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EmbeddingGemma 2 is a best-in-class open model for natively multimodal embeddings Skip to main content EmbeddingGemma 2: an open, lightweight multimodal embedding model Innovation & AI Products & platforms Company news Feed Newsletter Back Innovation & AI See all in Innovation & AI Models & Research Google DeepMind Google Research Google Labs Gemini models Quantum computing See all Products Developer tools Gemini app Gemini Notebook See all Infrastructure & cloud Global network Google Cloud See all Technology Safety & Security Health See all Learn more: Google DeepMind blog Google Research blog Google Developers blog Google Cloud blog Back Products & platforms See all in Products & platforms Products Search Maps Chrome Google Health Google Workspace Learning & Education Shopping See all Platforms Android Google Play Wear OS See all Devices Pixel Google Nest Fitbit Chromebook Googlebook See all Learn more: Google Ads & Commerce blog Waze blog Back Company news See all in Company news Outreach & initiatives Creating opportunity Safety & security Google.org Public policy Sustainability Health See all Leadership Sundar Pichai, CEO More authors See all Inside Google Around the globe Life at Google See all Learn more: Google Security blog Innovation & AI Innovation & AI See all in Innovation & AI Models & Research Google DeepMind Google Research Google Labs Gemini models Quantum computing See all Products Developer tools Gemini app Gemini Notebook See all Infrastructure & cloud Global network Google Cloud See all Technology Safety & Security Health See all Learn more: Google DeepMind blog Google Research blog Google Developers blog Google Cloud blog Products & platforms Products & platforms See all in Products & platforms Products Search Maps Chrome Google Health Google Workspace Learning & Education Shopping See all Platforms Android Google Play Wear OS See all Devices Pixel Google Nest Fitbit Chromebook Googlebook See all Learn more: Google Ads & Commerce blog Waze blog Company news Company news See all in Company news Outreach & initiatives Creating opportunity Safety & security Google.org Public policy Sustainability Health See all Leadership Sundar Pichai, CEO More authors See all Inside Google Around the globe Life at Google See all Learn more: Google Security blog Feed EmbeddingGemma 2: an open, lightweight multimodal embedding model Share x.com Facebook LinkedIn Mail Copy link [] Preferences Global (English) Africa (English) Australia (English) Brasil (Português) Canada (English) Canada (Français) Česko (Čeština) Deutschland (Deutsch) España (Español) France (Français) Greece (Ελληνικά) India (English) Indonesia (Bahasa Indonesia) Ireland (English) Italia (Italiano) 日本 (日本語) 대한민국 (한국어) Latinoamérica (Español) Malaysia (English) الشرق الأوسط وشمال أفريقيا (اللغة العربية) MENA (English) Nederlands (Nederland) New Zealand (English) Polska (Polski) Portugal (Português) România (Română) Sverige (Svenska) ประเทศไทย (ไทย) Türkiye (Türkçe) 台灣 (中文) Links Images RSS feed x.com Facebook LinkedIn Mail Copy link Newsletter Breadcrumb Home Innovation & AI Technology Developer tools EmbeddingGemma 2: an open, lightweight multimodal embedding model Oct 06, 2026 | x.com Facebook LinkedIn Mail Copy link EmbeddingGemma 2 is the most capable model for on-device multimodal embeddings, natively mapping combinations of text, images, audio, and video into a unified embedding space. Sahil Dua Research Engineer, Google DeepMind Henrique Schechter Vera Research Engineer, Google DeepMind Share x.com Facebook LinkedIn Mail Copy link Your browser does not support the audio element. Listen to article [[duration]] minutes This content is generated by Google AI. Generative AI is experimental Voice Speed Voice Speed 0.75X 1X 1.5X 2X We introduced EmbeddingGemma last year to provide a lightweight option for high-quality text embeddings, to help your apps organize, search, and connect information directly on consumer hardware. The developer community’s response blew past our expectations. With more than 20 million downloads, builders have used it to power smarter on-device search tools and privacy-first retrieval augmented generation (RAG) pipelines.Today, we’re launching EmbeddingGemma 2, expanding beyond text to unify code, images, video, and audio in a shared embedding space. Built on the Gemma 4 architecture and released under a commercially permissive Apache 2.0 license, EmbeddingGemma 2 has 740 million parameters, making it optimal for on-device inference. It can help find a specific video clip from a voice memo, or search through hours of audio recordings based on a text query, all processed by a