From the source

Google claims EmbeddingGemma 2 outperforms rival embedding models twice its size

Open embedding model runs locally, claims to beat larger rivals

Open-weight release

Google released EmbeddingGemma 2, an open multimodal embedding model with 740 million parameters that converts text, images, video, audio, and code into numerical vectors.

The company claims it outperforms competing models up to twice its size on multimodal embedding benchmarks while running locally without an API key, requiring about 191 MB of RAM and taking 20–70 milliseconds per query via WebGPU in the browser.

A 270-million-parameter text-only version is also available.

From the source
At 740 million parameters, Google says it's the most compact model of its kind and outperforms competing models up to twice its size on multimodal embedding benchmarks.
the-decoder.com

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Google claims EmbeddingGemma 2 outperforms rival embedding models twice its size

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