AI model
Open-weight multimodal embedding model from Google DeepMind in the Gemma family, built to map text, code, images, video and audio into one vector space on local hardware.
Ollama's OpenAI-compatible endpoint reported 3,875 prompt tokens per second where the real rate was 125, a dev.to author found. Any benchmark that repeats a prompt and reads that field overstates prefill speed by the share served from cache.
Reality
Google DeepMind's EmbeddingGemma 2 maps text, images, video and audio into one vector space using 740M Apache-licensed parameters. It is built to run on phones and laptops, so the case for a hosted embedding API now depends on whether its quality holds up on a buyer's own data.
Reality
Google DeepMind released EmbeddingGemma 2, a 740M-parameter open model that runs on a phone and embeds text, code, images, video and audio in one space. Teams running a separate embedder per modality can consolidate on it if Google's reported benchmark numbers hold on their own data.
Reality
Google has released AI Edge Foresight, a Granola-style Mac meeting note-taker that works fully offline on its on-device EmbeddingGemma 2 model. The Verge reported it is free and experimental, so teams barred from cloud transcription can pilot it cheaply.
Perspective Coverage
3 publishersReality
Google released EmbeddingGemma 2, an Apache 2.0 model that maps text, code, images, video and audio into one embedding space with 740M parameters. Phone apps get offline cross-media search from one set of weights, within limits set by a shared 8K-token window and lossy vector truncation.
Perspective Coverage
17 publishersReality