Google releases EmbeddingGemma 2 for on-device multimodal search

Many accounts welcomed EmbeddingGemma 2 for its compact 740M-parameter multimodal on-device performance that enables private embeddings across text, code, images, audio, and video.

Based on 219 sentiment-bearing replies from 148 accounts across 3 conversations.

A modular footprint for different setups

Google and related launch posts describe EmbeddingGemma 2 as modular rather than one fixed-size model. Those posts say configurations range from 270 million parameters for text-only use up to 740 million parameters for the full multimodal version. In an external launch summary of the release, Hugging Face’s Phil Schmid also listed 440 million parameters for text-and-vision and 570 million for text-and-audio. Osanseviero post Phil Schmid post

Google says active RAM use ranges from about 191MB to 567MB, depending on configuration. The company also says EmbeddingGemma 2 has an 8K context window, which it describes as four times larger than the first generation, and says that is enough to process up to 5.5 minutes of audio, 29 images or 58 video frames in one pass. Google specs post

Google’s benchmark claims

Google says the 740 million-parameter version is “best-in-class for its size” and that it outperforms some models more than twice its size. Sundar Pichai made a similar claim in his launch post, describing EmbeddingGemma 2 as a new open multimodal model focused on on-device efficiency. Google specs post Sundar Pichai post

Other launch-related posts point to claimed gains in code search. Phil Schmid’s post cites a 14% jump on MTEB Code, while Rohan Paul’s recap of the release gives a more specific claimed increase from 68.76 to 78.68. Phil Schmid post Rohan Paul summary

The release centers on a model that Google says can work across text, code, images, audio and video on local hardware under an Apache 2.0 license. For developers building private or offline search tools, the pitch is straightforward: keep the retrieval step on the device instead of sending personal files to a server.

Many accounts welcomed EmbeddingGemma 2 for its compact 740M-parameter multimodal on-device performance that enables private embeddings across text, code, images, audio, and video.

Based on 219 sentiment-bearing replies from 148 accounts across 3 conversations.