Google DeepMind released EmbeddingGemma 2 on October 6, a 740-million-parameter open embedding model that maps text, code, images, audio and video into a single 768-dimensional vector space. It is built on the Gemma 4 architecture and licensed under Apache 2.0, with weights on Hugging Face and Kaggle.

The model is modular: a 270M text-and-code backbone plus a 170M vision encoder and a 300M audio encoder that load only when needed. Quantized on a Pixel 11 Pro, Google reports about 191MB of active RAM text-only and about 567MB for the full multimodal model. The context window is 8K tokens — four times the original — fitting roughly 29 images, 58 video frames or 5.5 minutes of audio in one pass.

Google is explicitly pitching it for local, privacy-first retrieval: on-device search and RAG that never sends content to a remote service. On MTEB Code it scores 78.68 versus 68.76 for the first version, a jump of nearly 10 points. The first EmbeddingGemma, text-only, has passed 20 million downloads since September 2025.