Google’s open source EmbeddingGemma, a multilingual embedding model, tops benchmarks and brings advanced RAG and semantic search directly to phones and laptops, enabling private, offline AI applications.
Google has unveiled EmbeddingGemma, an open source embedding model designed to run natively on laptops, desktops, and mobile devices. With 308 million parameters and support for over 100 languages, the model is based on the Gemma 3 architecture and extends Google’s push for efficient, device-ready AI.
EmbeddingGemma has secured the top spot on the Massive Text Embedding Benchmark (MTEB) multilingual v2 leaderboard for models under 500M parameters. It is engineered to deliver high-quality embeddings that enable Retrieval Augmented Generation (RAG) and semantic search directly on hardware, eliminating reliance on cloud servers.
In a blog post, Min Choi, Product Manager, and Sahil Dua, Lead Research Engineer at Google DeepMind, highlighted the model’s flexibility and integrations:
“EmbeddingGemma offers customisable output dimensions and will work with its open source Gemma 3n model. It integrates with tools like Ollama, llama.cpp, MLX, LiteRT, LMStudio, LangChain, LlamaIndex and Cloudflare.”
“Designed specifically for on-device AI, its highly efficient 308 million parameter design enables you to build applications using techniques such as Retrieval Augmented Generation (RAG) and semantic search that run directly on your hardware. It delivers private, high-quality embeddings that work anywhere, even without an internet connection.”
“For this RAG pipeline to be effective, the quality of the initial retrieval step is critical… This is where EmbeddingGemma’s performance shines.”
A key innovation, Matryoshka Representation Learning, allows multiple embedding sizes within a single model, giving developers a choice between full 768-dimension vectors or smaller, faster embeddings.
The launch comes amid surging enterprise demand for embedding models and intensifying competition in on-device AI, with rivals including Cohere, Mistral, OpenAI, Qodo, and Google’s earlier Embedding Gemini. By making advanced RAG pipelines feasible on phones and laptops, EmbeddingGemma democratises access to AI with greater privacy and offline functionality.