single, natively multimodal model. Built from the same technology as Gemini Embedding models, EmbeddingGemma 2 is:Best-in-class for its size: Achieves leading scores among sub-1B multimodal embedders for its size across benchmarks like MTEB (Massive Text Embedding Benchmark) Code and MAEB (Massive Audio Embedding Benchmark), while matching or outperforming many larger models across text, vision, and audio tasks.Modular by design: Requires as little as 270M parameters for text-only workloads with optional vision (170M) and audio (300M) encoders for full multimodal support.Storage-efficient: Using Matryoshka Representation Learning (MRL), developers can dynamically truncate output vectors from 768 dimensions down to 512, 256, or 128 dimensions. This provides up to 6x storage reduction for local vector databases and memory usage.Optimized for on-device performance: Runs efficiently within tight resource constraints. With quantization, on a Google Pixel 11 Pro, EmbeddingGemma 2 requires as little as ~191MB active RAM for text-only weights and ~567MB for the full multimodal model.Extended context ready: Features an 8K token context window (4x larger than EmbeddingGemma 1), allowing it to process up to 5.5 minutes of audio, 29 images, 58 video frames, or interleaved combinations thereof directly on local hardware. Achieving top-tier quality for code, vision, and audioEmbeddingGemma 2 matches the strong multilingual text performance of EmbeddingGemma while delivering a significant 9.92-point improvement on code performance (in MTEB Code, from 68.76 to 78.68), making it well-suited for local codebase indexing, semantic code search, and coding agent retrieval. Across image, video, documents, and audio, it sets a new standard in quality-per-parameter for sub-1B models and even outperforms some specialist models more than twice its size. Find full evaluation metrics and model information in the EmbeddingGemma 2 model card. Enabling semantic search, routing, and retrieval, fully on-deviceEmbeddingGemma 2 brings robust capabilities directly to edge hardware. Generating embeddings locally helps ensure data privacy, reduces pipeline latency, and empowers developers to build cross-modal search and retrieval that works entirely offline.When paired with generative models such as Gemma 4, EmbeddingGemma 2 enables on-device RAG pipelines that understand complex multimodal data. Because EmbeddingGemma 2 is built on Gemma 4 and shares its text tokenizer and audio encoder, developers can run both models together in a unified pipeline with a lower combined total memory footprint. Use text or an image to find the top matches in your media library based on semantic similarity. Try it in Google AI Edge Gallery’s Instant Media Search. Locate specific moments in video using text or audio queries. Try it in Google AI Edge Gallery’s Video Moments Finder. Pair EmbeddingGemma 2 for local file retrieval with Gemma 4 for contextual reasoning. Try it in the Google AI Edge Foresight app. Create real-time decision engines leveraging multimodal context for classification, routing, and predictive capabilities via the MediaPipe Decision Task API. To learn how to build on-device search and RAG systems with LiteRT, read the Google AI Edge blog post. Getting started with EmbeddingGemma 2We worked closely with the following partners to ensure EmbeddingGemma 2 works immediately where you build:Download the models: Find the model weights on Hugging Face and Kaggle, with Gemini Enterprise Agent Platform Model Garden availability coming soon. Visit LiteRT Community on Hugging Face for models optimized for on-device.On-device deployment: Develop cross-platform apps with Google AI Edge MediaPipe for turnkey embedding, retrieval & decision tasks or LiteRT for custom model integration. Build for the browser with transformers.js or WebGPU.Use your favorite development tools: Serve the model efficiently using transformers, sentence-transformers, MLX, vLLM, llama.cpp, SGLang, Ollama, and LMStudio. Store your embedding vectors with Qdrant.Fine-tuning: Follow guidance by Unsloth for how to fine-tune EmbeddingGemma 2 for your use cases.Explore our developer guide, documentation, and guides for inference and fine-tuning. Posted in: Google DeepMind Developer tools Related stories Health Applying technology to understand and treat disease By Yossi Matias & Pushmeet Kohli Google DeepMind We're making it easier to identify AI-generated content globally. By Pushmeet Kohli AI The latest AI news we announced in September 2026 By Blog Team Google DeepMind We’re introducing SynthID Bio, bringing our watermarking technology to synthetic biology. 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