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: 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.
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.
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.
(Image credit: Google)
See also: Alibaba Qwen QwQ-32B: Scaled reinforcement learning showcase
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