Quantization

A collection of resources, libraries, and databases focused on handling and searching multidimensional vector data, directly relevant for storing and querying vector embeddings in AI-powered applications.

Compression technique that converts full-precision vectors to binary representations, achieving 32x storage reduction while maintaining 90-95% recall for efficient large-scale vector search.

Middle-ground quantization introduced in Qdrant v1.15.0 that provides better precision than binary quantization while being more aggressive than scalar quantization.

Advanced quantization technique that applies per-vector normalization and scalar quantization, adapting the quantization bounds individually for each vector. Achieves four-fold reduction in vector size while maintaining search accuracy with 26-37% overall memory footprint reduction.

Extreme vector compression technique converting each dimension to a single bit (0 or 1), achieving 32x memory reduction and enabling ultra-fast Hamming distance calculations with acceptable accuracy trade-offs.

Vector compression technique that splits high-dimensional vectors into subvectors and quantizes each independently, achieving significant memory reduction while enabling approximate similarity search.