DigiNews

Summary

TurboQuant explains compressing high-dimensional AI vectors to 2–4 bits per coordinate with near-optimal distortion using a random rotation and a universal codebook. It introduces MSE-based quantization, inner-product bias, and antidotes like QJL and TurboQuant-prod to achieve unbiased inner-product estimates while maintaining compression efficiency, with interactive demos and theoretical bounds relative to Shannon's limit.