
TurboQuant: Google’s New Algorithm Promises Extreme AI Compression Without Performance Loss
Google DeepMind’s TurboQuant combines Quantized Johnson–Lindenstrauss with a new PolarQuant technique to compress high‑dimensional vectors more efficiently. The approach removes extra normalization and constant storage overhead, potentially reducing memory costs for large language models and vector search systems while maintaining performance.




