Google DeepMind 已推出 EmbeddingGemma 2，这是一个开源的多模态嵌入模型，拥有 7.4 亿个参数，能够在共享嵌入空间中统一文本、图像、音频和视频。该模型基于 Gemma 4 架构，并采用 Apache 2.0 许可证发布，支持设备端推理，仅需 191MB RAM 即可处理纯文本任务，并具备 8K token 的上下文窗口。
为什么重要. 此次发布通过在设备端启用隐私保护、低延迟的多模态搜索和检索功能，直接在消费者硬件上提升AI能力，有助于开发者构建离线RAG流水线和边缘应用。
Create real-time decision engines leveraging multimodal context for classification, routing, and predictive capabilities via the MediaPipe Decision Task API .
delivering a significant 9.92-point improvement on code performance (in MTEB Code, from 68.76 to 78.68)
Use your favorite development tools : Serve the model efficiently using transformers, sentence-transformers, MLX , vLLM, llama.cpp , SGLang, Ollama , and LMStudio
When paired with generative models such as Gemma 4, EmbeddingGemma 2 enables on-device RAG pipelines that understand complex multimodal data.
EmbeddingGemma 2 has 740 million parameters, making it optimal for on-device inference.
Because EmbeddingGemma 2 is built on Gemma 4 and shares its text tokenizer and audio encoder, developers can run both models together in a unified pipeline with a lower combined total memory footprint.
Develop cross-platform apps with Google AI Edge MediaPipe for turnkey embedding, retrieval & decision tasks or LiteRT for custom model integration
Store your embedding vectors with Qdrant
Develop cross-platform apps with Google AI Edge MediaPipe for turnkey embedding, retrieval & decision tasks
Build for the browser with transformers.js or WebGPU
in MTEB Code, from 68.76 to 78.68
With more than 20 million downloads, builders have used it to power smarter on-device search tools and privacy-first retrieval augmented generation (RAG) pipelines.
To learn how to build on-device search and RAG systems with LiteRT, read the Google AI Edge blog post .
Today, we’re launching EmbeddingGemma 2 , expanding beyond text to unify code, images, video, and audio in a shared embedding space. Built on the Gemma 4 architecture and released under a commercially permissive Apache 2.0 license, EmbeddingGemma 2 has 740 million parameters, ma…
EmbeddingGemma 2 is the most capable model for on-device multimodal embeddings, natively mapping combinations of text, images, audio, and video into a unified embedding space.
