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Your phone’s vector index might be bigger than the AI model running it

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Your phone’s vector index might be bigger than the AI model running it

Google released EmbeddingGemma 2 on Tuesday, putting text, code, image, video, and audio retrieval into a 740-million-parameter open model that used about 567MB of active RAM with quantization in the company’s testing on a Pixel 11 Pro.

Built on Gemma 4, the model maps all five input types into the same 768-dimensional vector space. Images no longer have to be captioned and audio doesn’t have to be transcribed before either can be searched alongside text.

The company released the weights under Apache 2.0, and on-device deployment through LiteRT and MediaPipe Tasks is available now. An Android ML Kit integration, with NPU acceleration on devices that support it, is due in the coming weeks.

The full model tops out at 740 million parameters, but developers only load the encoders their data requires. Text and code run on a 270-million-parameter base, which Google measured at about 191MB of active RAM on the same phone.

With the vision encoder for images and video, the model reaches 440 million parameters; adding the audio encoder instead brings it to 570 million, while loading both takes it to the full 740 million. Every configuration projects into the same embedding space, so a team that starts with a text-only index can add image or audio search later without re-embedding anything it has already stored.

5 minutes of audio, 29 images or 58 video frames in a single input.



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