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adding slower indexing to PQ on when to use section

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Sabrina Aquino
2024-09-26 12:50:18 -03:00
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parent 607f9869e1
commit 1e8fa44a73
1 changed files with 2 additions and 2 deletions
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qdrant-landing/content/articles/what-is-quantization.md
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@@ -515,7 +515,7 @@ Here are some final thoughts to help you choose the right quantization method fo
|--------------------------|-------------------------------------------------------------|--------------------------------------------------------------------------------------------|
| **Binary Quantization** | • **Fastest method and most memory-efficient**<br>• Up to **40x** faster search and **32x** reduced memory footprint | • Use with tested models like OpenAI's `text-embedding-ada-002` and Cohere's `embed-english-v2.0`<br>• When speed and memory efficiency are critical |
| **Scalar Quantization** | • **Minimal loss of accuracy**<br>• Up to **4x** reduced memory footprint | • Safe default choice for most applications.<br>• Offers a good balance between accuracy, speed, and compression. |
| **Product Quantization** | • **Highest compression ratio**<br>• Up to **64x** reduced memory footprint | • When minimizing memory usage is the top priority<br>• Acceptable if some loss of accuracy is tolerable |
| **Product Quantization** | • **Highest compression ratio**<br>• Up to **64x** reduced memory footprint | • When minimizing memory usage is the top priority<br>• Acceptable if some loss of accuracy and slower indexing is tolerable |
### Learn More
@@ -528,4 +528,4 @@ Learn more about optimizing real-time precision with oversampling in Binary Quan
</iframe>
</div>
Stay up-to-date on the latest in [vector search](/advanced-search/) and quantization, share your projects, ask questions, [join our vector search community](https://discord.com/invite/qdrant)!
Stay up-to-date on the latest in [vector search](/advanced-search/) and quantization, share your projects, ask questions, [join our vector search community](https://discord.com/invite/qdrant)!