---
type: "post"
id: "google-released-embeddinggemma-2-a-740m-open-weight-8a4f42"
slug: "google-released-embeddinggemma-2-a-740m-open-weight-8a4f42"
title: "Google released EmbeddingGemma 2 , a 740M open-weight multimodal embedding model for text, image, audio, and video."
description: "EmbeddingGemma 2 makes shared multimodal retrieval available as a local model rather than requiring separate cloud or on-device stages for image, speech, and text processing."
retrieval_nugget: "EmbeddingGemma 2 makes shared multimodal retrieval available as a local model rather than requiring separate cloud or on-device stages for image, speech, and text processing."
published_at: "2026-10-21"
updated_at: "2026-10-21"
record_date: "2026-10-21"
date_kind: "published_at"
topics: ["google"]
source_urls: ["https://developers.googleblog.com/google-ai-edge-with-embeddinggemma-2"]
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stable_id: "post:google-released-embeddinggemma-2-a-740m-open-weight-8a4f42"
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 released EmbeddingGemma 2 , a 740M open-weight multimodal embedding model for text, image, audio, and video.

## Retrieval answer

EmbeddingGemma 2 makes shared multimodal retrieval available as a local model rather than requiring separate cloud or on-device stages for image, speech, and text processing.

EmbeddingGemma 2 makes shared multimodal retrieval available as a local model rather than requiring separate cloud or on-device stages for image, speech, and text processing. The thesis is that a compact shared representation can move some media search from model orchestration into an on-device retrieval layer.

Google announced a 740-million-parameter open-weight model that maps text, images, video frames, and audio into one vector space. Google reports about 567 MB active RAM for the full multimodal model on a Pixel 11 Pro and about 191 MB for text-only weights. The mechanism is to encode both queries and local media, then rank candidates by similarity; Google’s demos use this for natural-language photo search and timestamp retrieval in video without generated captions or audio transcription. The mechanism is a single embedding space plus local indexing and similarity ranking, rather than a chain of modality-specific models and intermediate text.

Why now: Google is showing a route from model release to user-facing local search demos, while saying ML Kit support is planned in the coming weeks. This matters for builders because the first implication is that teams can prototype private, offline media retrieval around one embedding interface instead of maintaining separate captioning, transcription, and text-embedding stages. The first implication is that teams can prototype private, offline media retrieval around one embedding interface instead of maintaining separate captioning, transcription, and text-embedding stages.

The evidence boundary is Google’s announcement: its memory figures and descriptions are vendor-reported, not independent benchmarks, and the ML Kit integration is a future plan rather than present availability. The evidence boundary is limited to the vendor’s described model, demos, and roadmap; no comparative quality, latency, battery, or device-compatibility results are supplied here. Watch whether the planned ML Kit support ships and how retrieval quality, latency, and memory use vary across devices. Watch whether the planned ML Kit support ships and how retrieval quality, latency, and memory use vary across devices.

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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