gemma 3, the latest version of its family of open AI models that aim to set a new benchmark for AI accessibility.
Built upon the foundations of the company’s Gemini 2.0 models, Gemma 3 is engineered to be lightweight, portable, and adaptable—enabling developers to create AI applications across a wide range of devices.
This release comes hot on the heels of Gemma’s first birthday, an anniversary underscored by impressive adoption metrics. Gemma models have achieved more than 100 million downloads and spawned the creation of over 60,000 community-built variants. Dubbed the “Gemmaverse,” this ecosystem signals a thriving community aiming to democratise AI.
“The Gemma family of open models is foundational to our commitment to making useful AI technology accessible,” explained Google.
Gemma 3：特性和功能
Gemma 3 模型有多种尺寸可供选择 - 1B、4B、12B 和 27B 参数 - 允许开发人员选择适合其特定硬件和性能要求的模型。这些模型保证即使在适度的计算设置下也能更快地执行，而不会影响功能或准确性。
以下是 Gemma 3 的一些突出特点：
单加速器性能： Gemma 3 为单加速器模型树立了新标杆。在 LMArena 排行榜的初步人类偏好评估中，Gemma 3 的表现优于 Llama-405B、DeepSeek-V3 和 o3-mini 等竞争对手。
支持 140 种语言：为了迎合不同的受众，Gemma 3 具有针对 140 多种语言的预训练功能。开发人员可以创建以母语与用户联系的应用程序，从而扩大其项目的全球影响力。
复杂的文本和视觉分析：借助先进的文本、图像和短视频推理功能，开发人员可以实施 Gemma 3 来制作交互式智能应用程序，解决从内容分析到创意工作流程的一系列用例。
扩展的上下文窗口： Gemma 3 提供 128k 标记上下文窗口，可以分析和合成大型数据集，使其成为需要扩展内容理解的应用程序的理想选择。
函数调用实现工作流自动化：通过函数调用支持，开发人员可以利用结构化输出来自动化流程并轻松构建代理 AI 系统。
轻量级效率的量化模型： Gemma 3 引入了官方量化版本，显著减小了模型尺寸，同时保持了输出准确性——这对于针对移动或资源受限环境进行优化的开发人员来说是一大福利。
该模型的性能优势在 Chatbot Arena Elo Score 排行榜上得到了清晰的体现。尽管只需要一块 NVIDIA H100 GPU，Gemma 3 的旗舰 27B 版本仍跻身顶级聊天机器人之列，Elo 得分达到 1338。许多竞争对手需要多达 32 个 GPU 才能提供相当的性能。
Gemma 3: Features and capabilities
Gemma 3 models are available in various sizes – 1B, 4B, 12B, and 27B parameters – allowing developers to select a model tailored to their specific hardware and performance requirements. These models promise faster execution, even on modest computational setups, without compromising functionality or accuracy.
Here are some of the standout features of Gemma 3:
Single-accelerator performance: Gemma 3 sets a new benchmark for single-accelerator models. In preliminary human preference evaluations on the LMArena leaderboard, Gemma 3 outperformed rivals including Llama-405B, DeepSeek-V3, and o3-mini.
Multilingual support across 140 languages: Catering to diverse audiences, Gemma 3 comes with pretrained capabilities for over 140 languages. Developers can create applications that connect with users in their native tongues, expanding the global reach of their projects.
Sophisticated text and visual analysis: With advanced text, image, and short video reasoning capabilities, developers can implement Gemma 3 to craft interactive and intelligent applications—addressing an array of use cases from content analysis to creative workflows.
Expanded context window: Offering a 128k-token context window, Gemma 3 can analyse and synthesise large datasets, making it ideal for applications requiring extended content comprehension.
Function calling for workflow automation: With function calling support, developers can utilise structured outputs to automate processes and build agentic AI systems effortlessly.
Quantised models for lightweight efficiency: Gemma 3 introduces official quantised versions, significantly reducing model size while preserving output accuracy—a bonus for developers optimising for mobile or resource-constrained environments.
The model’s performance advantages are clearly illustrated in the Chatbot Arena Elo Score leaderboard. Despite requiring just a single NVIDIA H100 GPU, the flagship 27B version of Gemma 3 ranks among the top chatbots, achieving an Elo score of 1338. Many competitors demand up to 32 GPUs to deliver comparable performance.
