Welcome EmbeddingGemma, Google's new efficient embedding model

Today, Google releases EmbeddingGemma , a state-of-the-art multilingual embedding model perfect for on-device use cases. Designed for speed and efficiency, the model features a compact size of 308M parameters and a 2K context window , unlocking new possibilities for mobile RAG pipelines, agents, and more. EmbeddingGemma is trained to support over 100 languages and is the highest-ranking text-only multilingual embedding model under 500M on the Massive Text Embedding Benchmark (MTEB) at the time of writing.

Text embeddings have become the backbone of modern natural‑language applications, turning words, sentences, and documents into dense vectors that capture meaning, sentiment, and intent. These vectors enable fast similarity search, clustering, classification, and retrieval across massive corpora, powering everything from recommendation engines and semantic search to retrieval-augmented generation and code‑search tools. Embedding models that calculate these embeddings are widely used, with well over 200 million monthly downloads on Hugging Face .

Building on this foundation, Google DeepMind’s EmbeddingGemma arrives as the newest, most capable small multilingual embedding model yet. With just 308M parameters, a 2k‑token context window, and support for over 100 languages, EmbeddingGemma delivers state‑of‑the‑art performance on the Massive Multilingual Text Embedding Benchmark (MMTEB) while staying under 200 MB of RAM when quantized.

In this blogpost, we describe the EmbeddingGemma architecture and training, and show you how to use the model with various frameworks like Sentence Transformers, LangChain, LlamaIndex, Haystack, txtai, Transformers.js, Text Embedding Inference, and ONNX.

EmbeddingGemma builds on the Gemma3 transformers backbone, but modified to use bi-directional attention instead of causal (one-way) attention. This means that earlier tokens in the sequence can attend to later tokens, effectively turning the architecture from a decoder into an encoder. Encoder models can outperform LLMs, which are decoders, on embedding tasks like retrieval ( Weller et al., 2025 ). With this backbone, the model can process a sizable 2048 tokens at once, sufficient for typical retrieval inputs, especially given that larger inputs often result in information loss in the text embeddings.

EmbeddingGemma was benchmarked on the MMTEB (Multilingual, v2) and MTEB (English, v2) suites, which span a wide range of tasks, domains, and languages. Despite its modest 308M‑parameter size, the model consistently beats comparable baselines while keeping a very small memory footprint.

In Sentence Transformers, you can set a lower dimensionality using the truncate_dim parameter on either the SentenceTransformer initialization or when calling model.encode / model.encode_query / model.encode_document :

If you run the Docker container with the cuda-1.8.1 tag, it includes support for multiple GPU architectures: Turing, Ampere, Ada Lovelace, and Hopper. For a lighter image tailored to just your GPU, you can instead use a specific tag such as turing-1.8.1 , 1.8.1 and 86-1.8.1 (Ampere), 89-1.8.1 (Ada Lovelace), or hopper-1.8.1 .

The various design choices result in a very practical, open-source tool for computing high-quality multilingual embeddings on everyday devices.

Afterwards, we demonstrate how to finetune EmbeddingGemma on your domain for even stronger performance. In our example, we finetune EmbeddingGemma on the Medical Instruction and Retrieval Dataset (MIRIAD). The resulting model, sentence-transformers/embeddinggemma-300m-medical , achieves state-of-the-art performance on our task: retrieving passages of scientific medical papers in response to detailed medical questions. It even outperforms models twice as big on this task.

Beyond the new Gemma3-based encoder backbone, which produces token embeddings, a mean pooling layer converts these token embeddings into text embeddings. Lastly, two dense layers transform the text embeddings into their final form, a 768-dimensional vector.