Google released its EmbeddingGemma 2 paper.

embedding model for on-device multimodal AI, under an Apache 2.0 license.

puts text, code, images, audio and video into 1 searchable space on phones. gives phones a missing piece: a way to understand and search your own stuff without sending it to a server.

shows that a 744M-parameter open model can embed text, images, audio, and video, beating many models 2 to 12 times larger.

On a Pixel 11 Pro, quantized text weights take about 191MB of active RAM, and the full multimodal model takes about 567MB.

The context window grows 4x to 8K tokens, enough for roughly 5.5 minutes of audio, 29 images or 58 video frames in 1 input.

They built EmbeddingGemma 2 on Gemma 4, trained it to copy the larger Gemini Embedding 2, then tuned it with contrastive training. All media types share 1 vector space, and the full model fits in 567 MB on a Pixel 11 Pro with 4-bit weights.

Embedding speech directly beat transcribing it, 69.54 versus 57.59 on the MSEB spoken-query retrieval benchmark. Code search scored 78.68 on MTEB Code, up from 68.76 for EmbeddingGemma 1.