Embedding Gemma: AI Compact Multilingual Embedding Model

Embedding Gemma

What is Embedding Gemma?

Embedding Gemma is a compact, open-source text embedding model developed by Google, based on the Gemma 3 architecture. With around 308 million parameters, it's designed to run on devices with limited compute resources—such as phones, tablets or laptops—and can function without an internet connection .

Its main job is to convert text (words, sentences, documents) into numerical vectors—sometimes called embeddings. These allow computers to compare meaning, group similar text segments together, and support tasks like search, classification or clustering.

Notably, Embedding Gemma can handle text in more than 100 languages, offers adjustable embedding sizes, and supports long context windows (up to 2,048 tokens) . It is small, efficient and privacy-friendly, making it practical for applications that need fast, local embedding generation.

Embedding Gemma Table Overview

Here’s a quick reference table summarizing Embedding Gemma’s main specifications:

Attribute	Details
Model Size	~308 million parameters
Language Support	Multilingual—trained on 100+ languages
Context Window	Up to 2,048 input tokens
Embedding Dimensions	Default 768 dimensions; can be truncated to 512, 256 or 128
RAM Requirement	Less than 200 MB when quantized
Inference Speed	Under 22 ms on EdgeTPU (for embeddings)
Architecture	Gemma 3 backbone, encoder-style (bi-directional attention instead of decoder-only)
Privacy & Offline Use	Fully on-device embedding generation; no network needed

Key Features

Compact yet potent With just over 300 million parameters, Embedding Gemma sits comfortably in the “small but capable” category. It delivers solid embedding quality while allowing deployment on hardware that can’t handle larger models .
Cross-language support It was trained on text from over a hundred languages, empowering multilingual embedding tasks such as cross-lingual search or multilingual clustering .
Flexible embedding sizes via MRL Matryoshka Representation Learning (MRL) enables users to shrink the embedding vector from the full size of 768 down to 512, 256, or 128, offering a trade-off between precision and performance or storage .
Long input support The model can process inputs up to 2,048 tokens, making it suitable for embedding summaries, longer passages or relatively long documents .
Efficiency in both memory and compute When quantized, Embedding Gemma can run in under 200 MB of RAM. Generating embeddings on EdgeTPU happens in under 20–22 milliseconds, offering fast performance.
Privacy-friendly and offline-ready Because it can run on-device, sensitive text never needs to leave your device—ideal for secure, private document processing.
Solid performance in benchmarks Despite its size, Embedding Gemma has ranked highest among multilingual, text-only embedding models under 500 million parameters in the Massive Text Embedding Benchmark (MTEB) at the time of launch.
Encoder-style architecture The model uses bidirectional attention (turning Gemma 3 into an encoder), improving embedding quality compared to decoder-only architectures on retrieval tasks .

How to Use Embedding Gemma

Setup & Installation

Get model access
- Create a Hugging Face account and accept the Embedding Gemma license.
- Generate a valid Hugging Face token to access the model .

Install necessary libraries

pip install -U sentence-transformers git+https://github.com/huggingface/transformers@v4.56.0-Embedding-Gemma-preview

Initialize the model Here’s Python code using Sentence Transformers:

import torch
from sentence_transformers import SentenceTransformer

device = "cuda" if torch.cuda.is_available() else "cpu"
model = SentenceTransformer("google/embeddinggemma-300M").to(device)
print(f"Device: {model.device}")
print("Parameter count:", sum(p.numel() for p in model.parameters()))

Generate embeddings
```
sentences = ["First sentence.", "Second sentence."]
embeddings = model.encode(sentences)
```
You now have numerical vectors representing the meaning of each input sentence .
Adjust embedding size (MRL) To trade off between speed and storage, truncate the embeddings to a smaller dimension (e.g., 256) and re-normalize. The documentation includes examples for this.

Fine-Tuning on Your Data

Use provided notebooks (e.g., in the Hugging Face blog) to fine-tune on datasets such as domain-specific text or harder retrieval tasks.
For example, one team fine-tuned Embedding Gemma on a medical retrieval dataset (MIRIAD), producing a model that outperformed larger models on that domain .

Integration with Tools & Frameworks

Embedding Gemma is supported across many platforms:

LangChain, LlamaIndex, Haystack, txtai
Transformers.js for browser embedding tasks
ONNX Runtime, and others.

An interesting browser demo ("Semantic Galaxy") lets you embed many texts in the browser and explore their relationship in 2D, with no server roundtrip .

Use Cases

On-device search: Embed emails, documents, notes locally.
Semantic similarity: Group or match text based on meaning.
Clustering or classification: Use embeddings downstream in models like k-means or classifiers.
RAG pipelines: Integrate with Gemma 3n to power retrieval-augmented features entirely offline.

FAQs

Q1: Does Embedding Gemma require an internet connection?
No. It runs fully on-device with no dependency on external servers once the model is downloaded.

Q2: What languages does it support?
Over 100 languages, thanks to multilingual training data used during development .

Q3: Why use smaller embeddings (128/256) instead of full size (768)?
Smaller embeddings reduce memory and storage needs, speed up similarity searches, and may be sufficient for simpler tasks. MRL makes this flexible .

Q4: Is quantization required for low memory use?
Quantized models shrink memory usage to below 200 MB and still deliver good embedding quality. It’s recommended for constrained devices.

Q5: Is fine-tuning worth it?
Yes—fine-tuning on domain-specific datasets (like medical retrieval) can yield better performance than relying on the base model .

Q6: How does Embedding Gemma compare to bigger models?
On benchmarks like MTEB, it ranks highest among text-only, multilingual embedding models under 500 million parameters. In domain-specific tasks, a fine-tuned version even outperforms much larger models .

Q7: Can it handle long documents?
Yes—it processes up to 2,048 tokens per input, enabling embedding of long passages or small documents .

Q8: What hardware is suitable?
It can run on CPU, GPU, or even EdgeTPU. On EdgeTPU, embeddings can be generated in under 22 milliseconds.

Q9: How does embedding generation work in the browser?
Using Transformers.js, embedding tasks can be done locally in the browser. The “Semantic Galaxy” demo shows interactive clustering and 2D mapping of embeddings without sending data to a server .

Q10: Where can I access the model files?
Embedding Gemma is available on:

Hugging Face (after accepting license)
Kaggle
Vertex AI model garden