EmbeddingGemma 2

Google DeepMind has released EmbeddingGemma 2, an Apache 2.0 open embedding model with 740M total parameters that maps text, images, video and audio into a single 768-dimensional vector space. According to the model card, it improves code retrieval on MTEB (code, v1) from 68.76 to 78.68 over its predecessor while keeping an 8,192-token context window.

Google announced EmbeddingGemma 2, a 740M-parameter natively multimodal embedding model built on the Gemma 4 architecture and released under Apache 2.0. Google says it runs in ~191MB of active RAM for text-only weights and ~567MB for the full multimodal model on a quantized Pixel 11 Pro. Google also released a Mac app, AI Edge Foresight, to demonstrate it.

Google announced EmbeddingGemma 2, a 740M-parameter natively multimodal embedding model built on the Gemma 4 architecture and released under Apache 2.0. Google says the quantized model needs about 191MB of active RAM for text-only weights and about 567MB for the full multimodal model on a Pixel 11 Pro. Google also launched a Mac app, AI Edge Foresight, to demonstrate it.