Qdrant 1.5-bit Quantization

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.

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.

Vector compression technique reducing precision of each vector component from 32-bit floats to 8-bit integers, achieving 4x memory reduction with minimal accuracy loss for vector search.

Activation-aware Weight Quantization method that preserves model accuracy at 4-bit quantization by identifying and skipping important weights. Maintains 99%+ of original performance with moderate inference speed improvements.