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TurboQuant: Redefining AI efficiency with extreme compression

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We strive to create an environment conducive to many different types of research across many different time scales and levels of risk. Our researchers drive advancements in computer science through both fundamental and applied research. We regularly open-source projects with the broader research community and apply our developments to Google products. Publishing our work allows us to share ideas and work collaboratively to advance the field of computer science. We make products, tools, and datasets available to everyone with the goal of building a more collaborative ecosystem. Supporting the next generation of researchers through a wide range of programming. Participating in the academic research community through meaningful engagement with university faculty. Connecting with the broader research community through events is essential for creating progress in every aspect of our work. March 24, 2026 Amir Zandieh, Research Scientist, and Vahab Mirrokni, VP and Google Fellow, Google Resea

TurboQuant: Redefining AI efficiency with extreme compression Skip to main content TurboQuant: Redefining AI efficiency with extreme compression March 24, 2026 Amir Zandieh, Research Scientist, and Vahab Mirrokni, VP and Google Fellow, Google Research We introduce a set of advanced theoretically grounded quantization algorithms that enable massive compression for large language models and vector search engines. Quick links TurboQuant Quantized Johnson-Lindenstrauss PolarQuant Share Copy link × Vectors are the fundamental way AI models understand and process information. Small vectors describe

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