EmbeddingGemma: Google’s 308M On-Device Text Embedding with Top MTEB Scores
What EmbeddingGemma is
EmbeddingGemma is Google’s compact open text embedding model optimized for on-device AI. It is designed to balance efficiency and high retrieval performance so developers can run embeddings locally on mobile devices and offline environments.
Compact size and low latency
At just 308 million parameters, EmbeddingGemma is lightweight enough to operate on consumer devices and in offline scenarios. Despite its small footprint, it delivers competitive retrieval quality compared to much larger models. Inference latency is very low — sub-15 ms for 256 tokens on EdgeTPU — making it suitable for real-time and interactive applications.
Multilingual strength and MTEB results
EmbeddingGemma was trained across more than 100 languages and achieved the highest ranking on the Massive Text Embedding Benchmark (MTEB) among models under 500M parameters. Its cross-lingual retrieval and semantic search capabilities match or outperform embedding models nearly twice its size, which is notable for multilingual and global applications.
Architecture and embedding characteristics
The model uses a Gemma 3–based encoder backbone with mean pooling. Unlike Gemma 3 variants that include multimodal bidirectional attention for images, EmbeddingGemma employs a standard transformer encoder stack with full-sequence self-attention — the typical choice for text embedding models. It produces 768-dimensional vectors and supports sequences up to 2,048 tokens, which is useful for retrieval-augmented generation (RAG) and long-document search. Mean pooling provides fixed-length vectors regardless of input length.
Matryoshka Representation Learning (MRL)
EmbeddingGemma implements Matryoshka Representation Learning (MRL), allowing embeddings to be truncated from 768 dimensions down to 512, 256, or 128 with minimal quality loss. This makes it easy to tune the trade-off between storage/compute efficiency and retrieval precision without retraining the model.
Offline, on-device, and privacy-focused use
The model is explicitly designed for on-device, offline-first scenarios. It shares a tokenizer with Gemma 3n, enabling embeddings to directly power compact local RAG systems and improving privacy by avoiding cloud inference.
Ecosystem and integrations
EmbeddingGemma integrates with common ML and retrieval tooling, enabling developers to add it to existing pipelines quickly:
- Hugging Face (transformers, Sentence-Transformers, transformers.js)
- LangChain and LlamaIndex for RAG
- Weaviate and other vector databases
- ONNX Runtime for optimized cross-platform deployment
This ecosystem support helps embed EmbeddingGemma into production workflows with minimal friction.
How to implement it in practice
(1) Load and Embed
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("google/embeddinggemma-300m")
emb = model.encode(["example text to embed"])
(2) Adjust Embedding Size Use full 768 dims for maximum accuracy or truncate to 512/256/128 dims for lower memory or faster retrieval.
(3) Integrate into RAG Run similarity search locally (cosine similarity) and feed top results into Gemma 3n for generation. This enables a fully offline RAG pipeline.
Why it matters
EmbeddingGemma demonstrates that smaller embedding models can reach best-in-class retrieval performance while remaining small enough for on-device deployment. It brings together efficiency, multilingual retrieval accuracy, adjustable embedding sizes via MRL, and privacy advantages for offline pipelines. Open weights and wide ecosystem support make it accessible for developers building scalable, privacy-conscious retrieval systems.