EmbeddingGemma – Google’s open-source multilingual text embedding model

What is EmbeddingGemma？

EmbeddingGemma is Google’s open-source multilingual text embedding model, designed specifically for on-device AI and capable of deployment on laptops, smartphones, and other edge devices. The model has 308 million parameters, built on the Gemma 3 architecture, and supports over 100 languages. After quantization, it requires less than 200MB of memory and can generate embedding vectors on an EdgeTPU in just 15ms. It performs strongly on the Massive Text Embedding Benchmark (MTEB), with results comparable to the larger Qwen-Embedding-0.6B model. EmbeddingGemma produces high-quality embeddings, supports offline operation to protect user privacy, and can be paired with Gemma 3n in mobile RAG pipelines, semantic search, and other applications—making it a key foundation for the widespread adoption of on-device intelligence.

Key Features

High-quality text embeddings: Converts text into numerical vectors that represent semantic meaning in high-dimensional space, precisely capturing linguistic nuances and complex features.
Multilingual support: Covers over 100 languages, enabling cross-lingual applications such as multilingual semantic search and information retrieval.
Flexible output dimensions: Output dimensions can be customized from 768 down to 128, allowing developers to balance speed, storage, and quality according to their needs.
On-device deployment: With a memory footprint under 200MB after quantization, it can generate embeddings quickly on EdgeTPUs, enabling low-latency, offline operation that protects user privacy.
Tool compatibility: Works seamlessly with popular tools and frameworks including sentence-transformers, llama.cpp, MLX, Ollama, LiteRT, transformers.js, LMStudio, Weaviate, Cloudflare, LlamaIndex, and LangChain.
RAG support: Can be paired with Gemma 3n to build mobile-first RAG pipelines, enabling personalized, domain-specific, and offline-capable chatbots while enhancing semantic search and Q&A systems.

Technical Principles of EmbeddingGemma

Transformer-based architecture: Built on the improved Gemma 3 architecture, optimized for handling long text sequences with a 2K token context window, improving comprehension of extended documents.
Matryoshka Representation Learning (MRL): Enables embeddings of varying dimensions. Developers can choose the vector size to strike the best balance between performance and resource consumption.
Quantization-Aware Training (QAT): Reduces memory usage and increases inference speed while maintaining performance, allowing the model to run efficiently on resource-constrained devices.
Multilingual training: Trained on large-scale datasets spanning 100+ languages, supporting the generation of embeddings across diverse linguistic contexts.
End-to-end on-device processing: Generates document embeddings directly on hardware without requiring network access, ensuring data privacy and security. Uses the same tokenizer as Gemma 3n to further optimize memory usage in RAG applications.

Project Resources

Official site: https://developers.googleblog.com/zh-hans/embeddinggemma-mobile-first-embedding-model/
Hugging Face model library: https://huggingface.co/collections/google/embeddinggemma-68b9ae3a72a82f0562a80dc4

Application Scenarios

Retrieval-Augmented Generation (RAG): Works with Gemma 3n to build mobile-first RAG pipelines, enabling personalized, offline chatbots and improving semantic search and QA performance.
Multilingual applications: Supports cross-lingual retrieval and multilingual chatbots, breaking down language barriers.
On-device AI: Low memory footprint and fast inference make it suitable for offline intelligent applications on mobile devices while preserving user privacy.
Text classification and clustering: Helps categorize or group text data for data mining and analytics.
Semantic similarity computation: Useful for text similarity measurement and recommendation systems, enabling precise semantic-based recommendations.