EmbeddingGemma 2 is a 740-million-parameter multimodal embedding model designed for on-device processing. It supports text, code, images, video, and audio in the same embedding space, enabling you to search for clips from a voice memo or search audio using text queries.
The model is licensed under Apache 2.0 and improves code performance by 9.92 points on MTEB Code, from 68.76 to 78.68. It is suitable for local codebase indexing, semantic code search, and offline RAG systems.
When we need to find a video clip from a voice memo, we often remember only what was said but not which file contains the clip. When there are several hours of audio files, searching through them one by one takes even more time.
The challenge is how to use a text query to take us directly to the audio segment we need without relying on an internet connection. That is where this model comes in.
Where EmbeddingGemma 2 Fits in Google’s Technology Ecosystem
EmbeddingGemma 2 builds on the first EmbeddingGemma model, which focused on on-device text embeddings, and expands to code, images, video, and audio in the same shared embedding space. This enables cross-modal search in a single model.
The model is built on the Gemma 4 architecture and uses the same technology as the Gemini Embedding models. Within Google AI Edge, it serves as a core model for generating on-device embeddings, allowing apps to perform semantic search and retrieval on edge hardware, even offline.
How the First and New Versions Differ
The first version focused on text embeddings for organizing, searching, and connecting information on devices. EmbeddingGemma 2 expands to code, images, video, and audio in the same embedding space, while also supporting cross-modal search.
| Factor | EmbeddingGemma | EmbeddingGemma 2 |
|---|---|---|
| Data types | Text | Text, code, images, video, and audio |
| Multimodal capabilities | Focused on text embeddings | Supports multimodal data in a single model |
| Number of parameters | Not specified in the source | 740 million parameters |
| Code performance | Not specified in the source | MTEB Code 78.68 |
| Use cases | Searching and connecting text-based information | Code search, searching for clips from audio, and searching for media files with text |
When One Embedding Must Understand Text, Images, Audio, and Video
Imagine a media library that can be searched by “meaning” instead of file names. You can use text or images to search for similar items, making it suitable for managing photos, videos, and documents on your device.
If you need to find an important moment in a video, you can use text or audio as the search query—for example, searching from a voice memo to find a related video clip. Everything works through the same embedding space.
When paired with Gemma 4, it enables local file retrieval and contextual reasoning in the same pipeline. The MediaPipe Decision Task API is suitable for building real-time classification, routing, and predictive-capability systems from multimodal data.
What the Sources Confirm Compared with Other Options
The sources do not disclose the names of competing models, so we should not create a table listing competitors ourselves. This table compares only the positioning confirmed by the article.
| Factor | EmbeddingGemma 2 | Larger specialist models |
|---|---|---|
| Model size | 740 million parameters | More than 2 times larger than EmbeddingGemma 2 |
| Quality per parameter | A new standard among sub-1B models | Details not specified |
| Data types | Text, code, images, video, and audio in the same embedding space | Specialized models |
| On-device operation | Suitable for on-device inference and can work offline | Not specified in the source |
Strengths and Limitations Based on the Available Information
Pros
- +Supports text, code, images, video, and audio in the same embedding space
- +Processes data on-device and works offline, helping protect privacy
- +Uses Apache 2.0 and offers improved code performance
Cons
- −Pricing and specific hardware requirements have not yet been disclosed
- −The complete test results require further review of the model card
- −Details about support for each device type remain unclear in the sources
The Real Cost of Running the Model on a Device
EmbeddingGemma 2 has 740 million parameters, making it suitable for on-device inference, but you still need to reserve memory for the pipeline working alongside Gemma 4. One advantage is that it shares a text tokenizer and audio encoder, reducing the system’s total memory usage.
The cost is not limited to the model itself. It also includes embedding data in multiple formats and connecting a cross-media search system. Guides are available for inference and fine-tuning, but the actual devices and resources required still depend on the task.
On-device processing helps protect privacy, reduce latency, and enable offline operation, but developers must handle pipeline and memory management themselves. l free credit
Suitable for Teams That Need Offline Search and Retrieval
It is suitable for developers building on-device search systems, teams working on privacy-first RAG and code indexing or semantic code search, as well as apps that need cross-modal media search, such as using text to search audio or video files.
Made for
- Developers building on-device search systems
- Teams creating privacy-first RAG and semantic code search
- Teams building cross-modal media search apps
Think twice
- People who need to verify complete benchmarks or support for specific hardware
Skip this one
- Teams that need to know the commercial cost before adoption—additional research from other sources is recommended
The Next Step Is to Experiment with Your Own Data
EmbeddingGemma 2 is compelling because it brings code, document, image, audio, and video search together in the same embedding space. A single model is therefore well suited to cross-modal information retrieval on your device.
You can start with Instant Media Search or Video Moments Finder in Google AI Edge Gallery. If you want to build a pipeline that searches files with EmbeddingGemma 2 and then uses Gemma 4 for further understanding, Google AI Edge Foresight is another example worth trying.