EmbeddingGemma
by Google
Best-in-class 308M parameter multilingual text embedding model optimized for on-device use, achieving highest MTEB ranking under 500M parameters with sub-15ms inference on EdgeTPU
See https://ai.google.dev/gemma/docs/embeddinggemma
Features
Architecture & Performance:
- 308 million parameters based on Gemma 3 encoder backbone with mean pooling
- Produces 768-dimensional embeddings from sequences up to 2,048 tokens
- Highest ranking open multilingual embedding model under 500M on MTEB benchmark
- Comparable results to competitors nearly double its size
Efficiency & Speed:
- Operates within less than 200MB RAM with quantization
- Inference latency under 15ms for 256 tokens on EdgeTPU (under 22ms typical)
- Quantization-Aware Training preserves model quality while reducing memory footprint
- Optimized for phones, laptops, tablets, and edge devices
Flexible Dimensions:
- Matryoshka Representation Learning (MRL) enables customizable output from 768 to 128 dimensions
- Embeddings can be truncated to 512, 256, or 128 dimensions with minimal quality loss
- Single model supports multiple dimension configurations for speed/storage tradeoffs
Multilingual Capabilities:
- Trained on 100+ languages
- Approximately 320 billion token training corpus
- Includes web documents, code, technical documentation, and synthetic task-specific data
Privacy & Offline:
- Works completely offline without internet connectivity
- On-device processing keeps sensitive data private
- Ideal for personal file search and private chatbots
Superpowers
EmbeddingGemma stands out as the premier on-device embedding model for mobile-first and privacy-conscious applications, making it ideal for:
- Mobile AI developers building offline-capable apps with semantic search and RAG
- Privacy-focused applications requiring on-device text understanding without cloud dependencies
- Edge computing projects needing efficient embeddings on resource-constrained devices
- Multilingual applications supporting 100+ languages with consistent quality
- Developers fine-tuning for domain-specific tasks (medical, legal, technical documentation)
Real-world applications:
- Offline semantic search across personal files, emails, and communications
- Retrieval-Augmented Generation (RAG) pipelines paired with Gemma 3n on mobile
- User query classification for mobile AI agents
- Document clustering and similarity search on edge devices
- Privacy-preserving semantic analysis of sensitive documents
Key advantages:
- Best MTEB performance in its parameter class (under 500M)
- Sub-15ms latency enables real-time responsive interactions
- Dimension flexibility allows optimization for specific use cases
- Open weights with commercial use licensing
- Ecosystem integration: transformers.js, llama.cpp, Ollama, LangChain, LlamaIndex
Pricing
- Open weights: Free under responsible commercial use license
- Self-hosted: Deploy on-device or edge infrastructure at no additional cost
- Vertex AI: Available through Google Cloud (pricing varies by deployment)
- Fine-tuning: Fully customizable for domain-specific applications
Cost efficiency: On-device deployment eliminates API costs and enables unlimited inference without per-query charges.
Getting Started
Available on:
- Hugging Face:
google/embeddinggemma-300m - Kaggle model repository
- Google Vertex AI
Development Support:
- Inference guides using Sentence Transformers
- Fine-tuning documentation with Sentence Transformers
- Quickstart RAG notebook for deployment reference
- Integration with popular frameworks (LangChain, LlamaIndex, Ollama)
Typical workflow:
- Load model via Hugging Face or Vertex AI
- Configure embedding dimensions (128-768) based on use case
- Generate embeddings for text corpus
- Build RAG pipeline or semantic search application
- Optional: Fine-tune on domain-specific data
Use Cases
Information Retrieval:
- Semantic search across documents and communications
- Personal knowledge base search
- Code search and documentation retrieval
RAG Applications:
- Offline chatbots with context retrieval
- Mobile AI assistants with grounded responses
- Document Q&A systems on edge devices
Classification & Clustering:
- Query intent classification
- Document categorization
- Content similarity analysis
Domain-Specific:
- Medical literature search
- Legal document analysis
- Technical documentation retrieval