Scalar Quantization

Overview

Scalar Quantization (SQ) is a simpler compression technique than Product Quantization that reduces the precision of each vector component, typically from 32-bit floating point to 8-bit integers, achieving 4x memory reduction.

How Scalar Quantization Works

Quantization Process

1. Find Range: Determine min and max values for each dimension
2. Map: Map the continuous range to discrete integer values (e.g., 0-255 for 8-bit)
3. Encode: Convert each float component to its quantized integer
4. Store: Store compact 8-bit representations

Search Process

1. Quantize query vector using same mapping
2. Compute approximate distances using quantized vectors
3. Optionally rerank top candidates using original vectors

Memory Reduction

• 32-bit float → 8-bit int: 4x compression
• 32-bit float → 4-bit int: 8x compression
• Example: 768-dim vector: 3,072 bytes → 768 bytes (8-bit) or 384 bytes (4-bit)

Variants

Asymmetric Scalar Quantization

• Database vectors: quantized
• Query vectors: kept in full precision
• Better accuracy than symmetric quantization

Per-Dimension vs Global

• Per-dimension: Different min/max for each dimension
• Global: Single min/max across all dimensions

Trade-offs

Advantages:

• Simple to implement
• Minimal accuracy loss (1-2% typical)
• Fast compression and decompression
• Works well with modern SIMD instructions

Disadvantages:

• Less compression than Product Quantization
• Still requires significant memory for large datasets

Comparison with Other Methods

vs Product Quantization:

• Less compression but better accuracy
• Simpler and faster
• No training required

vs Binary Quantization:

• Better accuracy but less compression

Use Cases

• When 4x compression is sufficient
• Applications requiring high accuracy
• Real-time search scenarios
• When simplicity is valued

Pricing

Implemented in most vector databases (Qdrant, Milvus, Weaviate, etc.)