# Google EmbeddingGemma 2

- Link: https://100xfounder.com/startup/google-embeddinggemma-2/
- Published: 2026-10-12T12:31:00+05:30
- Author: 

EmbeddingGemma 2 is an open embedding model from Google DeepMind. An embedding model
turns content into vectors so that software can search, cluster or classify it by
meaning. EmbeddingGemma 2 does this for text (including code), images, video and
audio in a single shared space of 768 dimensions, so a text query can find a photo
and a voice memo can find a video clip, without a chain of separate captioning and
speech-to-text models.

The model has 740 million parameters in total, split into modules: a 270M text backbone
plus optional vision (170M) and audio (300M) encoders, so you load only what you
need. Google says that, quantised on a Pixel 11 Pro, it needs about 191MB of memory
for text alone and about 567MB for all modalities. It reads up to 8,192 tokens of
input, four times its predecessor, and its vectors can be shortened to 512, 256 
or 128 dimensions to save storage. It is built on the Gemma 4 architecture, understands
more than 100 languages according to its model card, and runs offline. Google reports
a jump on the MTEB code benchmark from 68.76 to 78.68 over the first EmbeddingGemma.

The weights are free under the Apache 2.0 licence, subject to Google’s Gemma prohibited-
use policy, and are available on Hugging Face and Kaggle, with tooling for Sentence
Transformers, Transformers, Ollama, llama.cpp, vLLM, MLX and others. Google announced
it on 6 October 2026; it was featured on Product Hunt on 12 October 2026 and finished#
1 for the day with 300 upvotes.

_We have not benchmarked EmbeddingGemma 2 ourselves. Figures on this page are Google’s
own, from its announcement and model card, and we say where each comes from._