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Abdon Pijpelink
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qdrant-landing/content/blog/qdrant-1.18.x.md
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@@ -38,8 +38,6 @@ Version 1.18 introduces support for [TurboQuant](/documentation/manage-data/quan
Qdrant's implementation of TurboQuant extends the original algorithm to close the gap between the algorithm's theoretical assumptions and real-world embeddings. A length renormalization step corrects a recall-degrading bias caused by quantization error, an idea borrowed from [RaBitQ](https://arxiv.org/abs/2405.12497). A per-coordinate calibration pre-pass fits the data to precomputed codebooks, aiming to recover accuracy lost to distribution mismatch. Cosine, dot product, and L2 are all supported as first-class distance metrics. And finally, we implemented highly optimized SIMD acceleration for TurboQuant to achieve maximum performance. Qdrant's implementation of TurboQuant extends the original algorithm to close the gap between the algorithm's theoretical assumptions and real-world embeddings. A length renormalization step corrects a recall-degrading bias caused by quantization error, an idea borrowed from [RaBitQ](https://arxiv.org/abs/2405.12497). A per-coordinate calibration pre-pass fits the data to precomputed codebooks, aiming to recover accuracy lost to distribution mismatch. Cosine, dot product, and L2 are all supported as first-class distance metrics. And finally, we implemented highly optimized SIMD acceleration for TurboQuant to achieve maximum performance.
To learn more about Qdrant's TurboQuant implementation, refer to [our article](/articles/turboquant-quantization/).
### How TurboQuant Compares ### How TurboQuant Compares
#### TurboQuant vs Scalar Quantization #### TurboQuant vs Scalar Quantization
@@ -68,7 +66,7 @@ The following table shows recall@10 for 1-bit TurboQuant (TQ1) compared to uncom
Compared to 1-bit binary quantization, 1-bit TurboQuant offers better recall at equivalent storage budgets, albeit at a lower speed. Similar trends are observed for 1.5-bit and 2-bit configurations. Compared to 1-bit binary quantization, 1-bit TurboQuant offers better recall at equivalent storage budgets, albeit at a lower speed. Similar trends are observed for 1.5-bit and 2-bit configurations.
Detailed numbers including throughput and indexing times are [in our article](/articles/turboquant-quantization/). We will soon publish an article with detailed numbers, including throughput and indexing times.
### Get Started with TurboQuant ### Get Started with TurboQuant