Google releases EmbeddingGemma 2, a multimodal embedding model for on-device search
Original titleBring multimodal semantic search to the edge with EmbeddingGemma 2
AISummary
Google DeepMind launched EmbeddingGemma 2, an open-weight 740M parameter model that maps text, images, video frames, and audio into one vector space.
The model can run on-device, with about 567MB active RAM for the full multimodal model on a Google Pixel 11 Pro, and is available through Google AI Edge Gallery, Google AI Edge Foresight on Mac, and MediaPipe Tasks, with ML Kit support coming in the weeks ahead.
AIWhy it matters
The post names concrete on-device apps, memory footprints, and latency figures, showing how a multimodal embedding model can power local search without cloud calls.
Source: Google Developers Blog · developers.googleblog.comPublished · added here