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What is Gemma 3n?

Meet Gemma 3n, our state-of-the-art open multimodal model engineered for exceptional performance and efficiency on devices. Emphasizing responsive and low-footprint local inference, Gemma 3n sets the stage for a new era of intelligent applications that can be deployed while on the go. It possesses the ability to interpret and react to a combination of images and text, with upcoming plans to add video and audio capabilities shortly. This allows developers to build smart, interactive functionalities that uphold user privacy and operate smoothly without relying on an internet connection. The model features a mobile-centric design that significantly reduces memory consumption. Jointly developed by Google's mobile hardware teams and industry specialists, it maintains a 4B active memory footprint while providing the option to create submodels for enhanced quality and reduced latency. Furthermore, Gemma 3n is our first open model constructed on this groundbreaking shared architecture, allowing developers to begin experimenting with this sophisticated technology today in its initial preview. As the landscape of technology continues to evolve, we foresee an array of innovative applications emerging from this powerful framework, further expanding its potential in various domains. The future looks promising as more features and enhancements are anticipated to enrich the user experience.

What is Gemma 2?

The Gemma family is composed of advanced and lightweight models that are built upon the same groundbreaking research and technology as the Gemini line. These state-of-the-art models come with powerful security features that foster responsible and trustworthy AI usage, a result of meticulously selected data sets and comprehensive refinements. Remarkably, the Gemma models perform exceptionally well in their varied sizes—2B, 7B, 9B, and 27B—frequently surpassing the capabilities of some larger open models. With the launch of Keras 3.0, users benefit from seamless integration with JAX, TensorFlow, and PyTorch, allowing for adaptable framework choices tailored to specific tasks. Optimized for peak performance and exceptional efficiency, Gemma 2 in particular is designed for swift inference on a wide range of hardware platforms. Moreover, the Gemma family encompasses a variety of models tailored to meet different use cases, ensuring effective adaptation to user needs. These lightweight language models are equipped with a decoder and have undergone training on a broad spectrum of textual data, programming code, and mathematical concepts, which significantly boosts their versatility and utility across numerous applications. This diverse approach not only enhances their performance but also positions them as a valuable resource for developers and researchers alike.

Media

Media

Integrations Supported

Gemini platform
Gemma
Google AI Studio
Google Cloud Platform
Hugging Face
Ollama
PyTorch
JAX
OpenCode

Integrations Supported

Gemini platform
Gemma
Google AI Studio
Google Cloud Platform
Hugging Face
Ollama
PyTorch
C#
Database Mart
Google Colab
Molmo
MongoDB
Nebius Token Factory
Ruby
Rust
SQL
Scala
VESSL AI

API Availability

API Availability

Has API

Pricing Information

Pricing not provided
Free Trial Offered?

Pricing Information

Pricing not provided

Supported Platforms

Windows
Mac
On-Prem
Linux

Supported Platforms

Windows
Mac
On-Prem
Linux

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Webinars
On-Site Training

Training Options

Documentation Hub

Company Facts

Organization Name

Google DeepMind

Date Founded

2010

Company Location

United Kingdom

Company Website

deepmind.google/models/gemma/gemma-3n/

Company Facts

Organization Name

Google

Company Location

United States

Company Website

ai.google.dev/gemma

Categories and Features

AI Models

Not specified

Multimodal Models

Not specified

Small Language Models

Not specified

Categories and Features

AI Models

Not specified

Large Language Models

Not specified

Open-Weight AI Models

Not specified

Small Language Models

Not specified

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