Introducing EmbeddingGemma: The Best-in-Class Open Model for On-Device Embeddings

Google introduces EmbeddingGemma, a 308 million parameter open embedding model that delivers state-of-the-art performance for on-device AI applications. This compact model enables developers to build Retrieval Augmented Generation (RAG) systems and semantic search features that run entirely on user devices without internet connectivity.

Why EmbeddingGemma Matters for Developers

EmbeddingGemma solves a critical challenge in mobile AI development: creating high-quality text embeddings on resource-constrained devices. Traditional embedding models require server connections or consume excessive memory, limiting their practical applications. EmbeddingGemma changes this by delivering enterprise-grade performance in a package small enough for smartphones and laptops.

The model ranks highest among open multilingual text embedding models under 500M parameters on the Massive Text Embedding Benchmark (MTEB). Despite its compact size, it matches the performance of models nearly twice as large while running on less than 200MB of RAM with quantization.

Key Technical Features

Flexible Output Dimensions: EmbeddingGemma uses Matryoshka Representation Learning to provide multiple embedding sizes from one model. You can use the full 768-dimension vector for maximum quality or truncate to smaller dimensions (128, 256, or 512) for faster processing and reduced storage costs.

Optimized Performance: The model delivers sub-15ms embedding inference time for 256 input tokens on EdgeTPU, enabling real-time AI interactions. Quantization-Aware Training (QAT) reduces memory usage while preserving model quality.

Multilingual Support: Trained on 100+ languages with a 2K token context window, EmbeddingGemma handles diverse text processing needs across global applications.

Seamless Integration: The model shares the same tokenizer as Gemma 3n, reducing memory footprint in RAG applications and simplifying deployment pipelines.

Building Mobile-First RAG Applications

EmbeddingGemma enables sophisticated RAG pipelines that run entirely on-device. In a typical RAG system, you first generate embeddings of user queries and calculate similarity with document embeddings to retrieve relevant context. This context then feeds into a generative model like Gemma 3 to produce accurate, grounded responses.

The quality of initial retrieval determines overall system performance. Poor embeddings retrieve irrelevant documents, leading to inaccurate answers. EmbeddingGemma’s high-quality representations ensure reliable retrieval for accurate on-device applications.

Practical Use Cases

Personal Data Search: Search across files, texts, emails, and notifications simultaneously without internet connection, keeping sensitive data private.

Offline Chatbots: Build personalized, industry-specific chatbots using RAG with Gemma 3n that work without network access.

Query Classification: Classify user queries for relevant function calls to improve mobile agent understanding and response accuracy.

Custom Domain Applications: Fine-tune EmbeddingGemma for specific domains, tasks, or languages using the provided quickstart notebook.

Getting Started

EmbeddingGemma integrates with popular development tools from day one:

Model Access: Download weights from Hugging Face, Kaggle, and Vertex AI.

Development Tools: Use with sentence-transformers, llama.cpp, MLX, Ollama, LiteRT, transformers.js, LMStudio, Weaviate, Cloudflare, LlamaIndex, and LangChain.

Documentation: Access comprehensive guides for inference, fine-tuning, and RAG implementation through the Gemma Cookbook.

Quick Start: Try the interactive browser demo that runs entirely on-device using Transformers.js to visualize text embeddings in three-dimensional space.

Choosing Your Embedding Strategy

For on-device, offline applications requiring privacy and efficiency, EmbeddingGemma provides the optimal solution. For large-scale server-side applications where maximum performance matters most, consider the Gemini Embedding model via the Gemini API.

EmbeddingGemma represents a significant step forward in democratizing high-quality AI for mobile and edge computing, enabling developers to build sophisticated applications that respect user privacy while delivering professional-grade performance.