Overview
Binary quantization converts high-dimensional floating-point vectors into binary representations (0s and 1s), enabling dramatic storage and computational savings for vector search applications while maintaining acceptable accuracy.
How It Works
Quantization Process
- Threshold Selection: Choose value to split dimensions (often 0 or median)
- Bit Assignment: Values above threshold = 1, below = 0
- Packing: Pack 8 bits into bytes for efficient storage
- Indexing: Build search index on binary vectors
Example
Original vector: [0.5, -0.2, 0.8, -0.1, 0.3]
After quantization: [1, 0, 1, 0, 1]
Packed: 10101 (binary)
Storage Benefits
Compression Ratios
- Float32: 1024 dims × 4 bytes = 4,096 bytes per vector
- Binary: 1024 dims ÷ 8 = 128 bytes per vector
- Compression: 32x reduction
Scale Impact
- 1M vectors: 4GB → 128MB
- 100M vectors: 400GB → 12.5GB
- 1B vectors: 4TB → 125GB
Performance Characteristics
Speed
- Hamming Distance: Ultra-fast bitwise operations
- CPU Efficient: No floating-point arithmetic
- SIMD Friendly: Parallel bit operations
- Cache Efficient: More vectors fit in cache
Accuracy
- Typical Recall: 90-95% at k=10
- Use Case Dependent: Varies by data distribution
- Refinement Possible: Two-stage retrieval
Implementation Approaches
Statistical Binary Quantization
Available in pgvectorscale:
- Optimizes threshold per dimension
- Better accuracy than simple thresholding
- Minimal overhead
Sign-Based Quantization
Simplest approach:
- Positive values → 1
- Negative values → 0
- Fast but less accurate
Learned Quantization
- Train quantizer on representative data
- Optimize for specific similarity metrics
- Best accuracy, more complex
Applications in 2026
Local-First RAG
February 2026 implementations:
- SQLite with binary embeddings
- Hundreds of thousands of documents
- Commodity hardware
- No external dependencies
Edge AI
