🤖 AI EngineeringID:
scalar-quantization-sq8Scalar Quantization (SQ8/SQ4)
SQ8/SQ4 标量量化
🎯Core Definition
Scalar Quantization (SQ) is a vector compression technique that independently maps each component of a high-dimensional vector to discrete fixed-point integers; SQ8 linearly projects 32-bit floating point (FP32) values across dimension ranges into 8-bit unsigned integers (uint8) via , while SQ4 compresses down to 4-bit nibbles, achieving 4x to 8x memory reduction.
💡Use Cases
Building massive vector indices (tens of millions to billions of vectors) under memory constraints in vector databases, serving as raw vector caching or coarse pruning for HNSW.
⚡Key Problems Solved
100M 1536-dimensional FP32 vectors require 614GB RAM for raw storage, exceeding single-node capacity; SQ8 compresses this to ~153GB and leverages AVX-512 / VNNI integer SIMD instructions for blazing fast distance calculation while maintaining 98%+ Recall@10.
🎯5 High-Frequency Exam Points
1
Derive the SQ8 linear quantization and de-quantization formulas and source of precision loss?
2
What is the Recall difference between Global SQ and Per-Dimension SQ under heavy-tailed distribution?
3
How do modern CPU AVX-512 VNNI instructions accelerate int8 dot product operations?
4
Compare SQ8 vs PQ in terms of memory compression ratio, index construction time, and reconstruction error?
5
Why does SQ8 recall rate improve after applying outlier clipping / percentile thresholding?
📖 In-depth Guide:📄 vector-databases-and-hnsw →
Updated 2026-08-14🎯
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