Google AI Unveils EmbeddingGemma: High-Quality On-Device Embeddings

Google AI (DeepMind) introduced EmbeddingGemma, a compact yet powerful open embedding model engineered for on-device text understanding.

With just 308 million parameters, this multilingual model is a breakthrough in delivering best-in-class embeddings that work even offline, while preserving data privacy, speed, and multilingual versatility.

Key Features

Compact Yet Powerful Architecture: EmbeddingGemma features a unique dual-architecture design with approximately 100 million model parameters and 200 million embedding parameters, totaling 308 million parameters.

Built on the Gemma 3 backbone with bidirectional attention instead of causal attention, the model transforms from a decoder into an encoder optimized specifically for embedding tasks.

Built for On-Device, Offline Use: The model runs entirely on-device with sub-200 MB RAM usage when quantized, thanks to Quantization-Aware Training (QAT).

With inference speeds under 15ms for 256 tokens on an Edge TPU, EmbeddingGemma enables near real-time interactions without cloud calls.

Context-Rich Inputs and Multilingual Support: A generous 2,000-token context window enables processing of long text segments—ideal for complex queries, multi-sentence inputs, and RAG pipelines.

EmbeddingGemma supports over 100 languages, making it a universally applicable tool.

Matryoshka Representation Learning Integration: EmbeddingGemma implements Matryoshka Representation Learning (MRL), allowing flexible output dimensions from 768 down to 128, 256, or 512 dimensions without significant performance loss.

This enables developers to balance quality against speed and storage requirements based on specific application needs.

Privacy-First Architecture: By running fully offline on users’ devices, EmbeddingGemma ensures sensitive data stays local—key for chatbots, email or file search, personal assistants, and other privacy-conscious use cases.

Benchmark Performance and Rankings

Industry-Leading Results: EmbeddingGemma achieves the highest ranking on the Massive Text Embedding Benchmark (MTEB) among open multilingual text embedding models under 500 million parameters, establishing it as the gold standard for compact embedding models.

Competitive Performance Metrics: 

Superior multilingual performance across 100+ languages.

State-of-the-art accuracy for models in its size category.

Comparable results to models nearly twice its size.

Optimized for retrieval tasks including semantic search and classification.

Availability & Pricing

EmbeddingGemma is released as a completely free, open-source model under Google’s Gemma Terms of Use, which permits both personal and commercial usage.

The model follows the same licensing framework as other Gemma family models, allowing developers to use, modify, and distribute the model commercially.

Gemma (LLM series): Open-weight language models launched in February 2024 (2B and 7B parameters), followed by Gemma 2 and Gemma 3 in March 2025.

Gemma 3n: A variant optimized for on-device, offline multimodal use—supporting text, audio, and more on devices with just 2GB RAM.

EmbeddingGemma strategically fills the embedding niche lightweight yet powerful, tailored for offline, multilingual embedding tasks.

Key Applications and Use Cases

Privacy-First AI Solutions: Embedding Gemma addresses growing demand for privacy-focused AI applications by generating embeddings directly on user hardware without requiring internet connectivity, ensuring sensitive data never leaves the device.

Core Application Areas: 

• Retrieval-Augmented Generation (RAG) pipelines for mobile devices.
• Semantic search and similarity matching across user documents.
• Content classification and clustering for offline applications.
• Personalized recommendation systems without cloud dependency.
• Multilingual content analysis supporting global markets.

Technical Implementation and Deployment

Quantization-Aware Training: The model implements Quantization-Aware Training enabling reduced memory footprint while preserving model quality, making deployment feasible on resource-constrained devices including smartphones and IoT devices.

Integration & Developer Ecosystem

EmbeddingGemma seamlessly integrates with a broad range of developer tools and frameworks, including:

• Sentence-Transformers, transformers.js.
• LangChain, LlamaIndex for RAG workflows.
• Weaviate and other vector databases.
• MLX, Ollama, LiteRT, LMStudio, and more.

Developers can easily get started via Hugging Face, Kaggle, or Vertex AI, as model weights and docs are openly available.

A quickstart RAG example and fine-tuning guides are also provided in the Gemma Cookbook.

News Gist

Google AI has launched EmbeddingGemma, a 308M-parameter multilingual embedding model designed for on-device, offline AI applications.

Offering state-of-the-art performance, privacy-first architecture, and open licensing, it enables efficient local search, RAG pipelines, and intelligent mobile agents without internet dependency.

FAQs

Q1. What is EmbeddingGemma?

EmbeddingGemma is Google AI’s new 308M-parameter multilingual embedding model optimized for on-device, offline AI tasks like search, retrieval, and classification.

Q2. When was EmbeddingGemma announced?

EmbeddingGemma was officially announced on September 4, 2025 by Google AI.

Q3. What are its key features?

Compact design, multilingual support (100+ languages), Matryoshka embedding flexibility, sub-200MB RAM usage, 2,000-token context, and offline, privacy-first operations.

Q4. How fast is EmbeddingGemma?

It delivers under 15ms inference for 256 tokens on Edge TPU, making it highly efficient for real-time mobile and IoT use cases.

Q5. How much does it cost?

EmbeddingGemma is released as an open-weight model with a permissive license, allowing free use for research and responsible commercial applications.

Q6. Where can developers access it?

EmbeddingGemma is available via Hugging Face, Kaggle, Vertex AI, and Google’s official AI documentation, with integration support for LangChain, LlamaIndex, Weaviate, and more.