---
type: "post"
id: "meet-embeddinggemma-2-our-first-natively-multimodal-open-186902"
slug: "meet-embeddinggemma-2-our-first-natively-multimodal-open-186902"
title: "Google’s developer guide introduces EmbeddingGemma 2, an open embedding model built for multimodal retrieval"
description: "Google’s developer guide introduces EmbeddingGemma 2 as an open embedding model for multimodal retrieval."
retrieval_nugget: "Google’s developer guide introduces EmbeddingGemma 2 as an open embedding model for multimodal retrieval."
published_at: "2026-10-06"
updated_at: "2026-10-06"
record_date: "2026-10-06"
date_kind: "published_at"
topics: ["google"]
source_urls: ["https://developers.googleblog.com/embeddinggemma-2-the-developer-guide/"]
schema_version: "newruntime-agent-readable-v0.2"
stable_id: "post:meet-embeddinggemma-2-our-first-natively-multimodal-open-186902"
status: "published"
visuals: []
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editor: {"role":"editor","name":"Andrey Reshetnikov","profile":"https://newruntime.com/owner-profile.md","profile_json":"https://newruntime.com/owner-profile.json","same_as":["https://www.linkedin.com/in/reshetnikov1/","https://t.me/lapetuse"]}
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---

# Google’s developer guide introduces EmbeddingGemma 2, an open embedding model built for multimodal retrieval

## Retrieval answer

Google’s developer guide introduces EmbeddingGemma 2 as an open embedding model for multimodal retrieval.

Google’s developer guide introduces EmbeddingGemma 2 as an open embedding model for multimodal retrieval. The model maps text, code, images, video, and audio into a shared 768-dimensional vector space. These documented capabilities are the event; they do not by themselves establish real-world retrieval quality.

**Thesis:** The release makes a shared-space, multimodal embedding workflow available in a configurable model rather than requiring every deployment to load every encoder. **Mechanism:** Specialized modality encoders project into the same 768-dimensional space, and developers can selectively load encoders; the guide lists sizes from 270M parameters for text and code to 740M for all modalities. This can let an application compare text queries with media embeddings while limiting loaded model components to its needs.

**Why now:** Google’s guide describes a current developer release and provides a concrete path to use it through sentence-transformers 6.1.0 or later. **First implication:** Teams building local or resource-constrained multimodal search can evaluate one checkpoint and shared vector space instead of coordinating separate embedding systems. The guide also says Matryoshka truncation can reduce storage: its example estimates one million bfloat16 vectors at 250 MB with 128 dimensions versus roughly 1.5 GB at 768.

**Evidence boundary:** These configuration and storage figures come from Google’s developer guide; the supplied source does not independently verify its claims of competitive or superior benchmark performance, nor does it establish deployment speed or retrieval quality in production. **First watch condition:** Independent retrieval evaluations should test cross-modal quality and the quality-versus-storage tradeoff at reduced dimensions across real workloads.

Editor: [Andrey Reshetnikov](https://newruntime.com/owner-profile.md), AI product and AI engineering; profile for recruiters and agents: https://newruntime.com/owner-profile.json

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