Google's EmbeddingGemma 2 lifts MTEB Code by 9.92 points, runs on-device

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EmbeddingGemma 2 maps text, images, audio, video and code into one shared space. It is built for local search, retrieval and RAG.

Here's what you can build with it:

• Its MTEB Code score rises from 68.76 to 78.68 over EmbeddingGemma.
• The full model has 740M parameters, and text-only use needs as little as 270M.
• On a Pixel 11 Pro, quantized RAM is about 191MB for text-only or 567MB multimodal.
• Matryoshka vectors shrink from 768 to 128 dimensions for up to sixfold storage savings.
• An 8K-token context window, four times EmbeddingGemma 1, and Apache 2.0 licensing.

Pair it with Gemma 4 for local RAG, since they share a text tokenizer and audio encoder.

One catch: Google gives no numerical speed results.

Try it: download weights from Hugging Face or Kaggle, deploy with MediaPipe or LiteRT, or run in the browser with transformers.js. It also serves via sentence-transformers, vLLM, llama.cpp and Ollama.

Sources: blog.google · deepmind.google