Google Open-Sources EmbeddingGemma 2, a 740M Multimodal Embedding Model
Google DeepMind has open-sourced EmbeddingGemma 2 (Apache 2.0), a 740M-parameter multimodal embedding model that maps text, code, images, video and audio into a shared vector space and runs on-device with just 191MB of memory.
2026-10-09 ~ 2026-10-11 · 3 related posts
- Episode 1: Unsloth Releases EmbeddingGemma-2 Multimodal Embedding Model(2026-10-06, 2 posts)
- Episode 2: Google Open-Sources EmbeddingGemma 2, a Native Multimodal On-Device Embedding Model(2026-10-06, 62 posts)
- Episode 3: Google EmbeddingGemma 2 Runs Locally in Browsers via WebGPU(2026-10-07, 3 posts)
- Episode 4: EmbeddingGemma 2 Delivers Unified Multimodal Search with 740M Parameters(2026-10-07, 3 posts)
- Episode 5: Google Open-Sources EmbeddingGemma 2, a 740M Multimodal Embedding Model(2026-10-09, 3 posts)
- Episode 6: Open-EmbeddingGemma Reproduces Google's 740M Embedding Model in PyTorch(2026-10-10, 3 posts)
- Episode 7: Google Open-Sources EmbeddingGemma 2, a Multimodal Embedding Model That Runs on Phones(2026-10-11, 6 posts)
- Google open-sources EmbeddingGemma 2: 740M multimodal embeddings that run on a Pixel — Prompt Engineering · 2026-10-09
- How EmbeddingGemma 2 works: six steps from five modalities to one 768-dim vector — KyeGomezB · 2026-10-10
- EmbeddingGemma 2 ships at 740M params, runs in 191MB RAM and hits 620 embeddings/sec on-device — aronchick · 2026-10-11