Google DeepMind has launched EmbeddingGemma 2, its first open, native multimodal embedding model, with weights publicly available on Hugging Face and Kaggle, licensed under Apache 2.0 for commercial fine-tuning. The model is built on the Gemma 4 architecture, mapping text, code, images, videos, and audio into the same 768-dimensional vector space.

The full model has 740 million parameters and can be disassembled as needed: core text and code at 270 million (with a backbone of 130 million and embedding layer of 140 million), visual encoder at 170 million, and audio encoder at 300 million. Developers can load only text or combine to create models of 440 million, 570 million, or 740 million parameters. The context window is 8192 tokens, covering over 100 languages, and the audio side can handle approximately 5.5 minutes of recording.

After quantization on the Pixel 11 Pro, the active memory for pure text weights is about 191 MB, while the full multimodal model is approximately 567 MB. The output supports Matryoshka truncation, allowing dimensions to be reduced from 768 to 512, 256, or 128, with Google stating that local vector storage costs can be reduced to about one-sixth. It shares the text tokenizer and audio encoder with Gemma 4, resulting in lower total memory when running both ends simultaneously.

Compared to the previous generation pure text EmbeddingGemma, code retrieval MTEB increased from 68.76 to 78.68, a rise of 9.92 points, or about 14%; the multilingual text MTEB v2 is 61.36, up from 61.15 in the previous generation. The average for image MIEB lite is 64.64, video MMEB v2 hit rate is 50.67, audio MSEB retrieval is 69.54, and MAEB average is 49.39. Google claims it leads in quality among multimodal embeddings under 1 billion parameters, surpassing some dedicated models by more than twice the size.

The previous generation EmbeddingGemma has been downloaded over 20 million times, primarily for edge-side search and privacy-focused retrieval-enhanced generation. The new model can locate video clips by voice memo or retrieve hours of audio using text, with data not needing to leave the device. Deployment channels also include Ollama, llama.cpp's GGUF and LiteRT, and browser-side can utilize transformers.js or WebGPU.

Edge-side retrieval shifts purchases from cloud-based embedded APIs to open-source weights and local computing power. Application developers, terminal manufacturers, and privacy-sensitive industries are the direct users, putting pressure on cloud embedding services and larger parameter dedicated models. The event-driven nature stems from the extension of the Gemma 4 open weight system, with funding and traffic flowing towards Hugging Face weights, edge-side inference frameworks, and local vector libraries, rather than per-call cloud APIs.

Source: Public Information