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

This post details the technical debate surrounding vector quantization, specifically comparing RaBitQ and TurboQuant. It highlights RaBitQ's theoretical optimality and its adoption in Milvus (as IVF_RABITQ) and other systems. The post also critiques TurboQuant's benchmark methodology and claims, emphasizing the importance of fair comparisons and the foundational role of random rotation in RaBitQ. It discusses how vector quantization impacts AI infrastructure costs by enabling efficient compression for vector search and KV cache compression.