⚡ Vector Quantization: SQ8 & 1-Bit Binary (32x RAM Compression)

FlashQuantizer is FLASH's superpower vector compression engine designed to solve the RAM Explosion Problem in high-scale AI vector databases.

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💥 The Big Problem: Vector Memory Inflation

Storing high-dimensional AI vectors (e.g. OpenAI 1536-dim or BERT 768-dim) as raw Float32 arrays consumes massive amounts of RAM:

Vector Count	Dimensions	Raw Float32 RAM	With Flash SQ8	With Flash 1-Bit Binary
10,000	1536	61.4 MB	15.5 MB	1.9 MB
100,000	1536	614.4 MB	155.2 MB	19.2 MB
1,000,000	1536	6.14 GB	1.55 GB (75% Savings)	192 MB (32x Savings)
10,000,000	1536	61.44 GB	15.5 GB	1.92 GB

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🚀 1. Scalar Quantization (SQ8)

Scalar Quantization (SQ8) compresses 32-bit floating point values (4 bytes) into 8-bit integers (1 byte), retaining > 99% accuracy while reducing RAM footprint by 75%.

import { FlashQuantizer } from 'flash-zk';

const floatVector = new Float32Array([0.85, -0.42, 0.91, -0.15, 0.05, 0.77, -0.88, 0.33]);

// 1. Quantize Float32 to SQ8 Uint8Array
const sq8 = FlashQuantizer.quantizeSQ8(floatVector);
console.log(sq8.data); // Uint8Array [ 255, 68, 264, ... ]

// 2. Fast Asymmetric Cosine Similarity directly without full dequantization
const queryVec = new Float32Array([0.80, -0.40, 0.88, -0.10, 0.00, 0.70, -0.85, 0.30]);
const score = FlashQuantizer.asymmetricCosineSQ8(queryVec, sq8.data, sq8.min, sq8.scale);
console.log(`Cosine Similarity: ${score.toFixed(4)}`);

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⚡ 2. 1-Bit Binary Quantization (32x RAM Savings)

Packs vector sign bits directly into unsigned 32-bit integers (Uint32Array). 1536 dimensions are packed into just 48 32-bit integers (192 bytes total)!

Distances are computed in single-cycle CPU instructions using bitwise XOR and POPCNT:

import { FlashQuantizer } from 'flash-zk';

// 1. Quantize vectors to 1-Bit Binary Bitmasks
const binA = FlashQuantizer.quantizeBinary(vectorA);
const binB = FlashQuantizer.quantizeBinary(vectorB);

// 2. Single-Cycle Bitwise Hamming Distance
const hammingDist = FlashQuantizer.hammingDistance(binA.data, binB.data);

// 3. Approximate Angular Cosine Similarity
const cosineSim = FlashQuantizer.cosineApproxFromBinary(binA.data, binB.data, 1536);
console.log(`Binary Cosine Match: ${cosineSim.toFixed(4)}`);

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🛠️ Using Quantization in FlashVectorIndex & FlashAIDatabase

You can enable quantization seamlessly with zero configuration:

import { FlashAIDatabase } from 'flash-zk';

const aiDb = new FlashAIDatabase({
  name: 'enterprise_rag_vault',
  dimensions: 1536,
  quantization: 'sq8', // 'none' | 'sq8' | 'binary1bit'
});

// Storing vectors automatically uses SQ8 compression
await aiDb.remember('High-scale sovereign AI memory engine', { tag: 'ai' });

// Check live memory savings analytics
console.log(aiDb.getMemoryStats());