Interview with RaBitQ Authors: The TurboQuant Dispute and Why the Storage Selloff Was a False Alarm

What this post added

This post provides a technical deep dive into vector quantization, contrasting the RaBitQ and TurboQuant methods. It explains the core concepts of vector quantization, its application in vector search and KV-cache compression, and the specific technical innovations of RaBitQ (random rotation, optimal quantization estimation). It also critiques TurboQuant's methodology, including benchmarking fairness and attribution of prior work. The post highlights the practical implications for Milvus, including the adoption of IVF_RABITQ and the importance of rigorous evaluation of new techniques for AI infrastructure.