Google Research· Research

TurboQuant: Redefining AI efficiency with extreme compression

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 simple attributes, such as a point in a graph, while “high-dimensional” vectors capture complex information such as the features of an image, the meaning of a word, or the properties of a dataset. High-dimensional vectors are incredibly powerful, but they also consume vast amounts of memory, leading to bottlenecks in the key-value cache , a high-speed "digital cheat sheet" that stores frequently used information under simple labels so a computer can retrieve it instantly without having to search through a slow, massive database. Vector quantization is a powerful, classical data compression technique that reduces the size of high-dimensional vectors. …

Announced

24 Mar 2026 19:54 UTC