Intermediate LevelAI, LLMs, Embeddings & MCP
What is Vector Quantization (Binary, Scalar INT8) and how does it compress vector database RAM?
❓ Interview Prompt: Explain Scalar Quantization (SQ8), Binary Quantization (BQ), and memory reduction in large vector databases.
Storing millions of 1536-dimensional FP32 vectors in vector database RAM requires massive server hardware costs. Vector Quantization compresses vector memory size:
1. Scalar Quantization (SQ8):
Converts 32-bit floats (FP32) to 8-bit integers (INT8).
•
Compression: 4x reduction in RAM (e.g. 100GB RAM reduced to 25GB) with $>99\%$ search recall retention!
2. Binary Quantization (BQ):
Converts positive floats to 1 and negative floats to 0 (1-bit per dimension).
•
Compression: 32x reduction in RAM! Allows computing Hamming distance using hardware bitwise XOR instructions.
Code Implementation Example
typescript
// Binary Quantization function: FP32 vector -> 1-bit array
function binaryQuantize(floatVector: number[]): Uint8Array {
const binary = new Uint8Array(Math.ceil(floatVector.length / 8));
floatVector.forEach((val, i) => {
if (val > 0) {
const byteIndex = Math.floor(i / 8);
const bitIndex = i % 8;
binary[byteIndex] |= (1 << bitIndex);
}
});
return binary;
}💡Binary quantization converting FP32 float vector into packed bit array.
🎯 Interview Key Takeaway
Scalar INT8 and Binary quantization compress vector database RAM usage by 75% to 95% with high recall retention.
#Vector Quantization#SQ8#Binary Quantization#Vector DB#Intermediate
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