In the news
Bring multimodal semantic search to the edge with EmbeddingGemma 2- Google Developers Blog
Google Developers · Published · 3 min read
In 30 seconds
- What happened
- Google DeepMind released EmbeddingGemma 2, a 740M parameter multimodal embedding model that maps text, images, video, and audio into unified vectors for on-device search.
- Why it matters
- Android and cross-platform developers building privacy-first local search, media retrieval, or semantic applications where latency and memory efficiency matter on edge devices.
- Watch out
- Model requires 191MB to 567MB RAM depending on modality; performance varies across devices; availability on Android through ML Kit still coming in coming weeks.
- embedding
- edge
Who else ran this
- EmbeddingGemma 2: The Developer Guide- Google Developers BlogGoogle Developers
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