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Sabrina
2024-09-13 13:28:38 -03:00
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qdrant-landing/content/articles/what-is-quantization.md
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@@ -7,7 +7,6 @@ description: Learn what vector quantization is and explore how methods like Scal
preview_dir: /articles_data/what-is-vector-quantization/preview
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date: 2024-09-09T09:29:33-03:00
author: Sabrina Aquino
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## 1. What is Scalar Quantization?
![](/articles_data/what-is-vector-quantization/astronaut-mars.jpg)
In Qdrant, each vector is represented by a float32 value, which uses **4 bytes** of memory. When using [Scalar Quantization](https://qdrant.tech/documentation/guides/quantization/#scalar-quantization), we are mapping our vectors to a range that the smaller int8 type can represent. An int8 can store 256 values (from -128 to 127) which uses only **1 byte.** This typically results in a **75% reduction** in memory size.
For example, if our data lies in the identified range of -1.0 to 1.0, Scalar Quantization will transform these values to a range that int8 can represent, that is, within -128 to 127. So, the system **maps** the float32 values into this range.
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# 2. What is Product Quantization?
![](/articles_data/what-is-vector-quantization/astronaut-centroids.jpg)
[Product Quantization](https://qdrant.tech/documentation/guides/quantization/#product-quantization) is a method used to compress high-dimensional vectors by representing them with a smaller set of representative points.
The process begins by setting up a **codebook,** which represents regions in the data space where common patterns occur.
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# 3. What is Binary Quantization?
![](/articles_data/what-is-vector-quantization/astronaut-white-surreal.jpg)
[Binary Quantization](https://qdrant.tech/documentation/guides/quantization/#binary-quantization) is an excellent option if you're looking to **reduce memory** usage while also achieving a significant **boost in speed**. It works by converting high-dimensional vectors into simple binary (0 or 1) representations.
* Values greater than zero are converted to 1
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# Wrapping Up
![](/articles_data/what-is-vector-quantization/astronaut-running.jpg)
If you want to learn more about improving accuracy, memory efficiency, and speed when using quantization in Qdrant, we have a dedicated [Quantization tips](https://qdrant.tech/documentation/guides/quantization/#quantization-tips) section in our docs that explains all the quantization tips you can use to enhance your results.