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    <title>The Tech Gazette · EmbeddingGemma 2</title>
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    <description>Every piece of the story &quot;EmbeddingGemma 2&quot;, from its sources' own pages — primary sources only.</description>
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    <lastBuildDate>Tue, 13 Oct 2026 05:26:00 GMT</lastBuildDate>
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      <title>Ollama 0.40.4: llama, update llama.cpp to b11521 for decision models and EmbeddingGemma 2 by @jmorganca in #18917</title>
      <link>https://github.com/ollama/ollama/releases/tag/v0.40.4-rc1</link>
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      <pubDate>Tue, 13 Oct 2026 05:26:00 GMT</pubDate>
      <dc:creator>Ollama</dc:creator>
      <category>Release · Releases</category>
      <description>Ollama — What's Changed llama: update llama.cpp to b11521 for decision models and EmbeddingGemma 2 by @jmorganca in #18917 server: reuse verified manifests and loaded models in inference routes by @dhiltgen in #18947 mlx: compact…</description>
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    <item>
      <title>Ollama 0.40.4: llama, update llama.cpp to b11521 for decision models and EmbeddingGemma 2 by @jmorganca in #18917</title>
      <link>https://github.com/ollama/ollama/releases/tag/v0.40.4-rc0</link>
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      <pubDate>Mon, 12 Oct 2026 19:00:00 GMT</pubDate>
      <dc:creator>Ollama</dc:creator>
      <category>Release · Releases</category>
      <description>Ollama — What's Changed llama: update llama.cpp to b11521 for decision models and EmbeddingGemma 2 by @jmorganca in #18917 server: reuse verified manifests and loaded models in inference routes by @dhiltgen in #18947 mlx: compact…</description>
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    <item>
      <title>EmbeddingGemma 2: an open, lightweight multimodal embedding model</title>
      <link>https://deepmind.google/blog/embeddinggemma-2-an-open-lightweight-multimodal-embedding-model/</link>
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      <pubDate>Tue, 06 Oct 2026 19:57:00 GMT</pubDate>
      <dc:creator>Google DeepMind</dc:creator>
      <category>Announcement · News</category>
      <description>Google DeepMind</description>
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    <item>
      <title>Introducing EmbeddingGemma 2: An open model for natively multimodal embeddings</title>
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      <pubDate>Tue, 06 Oct 2026 16:10:00 GMT</pubDate>
      <dc:creator>Google for Developers · YouTube</dc:creator>
      <category>News · Watch</category>
      <description>Google for Developers · YouTube — Sebastian Russo, Ivan Llanos, and Sahil Dua from Google DeepMind introduce EmbeddingGemma 2, a lightweight open model that maps text, images, video, and audio into a single unified embedding space. At just 740 million pa…</description>
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    <item>
      <title>EmbeddingGemma 2: an open, lightweight multimodal embedding model</title>
      <link>https://blog.google/innovation-and-ai/technology/developers-tools/embeddinggemma-2/</link>
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      <pubDate>Tue, 06 Oct 2026 16:00:00 GMT</pubDate>
      <dc:creator>Google · Developers</dc:creator>
      <category>News · News</category>
      <description>Google · Developers — Introducing EmbeddingGemma 2, an open multimodal embedding model optimized for privacy-first use cases</description>
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      <title>EmbeddingGemma 2: The Developer Guide</title>
      <link>https://developers.googleblog.com/embeddinggemma-2-the-developer-guide/</link>
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      <pubDate>Tue, 06 Oct 2026 00:00:00 GMT</pubDate>
      <dc:creator>Google for Developers</dc:creator>
      <category>Analysis · Technical Guides</category>
      <description>Google for Developers — EmbeddingGemma 2 is a compact, open-source multimodal embedding model that maps text, code, images, video, and audio into a unified 768-dimensional space. Developers can use the sentence-transformers library to selective…</description>
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    <item>
      <title>Bring multimodal semantic search to the edge with EmbeddingGemma 2</title>
      <link>https://developers.googleblog.com/google-ai-edge-with-embeddinggemma-2/</link>
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      <pubDate>Tue, 06 Oct 2026 00:00:00 GMT</pubDate>
      <dc:creator>Google for Developers</dc:creator>
      <category>News · News</category>
      <description>Google for Developers — EmbeddingGemma 2 is a new 740M open-weight multimodal model that maps text, images, video, and audio into a unified vector space for privacy-first, on-device retrieval. Developers can easily integrate these capabilities…</description>
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    <item>
      <title>How Small Can Google's New EmbeddingGemma 2 Get?</title>
      <link>https://qdrant.tech/blog/embeddinggemma-2/</link>
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      <pubDate>Tue, 06 Oct 2026 00:00:00 GMT</pubDate>
      <dc:creator>Qdrant</dc:creator>
      <category>Analysis · Technical Guides</category>
      <description>Qdrant — Google&amp;rsquo;s new EmbeddingGemma 2 can run on a phone. The model travels light. The vectors need their own suitcase. At 10 million documents, full-size float32 vectors alone take 30.7 GB of RAM. We got early access ahea…</description>
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