Technical Comparison

Vector Quantization vs Vector Reduction

Vector quantization and vector reduction both shrink a vector index, but in fundamentally different ways. Quantization makes each vector smaller by lowering its precision, for example storing each number in fewer bits, which saves space at the cost of some accuracy. Vector reduction makes vectors fewer by eliminating semantic redundancy, keeping each remaining vector at full precision. Quantization changes the size of every vector; reduction changes how many vectors exist. The two operate on different axes and address different causes of index bloat.

The two ways everyone shrinks a vector index

High-dimensional embeddings are expensive to store and search. A single 1536-dimension float32 vector uses about 6KB, so a hundred million vectors is hundreds of gigabytes before any index overhead. Almost every technique for managing this cost works in one of two ways: making each vector use fewer bits, or making each vector use fewer dimensions. Both shrink the size of an individual vector.

Vector quantization, in detail

Quantization reduces the number of bits used to represent each value in a vector. The main forms are:

Scalar quantization converts each dimension from a 32-bit float to a lower-precision integer, commonly int8. This cuts storage roughly fourfold with modest accuracy loss, and works well for most general-purpose embedding models out of the box.

Product quantization splits each vector into subvectors and replaces each subvector with the nearest entry in a learned codebook. It can achieve higher compression than scalar quantization, but results are more dataset-dependent.

Binary quantization reduces each dimension to a single bit, keeping only its sign. This is extreme compression, on the order of thirty-twofold, and works well for some embedding types, particularly those from contrastive training, while degrading badly on others. It suits high-throughput, cost-sensitive applications where some precision loss is acceptable.

RaBitQ and Better Binary Quantization (BBQ) are modern refinements of binary quantization. They apply a random rotation before binarizing and add a correction step to recover accuracy, with theoretical error bounds. Elastic's BBQ is built on this family and is one of the most widely deployed quantization methods in production.

TurboQuant, introduced in 2025 by researchers at Google Research and NYU and presented at ICLR 2026, is among the most advanced quantizers available. It randomly rotates each vector so that an optimal quantizer can be applied to each coordinate independently, achieving near-optimal distortion provably within a small constant factor of the theoretical limit, and without any per-dataset training. Its significance goes beyond performance: by reaching near the theoretical floor, TurboQuant demonstrates that quantization as a category is approaching its mathematical ceiling. There is only so far you can compress an individual vector before accuracy suffers, and TurboQuant is already close to that limit.

A related but distinct approach: Matryoshka dimensionality reduction

Matryoshka Representation Learning (MRL) shrinks vectors along a different dimension: it reduces the number of values per vector rather than the bits per value. Models trained with MRL front-load the most important information into the earliest dimensions, so a vector can be truncated to a fraction of its length with limited accuracy loss. It is not quantization, but it shares the same fundamental property: it makes each individual vector smaller.

What all of these have in common

Scalar, product, and binary quantization, RaBitQ, BBQ, TurboQuant, and Matryoshka all make each vector smaller, whether by using fewer bits or fewer dimensions, and all accept some loss of accuracy in exchange for space. None of them changes the number of vectors in the index. If your index contains many near-duplicate or redundant vectors, every one of these techniques faithfully shrinks all of them, redundancy included.

Vector reduction: a third axis

Vector reduction takes a different approach. Instead of making each vector smaller, it makes the set of vectors smaller by eliminating semantic redundancy. Many vector indexes contain large numbers of near-duplicate vectors representing overlapping meaning. Vector reduction removes that redundancy, collapsing semantically redundant vectors into single representations, while keeping each remaining vector at full precision and full dimensionality. Green Vectors performs this reduction at ingestion through patent-pending semantic transformation, identifying redundant signal before it is ever stored.

Taxonomy

Three ways to shrink a vector index

Fewer bits per value

Quantization

Scalar, product, binary, RaBitQ, BBQ. Each vector becomes smaller; accuracy drops.

