Google claims EmbeddingGemma 2 outperforms rival embedding models twice its size
Google released EmbeddingGemma 2, a 740-million-parameter open multimodal embedding model.
Google released EmbeddingGemma 2, an open model that turns text, images, video, audio, and code into vectors for similarity search. At 740 million parameters, Google says it is the most compact model of its kind and outperforms rivals up to twice its size on multimodal embedding benchmarks. It scores 78.68 on MTEB Code, up from 68.76, runs locally in about 20 to 70 milliseconds, and uses roughly 191 MB of RAM. A 270-million-parameter text-only variant is available, with weights on Hugging Face and Kaggle.
- EmbeddingGemma 2 has 740 million parameters and open weights.
- It scores 78.68 on MTEB Code, up from 68.76.
- Local runs need about 191 MB of RAM.
- A 270-million-parameter text-only version is available.
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Google released EmbeddingGemma 2, an open model that converts text, images, video, audio, and code into numerical vectors so similar content can be found and compared more easily. 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 model runs locally without an API key. Each query takes about 20 to 70 milliseconds via WebGPU in the browser. It needs only around 191 MB of RAM and cuts local vector database storage by up to six times. For text-only tasks, a 270-million-parameter version is enough.
Paired with small open models like Gemma 4, EmbeddingGemma 2 can run offline RAG apps without sending data to external servers. The weights are available on Hugging Face and Kaggle, along with a developer guide and documentation.
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