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TurboQuant: Redefining AI efficiency with extreme compression

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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 Vectors are the fundamental way AI models understand and...
The article presents TurboQuant, a new AI compression algorithm designed to address the memory overhead issue in vector quantization. By utilizing PolarQuant for high-quality compression and QJL for eliminating hidden errors, TurboQuant reduces the size of key-value pairs while preserving AI model performance. The researchers tested the algorithms on various tasks, demonstrating that TurboQuant achieves optimal scoring performance with minimal key-value memory footprint. The implications of this...