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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteGoogle DeepMind’s EmbeddingGemma 2 maps text and code, images, video, and audio into a shared 768-dimensional embedding space, so developers can retrieve related items across media types using vector similarity. The 740-million-parameter figure describes the full multimodal configuration—not every setup—and Google lists the model under the Apache 2.0 license.
What EmbeddingGemma 2 does
EmbeddingGemma 2 is an embedding model for turning content into vectors that represent meaning. Because supported modalities share a vector space, a text query can be compared with image, video, or audio embeddings to find semantically related material. It is intended for retrieval and related tasks, not as a general-purpose conversational generator.
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Google says the model is based on the Gemma 4 architecture, supports 100+ languages, has an 8,192-token context window, and produces native 768-dimensional embeddings. Its model card also describes task-steered text prefixes for search, classification, clustering, and semantic similarity. These are capabilities documented by Google, not independent evaluations.
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What the 740M parameter count includes
The full configuration combines text and code processing with vision and audio encoders. Google documents smaller configurations when developers omit encoders they do not need.
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| Configuration | Parameters | Modalities included |
|---|---|---|
| Text/code | 270M | Text and code |
| Text plus vision | 440M | Text, code, and images |
| Text plus audio | 570M | Text, code, and audio |
| Full multimodal | 740M | Text, code, images, video, and audio |
Google’s model-card breakdown of the full 740M configuration is a 130M backbone, a 140M embedder, a 170M vision encoder, and a 300M audio encoder. The text/code portion is therefore 270M; the total drops when vision or audio components are excluded.
Benchmark results Google reports
Google AI for Developers’ 2026 model card reports the following results for the full-precision checkpoint. These are vendor-reported benchmark scores, not independent validation or a prediction of performance on a particular dataset.
| Benchmark | Metric | EmbeddingGemma 2 | Comparison |
|---|---|---|---|
| MTEB multilingual v2 | Mean task score | 61.36 | EmbeddingGemma 1: 61.15 |
| MTEB code v1 | NDCG@10 | 78.68 | EmbeddingGemma 1: 68.76 |
| MIEB lite | Mean task type | 64.64 | Not stated in the model card |
| MMEB v2 image | Hit@1 | 57.28 | Not stated in the model card |
| MMEB v2 visual document | NDCG@5 | 67.84 | Not stated in the model card |
| MMEB v2 video | Hit@1 | 50.67 | Not stated in the model card |
| MSEB retrieval | MRR@10 | 69.54 | Not stated in the model card |
| MAEB | Mean task score | 49.39 | Not stated in the model card |
For MTEB code v1, Google’s guide characterizes the change over EmbeddingGemma 1 as a 14% improvement; the model-card comparison gives the scores and NDCG@10 metric behind that summary. The figures do not establish that EmbeddingGemma 2 outperforms every alternative.
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The model supports 768 dimensions natively and documents truncated outputs of 512, 256, and 128 dimensions. Fewer dimensions use less storage, but Google’s guidance reports quality trade-offs, particularly for multimodal retrieval at 128 dimensions.
| Output dimensions | Google’s stated guidance | Storage implication |
|---|---|---|
| 768 | Native full-dimensional output | Highest storage among the listed options |
| 512 | Available truncation option; no specific quality-retention figure stated in the guide | Less than 768 dimensions |
| 256 | Retains most full-quality results on text and code and about 95% on image, video, and speech retrieval, according to Google | One-third the storage of 768 dimensions, according to Google |
| 128 | Retains around 90% of text and code quality; image, video, and speech retrieval quality falls to around 75%, according to Google | Google’s example: about 250 MB for one million vectors, versus roughly 1.5 GB at 768 dimensions |
The storage example is Google’s calculation for one million vectors stored in bfloat16; it is not a measurement of a complete vector database or search system. Google recommends validating the 128-dimensional option on the target data.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Implementation details that affect retrieval
Match the query and document dimensions
Use the same output dimension for query and indexed-document vectors. Mixing dimensions makes the vectors incompatible for comparison.
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Normalize after truncating
The model card warns that truncating a unit vector does not preserve its unit length. For cosine similarity, L2-normalize the vector after truncation; otherwise, rankings may degrade.
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Use task-specific prompts
For text retrieval, Google’s guide recommends distinct task prompts such as SearchQuery for queries and Document for documents. Its example also compares a natural-language query with image and audio embeddings in the shared space.
Account for documented input handling
- Google DeepMind says the model can process audio up to 5.5 minutes.
- Google’s developer guide says video is sampled at one frame per second by default.
- The guide specifies 16 kHz mono audio input.
These describe documented limits and handling, not a guarantee of processing speed or quality for every file.
Setup, license, and on-device use
Google’s October 6, 2026 developer guide provides setup examples for Sentence Transformers using the model identifier google/embeddinggemma-2 and specifies Sentence Transformers 6.1.0 or later. It describes loading text-only, text-plus-vision, text-plus-audio, or full configurations by disabling unused encoders, and lists Transformers and other deployment or inference tools. These are documented access routes; they do not establish identical support or performance across integrations.
Google AI for Developers and the model repository list the license as Apache 2.0. Google’s model card says the model is designed to run on consumer hardware such as phones and laptops; that is the vendor’s description, not an independent performance measurement.
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Google AI Edge describes local semantic-search demonstrations and reports approximately 191 MB of active RAM for text-only weights and approximately 567 MB for the full multimodal model on a Google Pixel 11 Pro. Those figures apply to that named-device example and should not be treated as minimum memory requirements for other hardware. Its October 6, 2026 article said Android availability through ML Kit was planned “in the coming weeks”; that dated statement does not establish current availability.
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