Fewer dimensions per vector

Matryoshka (MRL)

Truncates lower-importance dimensions. Each vector becomes smaller; accuracy drops.

Fewer vectors

Vector Reduction (Green Vectors)

Eliminates semantic redundancy at ingestion. Each remaining vector keeps full precision.

The core difference

	Quantization	Matryoshka (MRL)	Vector Reduction (Green Vectors)
What it changes	Bits per value	Dimensions per vector	Number of vectors
Each vector	Smaller, lower precision	Smaller, fewer dimensions	Full precision, unchanged
Vector count	Unchanged	Unchanged	Reduced
Accuracy effect	Some loss	Some loss	Preserved or improved
Addresses redundancy	No	No	Yes

Why the distinction matters for accuracy

Quantization and dimensionality reduction trade accuracy for size because they discard information from every vector. Vector reduction does not discard information from the vectors it keeps; it removes vectors that were redundant in the first place. Eliminating redundant vectors can actually improve accuracy, because a search space crowded with near-duplicates is noisier and harder to rank than a clean one. This is why reduction can lower storage and raise accuracy at the same time, which compression cannot do.

Benchmark: Green Vectors versus Elastic BBQ

Morphos AI benchmarked Green Vectors against Elastic BBQ on the complete Project Gutenberg dataset, measuring three configurations. Green Vectors alone achieved 1.5GB of storage at a .9658 relevancy score. BBQ alone required 175GB at a .4576 relevancy score. That is roughly 116 times more storage-efficient and more than twice as accurate. A third configuration combined the two: Green Vectors with BBQ held relevancy at .9653 but used 2.6GB, more storage than Green Vectors alone, because on an already-minimal index the overhead of BBQ's rotation and correction data costs more than it saves. In other words, Green Vectors alone was the single best configuration tested.

Storage on Project Gutenberg

Lower is better

BBQ alone175GB · relevancy .4576

Green Vectors + BBQ2.6GB · relevancy .9653

Green Vectors alone1.5GB · relevancy .9658

Green Vectors alone outperforms both BBQ alone and the combined configuration.

Can you combine reduction and quantization?

Conceptually, yes, because they operate on different axes: you can reduce the number of vectors and then quantize the ones that remain. In practice, the Green Vectors benchmark shows that once the index is reduced, there is often little left for quantization to improve, and its overhead can outweigh its benefit. Quantization remains available for pipelines that already use it, but with Green Vectors it becomes optional rather than necessary, because reduction delivers the efficiency that quantization aims for without the accuracy tradeoff.

Which approach should you use?

If your goal is to fit more vectors into the same memory and you can accept some accuracy loss, quantization is a reasonable tool. If your index is bloated with redundant vectors, the more effective move is to reduce their number first. Reduction addresses the cause of index bloat rather than compressing the symptom, and it preserves accuracy while doing so. For most production workloads, reducing redundancy at ingestion makes a separate quantization step optional.

FAQ

Frequently asked questions.

Quantization makes each vector smaller by lowering its precision, with some accuracy loss. Vector reduction makes vectors fewer by eliminating semantic redundancy, keeping each remaining vector at full precision. Quantization changes vector size; reduction changes vector count.

They address different problems. Quantization fits more vectors into memory at some accuracy cost. Reduction removes redundant vectors entirely, lowering storage while preserving or improving accuracy. In the Elastic BBQ benchmark, Green Vectors alone was 116 times more storage-efficient and more than twice as accurate as BBQ alone.

Conceptually yes, since they work on different axes. But once an index is reduced, quantization often adds little and its overhead can outweigh its savings, as the Green Vectors benchmark showed. With reduction, quantization becomes optional.

No. Quantization discards information from every vector. Reduction removes vectors that were redundant, keeping the rest at full precision, which can improve accuracy by reducing search-space noise.

No. Quantization lowers the bits per value. Matryoshka lowers the number of dimensions per vector. Both make each vector smaller; neither reduces the number of vectors.

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