Gemma 3 的优势之一在于它能够适应开发人员现有的工作流程。
- 多样化的工具兼容性： Gemma 3 支持流行的 AI 库和工具，包括 Hugging Face Transformers、JAX、PyTorch 和 Google AI Edge。为了优化部署，Vertex AI 或 Google Colab 等平台已准备好帮助开发人员以最少的麻烦开始使用。
- NVIDIA 优化：无论是使用 Jetson Nano 等入门级 GPU 还是 Blackwell 芯片等尖端硬件，Gemma 3 都能确保最大性能，并通过 NVIDIA API 目录进一步简化。
- 扩大硬件支持：除了 NVIDIA 之外，Gemma 3 还通过 ROCm 堆栈与 AMD GPU 集成，并支持使用 Gemma.cpp 进行 CPU 执行，以增加多功能性。
为了立即进行实验，用户可以通过 Hugging Face 和 Kaggle 等平台访问 Gemma 3 模型，或利用 Google AI Studio 进行浏览器内部署。
One of Gemma 3’s strengths lies in its adaptability within developers’ existing workflows.
- Diverse tooling compatibility: Gemma 3 supports popular AI libraries and tools, including Hugging Face Transformers, JAX, PyTorch, and Google AI Edge. For optimised deployment, platforms such as Vertex AI or Google Colab are ready to help developers get started with minimal hassle.
- NVIDIA optimisations: Whether using entry-level GPUs like Jetson Nano or cutting-edge hardware like Blackwell chips, Gemma 3 ensures maximum performance, further simplified through the NVIDIA API Catalog.
- Broadened hardware support: Beyond NVIDIA, Gemma 3 integrates with AMD GPUs via the ROCm stack and supports CPU execution with Gemma.cpp for added versatility.
For immediate experiments, users can access Gemma 3 models via platforms such as Hugging Face and Kaggle, or take advantage of the Google AI Studio for in-browser deployment.
推进负责任的人工智能
谷歌解释道：“我们相信开放模式需要仔细的风险评估，我们的方法在创新与安全之间取得平衡。”
Gemma 3 团队采取了严格的治理政策，通过微调和严格的基准测试使模型符合道德准则。鉴于该模型在 STEM 领域的能力增强，它接受了特定评估，以降低滥用风险，例如产生有害物质。
谷歌正在推动行业内的共同努力，为日益强大的模型创建相应的安全框架。
为了发挥其作用，谷歌推出了 ShieldGemma 2。4B 图像安全检查器利用 Gemma 3 的架构，并输出危险内容、露骨内容和暴力等类别的安全标签。在提供开箱即用的解决方案的同时，开发人员可以定制该工具以满足量身定制的安全要求。
“Gemmaverse” 不仅仅是一个技术生态系统，它还是一场社区驱动的运动。AI Singapore 的 SEA-LION v3、INSAIT 的 BgGPT 和 Nexa AI 的 OmniAudio 等项目证明了这个生态系统内协作的力量。
为了支持学术研究，谷歌还推出了 Gemma 3 学术计划。研究人员可以申请价值 10,000 美元的 Google Cloud 积分来加速他们的 AI 项目。申请现已开放，有效期为四周。
Gemma 3 凭借其可访问性、功能和广泛的兼容性，有充分的理由成为 AI 开发社区的基石。
Advancing responsible AI
“We believe open models require careful risk assessment, and our approach balances innovation with safety,” explains Google.
Gemma 3’s team adopted stringent governance policies, applying fine-tuning and robust benchmarking to align the model with ethical guidelines. Given the models enhanced capabilities in STEM fields, it underwent specific evaluations to mitigate risks of misuse, such as generating harmful substances.
Google is pushing for collective efforts within the industry to create proportionate safety frameworks for increasingly powerful models.
To play its part, Google is launching ShieldGemma 2. The 4B image safety checker leverages Gemma 3’s architecture and outputs safety labels across categories such as dangerous content, explicit material, and violence. While offering out-of-the-box solutions, developers can customise the tool to meet tailored safety requirements.
The “Gemmaverse” isn’t just a technical ecosystem, it’s a community-driven movement. Projects such as AI Singapore’s SEA-LION v3, INSAIT’s BgGPT, and Nexa AI’s OmniAudio are testament to the power of collaboration within this ecosystem.
To bolster academic research, Google has also introduced the Gemma 3 Academic Program. Researchers can apply for $10,000 worth of Google Cloud credits to accelerate their AI-centric projects. Applications open today and remain available for four weeks.
With its accessibility, capabilities, and widespread compatibility, Gemma 3 makes a strong case for becoming a cornerstone in the AI development community.