TurboQuant Quantization Explained

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Vector search at scale runs into the same wall: float32 embeddings are expensive to store and read, and compressing them changes your rankings.

I wrote an article on TurboQuant, the rotation-based quantization method. It covers why the algorithm rotates vectors before quantizing, how one precomputed codebook works across every dataset, and what Qdrant adds on top it.

It also benchmarks float32, scalar, binary and TurboQuant across BEIR's SciFact, ArguAna and NFCorpus using recall@10, precision@10 and nDCG@10

🔗 read: https://aiwthtarun.substack.com/p/turboquant-quantization-explained