EmbeddingGemma: a lightweight model to run on your device

Google DeepMind has just released a new high-performance embedding model specifically designed for on-device applications.

EmbeddingGemma is an open model (Gemma license) with 308 million parameters, capable of generating semantic representations of text quickly, efficiently, and privately, even without an internet connection.

First, what are embeddings?

In natural language processing (NLP), embeddings are numerical representations of text (such as words, sentences, or documents), expressed as vectors in a high-dimensional space.

The idea is to transform language into numbers that preserve semantics and similarity relationships. For example, in a well-trained embedding space:

• The words “cat” and “dog” are close to each other because they share context

• “Cat” and “airplane” are far apart because their meanings differ

This is essential because AI models do not understand text directly; they operate on numbers. Embeddings act as the “bridge” that translates the meaning of language into a mathematical format.

These representations can be used in various tasks:

• Semantic Search: Retrieve documents that are truly relevant to a query, even without exact matching words

• Text Classification: Categorize emails as spam or not spam, for example

• Clustering: Automatically group similar documents

• RAG (Retrieval-Augmented Generation): Provide relevant context for generative models to produce more accurate responses

The better the embedding, the more capable the system is of capturing language nuances, resulting in smarter and more reliable applications.

Why EmbeddingGemma is revolutionary

With just 308M parameters, EmbeddingGemma delivers performance comparable to models almost twice its size. To put it in perspective: it occupies less than 200MB of RAM when optimized, yet maintains state-of-the-art embedding quality.

Key highlights:

• Best-in-Class Performance: It is considered the top open-source multilingual text embedding model in its category, supporting over 100 languages

• Designed for Offline Use: Small and fast, it runs directly on your hardware—smartphone, laptop, or desktop. This means you can have high-level AI even without internet access

• Privacy-Focused: By processing data locally, EmbeddingGemma ensures that your most sensitive information, like personal files, stays secure on your device without needing to send it to the cloud

• Flexible and Compatible: With customizable output dimensions (from 128 to 768) and compatibility with popular tools like LangChain, Hugging Face, llama.cpp, and Ollama, it easily integrates into developers’ projects

How This Changes the Game

EmbeddingGemma opens up a world of possibilities for applications that require speed and privacy. Imagine RAG systems running in real time on your phone, allowing you to search through your personal files, emails, and notes instantly and without internet.

In a RAG pipeline, the process involves:

• Retrieving Relevant Context: EmbeddingGemma converts the user’s query into a numerical vector (embedding) and finds similar documents.

• Generating Responses: The retrieved passages are fed to a generative model, such as Gemma 3, to produce accurate and contextualized answers.

If embeddings are of low quality, retrieval fails and the model generates incorrect responses. EmbeddingGemma ensures high-fidelity semantic representation, even on mobile devices.

This technology also enables the creation of personalized chatbots and AI assistants that operate completely offline, providing fast and secure responses.

Getting Started