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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 EmbeddingGemma?

EmbeddingGemma is a flexible multilingual text embedding model boasting 308 million parameters, engineered to be both lightweight and highly effective, which enables it to function effortlessly on everyday devices such as smartphones, laptops, and tablets. Built on the Gemma 3 architecture, this model supports over 100 languages and accommodates up to 2,000 input tokens, leveraging Matryoshka Representation Learning (MRL) to offer customizable embedding sizes of 768, 512, 256, or 128 dimensions, thereby achieving a balance between speed, storage, and accuracy. Its capabilities are enhanced by GPU and EdgeTPU acceleration, allowing it to produce embeddings in just milliseconds—taking less than 15 ms for 256 tokens on EdgeTPU—while its quantization-aware training keeps memory usage under 200 MB without compromising on quality. These features make it exceptionally well-suited for real-time, on-device applications, including semantic search, retrieval-augmented generation (RAG), classification, clustering, and similarity detection. The model's versatility extends to personal file searches, mobile chatbot functionalities, and specialized applications, with a strong emphasis on user privacy and operational efficiency. Therefore, EmbeddingGemma is not only effective but also adapts well to various contexts, solidifying its position as a premier choice for diverse text processing tasks in real time.

Media

Media

Integrations Supported

Gemini
Gemini Enterprise
Gemini Nano
Gemini platform
Gemma
Google AI Edge
Google AI Edge Gallery
Google AI Studio
Google Cloud Platform
Hugging Face
JAX
Keras
Ollama
OpenCode
PyTorch

Integrations Supported

Gemma 3
Gemma 4
Mimasa 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

SaaS

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub
Webinars
On-Site Training

Training Options

Documentation Hub
Online Training
On-Site Training

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

Date Founded

1998

Company Location

United States

Company Website

ai.google.dev/gemma/docs/embeddinggemma

Categories and Features

AI Models

Not specified

Multimodal Models

Not specified

Small Language Models

Not specified

Categories and Features

Embedding Models

Not specified

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