- Gemma 3 model overview - Google AI for Developers
Gemma is a family of generative artificial intelligence (AI) models and you can use them in a wide variety of generation tasks, including question answering, summarization, and reasoning
- Gemma (language model) - Wikipedia
Gemma is a series of open-source large language models developed by Google DeepMind It is based on similar technologies as Gemini The first version was released in February 2024, followed by Gemma 2 in June 2024 and Gemma 3 in March 2025
- Gemma 3n Powers Real-World Impact at the Edge
The Gemma 3n Impact Challenge reveals the model's profound potential for on-device, multimodal AI solutions addressing real-world problems
- Gemma 3 AI | The best AI multimodal model on a single GPU
Unlike other models that require expensive setups, Gemma 3 delivers top-tier performance on a single GPU Whether you're a developer testing ideas or a business deploying solutions, we make advanced AI accessible without the need for massive computing resources
- Welcome Gemma 3: Googles all new multimodal, multilingual, long . . .
Today Google releases Gemma 3, a new iteration of their Gemma family of models The models range from 1B to 27B parameters, have a context window up to 128k tokens, can accept images and text, and support 140+ languages Try out Gemma 3 now 👉🏻 Gemma 3 Space All the models are on the Hub and tightly integrated with the Hugging Face ecosystem
- Introducing Gemma 3: The Developer Guide- Google Developers Blog
We are excited to introduce Gemma 3, our most capable and advanced version of the Gemma open-model family, building upon the success of previous Gemma releases
- Gemma 3n - Google DeepMind
Gemma 3n was created in close collaboration with leading mobile hardware manufacturers It shares architecture with the next generation of Gemini Nano to empower a new wave of intelligent, on-device applications Engineered for speed and quality, with a significantly reduced memory footprint
- Gemma 3 | Powerful Lightweight AI Model | Try Free
Integrate Gemma 3 seamlessly with popular ML frameworks including PyTorch, TensorFlow, and JAX Enjoy optimized memory usage and computational efficiency, allowing you to run more complex workloads on existing hardware
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