Google DeepMind releases EmbeddingGemma 2 multimodal embedding model

On October 6, 2026, Google DeepMind released EmbeddingGemma 2, announced in a post on Google's official blog by Google DeepMind research engineers Sahil Dua and Henrique Schechter Vera. It is an open multimodal embedding model with 740M total parameters, released under the Apache 2.0 license, that maps text (including code), images, video, and audio — and combinations of them — into a single, unified 768-dimensional vector space. Google describes it as the most capable model for on-device multimodal embeddings.

A post on the r/LocalLLaMA subreddit fills in the architecture: the 740M total combines a 270M-parameter text model with modular vision (170M) and audio (300M) encoders. The model is designed to run on consumer hardware such as mobile devices and laptops, and its model page sits at huggingface.co/google/embeddinggemma-2.

Coverage followed on October 7, with Techmeme, r/LocalLLaMA, and Unite.AI each reporting the launch within roughly half an hour of one another. The accounts differ slightly in how they describe the modality coverage. Techmeme says the model maps code, images, video, and audio into a shared embedding space. Unite.AI describes five modalities — text, code, images, video, and audio — mapped into the same 768-dimensional space. The r/LocalLLaMA post frames it as text (including code), images, video, and audio, plus any combination of those inputs. The reports agree on the 740M parameter count, the single 768-dimensional embedding space, the Apache 2.0 license, and the focus on consumer-grade hardware.

Nothing in the available material goes beyond the release itself; there is no reported follow-up past the initial announcement and open-source publication.