Output vectors can be truncated from 768 dimensions to 512, 256, or 128, so a local index can shrink by up to 6× if you store 128 dimensions instead of 768. Context is 8,000 tokens, four times EmbeddingGemma 1, which Google says covers about 5.5 minutes of audio, 29 images, or 58 video frames. An app that only embeds text does not have to load vision and audio.
Google says that with quantization, a Pixel 11 Pro needs about 191 MB of active RAM for the text-only weights and about 567 MB for the full multimodal model. On code, Google reports MTEB Code moving from 68.76 to 78.68. The lab says the model leads sub-1-billion multimodal embedders on suites such as MTEB Code and the Massive Audio Embedding Benchmark, and that it matches EmbeddingGemma’s multilingual text scores. Those comparisons are the lab’s.
Serving paths named in the post include transformers, sentence-transformers, MLX, vLLM, llama.cpp, SGLang, Ollama, and LM Studio. Google says the model shares a text tokenizer and audio encoder with Gemma 4, so the embedder and a generative Gemma 4 model can sit in one pipeline. Fine-tuning notes point to Unsloth. Vector storage points to Qdrant. The text-only predecessor passed 20 million downloads by Google’s count. On-device examples in the post are Instant Media Search, Video Moments Finder, and a pairing with Gemma 4 in the AI Edge Foresight app.