# Google DeepMind releases EmbeddingGemma 2 multimodal embedding model

> **Key takeaway:** EmbeddingGemma 2 is an open-source, 740M parameter multimodal model designed for efficient on-device text, image, audio, and video embeddings.

**Published:** 2026-10-06T19:57:04+00:00 
**Source:** Google DeepMind Research 
**Category:** ai-frontier 
**Canonical URL:** https://fosswire.org/news/google-deepmind-releases-embeddinggemma-2-multimodal-embeddi.html 

## Summary
Google DeepMind has launched EmbeddingGemma 2, a lightweight multimodal embedding model released under the Apache 2.0 license. Built on the Gemma 4 architecture, the 740M parameter model maps text, code, images, audio, and video into a unified embedding space. It is specifically optimized for on-device inference, supporting privacy-first RAG pipelines and local semantic search.

## Technical specifications of EmbeddingGemma 2
- **Architecture**: 740M parameters, built on Gemma 4, with modular encoders (270M for text-only, 170M for vision, 300M for audio).
- **Context Window**: 8K tokens, enabling processing of up to 5.5 minutes of audio or 58 video frames.
- **Storage Efficiency**: Uses Matryoshka Representation Learning (MRL) to allow vector truncation from 768 down to 128 dimensions.
- **Memory Footprint**: Requires approximately 191MB RAM for text-only weights and 567MB for the full multimodal model on quantized hardware.
- **Performance**: Significant improvements in code tasks (MTEB Code score increased from 68.76 to 78.68).

## Why it matters
The Apache 2.0 release allows developers to build private, offline, and low-latency multimodal search and RAG applications directly on consumer hardware.