TurboQuant uses vector quantization to dramatically compress AI models and solve memory bottleneck problems in key-value caches

High-dimensional vectors enable AI models to process complex information like images and language, but consume massive amounts of memory

Vector quantization is a classical data compression technique that reduces the size of high-dimensional vectors to improve efficiency

The optimization addresses two critical issues: it speeds up vector search technology used in large-scale AI systems and search engines, while also relieving key-value cache bottlenecks that slow down performance