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Evaluating Quantization Strategies for Scalable Vector Search

Think about on the lookout for related issues primarily based on deeper insights as an alternative of simply key phrases. That is what vector databases and similarity searches assist with. Vector databases allow vector similarity search. It makes use of the gap between vectors to search out information factors in search queries.

Nevertheless, similarity search in high-dimensional information may be gradual and resource-intensive. Enter Quantization strategies! They play an necessary function in optimizing information storage and accelerating information retrieval in vector databases.

This text explores varied quantization strategies, their varieties, and real-world use circumstances.

What’s Quantization and How Does it Work?

Quantization is the method of changing steady information into discrete information factors. Particularly whenever you’re coping with billion-scale parameters, quantization is important for managing and processing. In vector databases, quantization transforms high-dimensional information into compressed house whereas preserving necessary options and vector distances.

Quantization considerably reduces reminiscence bottlenecks and improves storage effectivity.

The method of quantization consists of three key processes:

1. Compressing Excessive-Dimensional Vectors

In quantization, we use strategies like codebook technology, function engineering, and encoding. These strategies compress high-dimensional vector embeddings right into a low-dimensional subspace. In different phrases, the vector is cut up into quite a few subvectors. Vector embeddings are numerical representations of audio, photographs, movies, textual content, or sign information, enabling simpler processing.

2. Mapping to Discrete Values

This step entails mapping the low-dimensional subvectors to discrete values. The mapping additional reduces the variety of bits of every subvector.

3. Compressed Vector Storage

Lastly, the mapped discrete values of the subvectors are positioned within the database for the unique vector. Compressed information representing the identical data in fewer bits optimizes its storage.

Advantages of Quantization for Vector Databases

Quantization presents a variety of advantages, leading to improved computation and decreased reminiscence footprint.

1. Environment friendly Scalable Vector Search

Quantization optimizes the vector search by decreasing the comparability computation value. Subsequently, vector search requires fewer sources, enhancing its total effectivity.

2. Reminiscence Optimization

Quantized vectors permits you to retailer extra information inside the identical house. Moreover, information indexing and search are additionally optimized.

3. Pace

With environment friendly storage and retrieval comes quicker computation. Diminished dimensions permit quicker processing, together with information manipulation, querying, and predictions.

Some well-liked vector databases like Qdrant, Pinecone, and Milvus provide varied quantization strategies with totally different use circumstances.

Use Circumstances

The flexibility of quantization to scale back information dimension whereas preserving vital data makes it a useful asset.

Let’s dive deeper into a couple of of its functions.

1. Picture and Video processing

Photos and video information have a broader vary of parameters, considerably growing computational complexity and reminiscence footprint. Quantization compresses the information with out shedding necessary particulars, enabling environment friendly storage and processing. This speeds searches for photographs and movies.

2. Machine Studying Mannequin Compression

Coaching AI fashions on massive information units is an intensive activity. Quantization helps by decreasing mannequin dimension and complexity with out compromising its effectivity.

3. Sign Processing

Sign information represents steady information factors like GPS or surveillance footage. Quantization maps information into discrete values, permitting quicker storage and evaluation. Moreover, environment friendly storage and evaluation pace up search operations, enabling quicker sign comparability.

Totally different Quantization Strategies

Whereas quantization permits seamless dealing with of billion-scale parameters, it dangers irreversible data loss. Nevertheless, discovering the precise stability between acceptable data loss and compression improves effectivity.

Every quantization approach comes with professionals and cons. Earlier than you select, it’s best to perceive compression necessities, in addition to the strengths and limitations of every approach.

1. Binary Quantization

Binary quantization is a technique that converts all vector embeddings into 0 or 1. If a price is bigger than 0, it’s mapped to 1, in any other case it’s marked as 0. Subsequently, it converts high-dimensional information into considerably lower-dimensional permitting quicker similarity search.


The Components is:

Binary quantization method. Picture by writer.

Right here’s an instance of how binary quantization works on a vector.

BQ Illustration

Graphical illustration of binary quantization. Picture by writer.


  • Quickest search, surpassing each scalar and product quantization strategies.
  • Reduces reminiscence footprint by an element of 32.


  • Increased ratio of knowledge loss.
  • Vector parts require a imply roughly equal to zero.
  • Poor efficiency on low-dimensional information attributable to increased data loss.
  • Rescoring is required for the most effective outcomes.

Vector databases like Qdrant and Weaviate provide binary quantization.

2. Scalar Quantization

Scalar quantization converts floating level or decimal numbers into integers. This begins with figuring out a minimal and most worth for every dimension. The recognized vary is then divided into a number of bins. Lastly, every worth in every dimension is assigned to a bin.

The extent of precision or element in quantized vectors relies upon upon the variety of bins. Extra bins lead to increased accuracy by capturing finer particulars. Subsequently, the accuracy of vector search additionally relies upon upon the variety of bins.


The method is:

Scalar quantization method. Picture by writer.

Right here’s an instance of how scalar quantization works on a vector.

SQ Illustration

Graphical illustration of scalar quantization. Picture by writer.


  • Vital reminiscence optimization.
  • Small data loss.
  • Partially reversible course of.
  • Quick compression.
  • Environment friendly scalable search attributable to small data loss.


  • A slight lower in search high quality.
  • Low-dimensional vectors are extra inclined to data loss as every information level carries necessary data.

Vector databases equivalent to Qdrant and Milvus provide scalar quantization.

3. Product Quantization

Product quantization divides the vectors into subvectors. For every part, the middle factors, or centroids, are calculated utilizing clustering algorithms. Their closest centroids then symbolize each subvector.

Similarity search in product quantization works by dividing the search vector into the identical variety of subvectors. Then, a listing of comparable outcomes is created in ascending order of distance from every subvector’s centroid to every question subvector. For the reason that vector search course of compares the gap from question subvectors to the centroids of the quantized vector, the search outcomes are much less correct. Nevertheless, product quantization hastens the similarity search course of and better accuracy may be achieved by growing the variety of subvectors.


Discovering centroids is an iterative course of. It makes use of the recalculation of Euclidean distance between every information level to its centroid till convergence. The method of Euclidean distance in n-dimensional house is:

Product quantization method. Picture by writer.

Right here’s an instance of how product quantization works on a vector.

PQ Illustration

Graphical illustration of product quantization. Picture by writer.


  • Highest compression ratio.
  • Higher storage effectivity than different strategies.


  • Not appropriate for low-dimensional vectors.
  • Useful resource-intensive compression.

Vector databases like Qdrant and Weaviate provide product quantization.

Selecting the Proper Quantization Technique

Every quantization methodology has its professionals and cons. Selecting the best methodology relies upon upon elements which embrace however aren’t restricted to:

  • Knowledge dimension
  • Compression-accuracy tradeoff
  • Effectivity necessities
  • Useful resource constraints.

Take into account the comparability chart under to know higher which quantization approach fits your use case. This chart highlights accuracy, pace, and compression elements for every quantization methodology.

Picture by Qdrant

From storage optimization to quicker search, quantization mitigates the challenges of storing billion-scale parameters. Nevertheless, understanding necessities and tradeoffs beforehand is essential for profitable implementation.

For extra data on the newest tendencies and know-how, go to Unite AI.

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