as a tradeoff between memory and recall. The standard is Float32 with high fidelity and high memory cost. The basic solution is scalar quantization, which reduces each value to fewer bits (around 4× compression) with a slight recall loss. Although binary quantization pushes much harder, often reaching 32× compression, the retrieval result might become inconsistent due to information loss. On the other hand, product quantization may be more efficient, but it is harder to tune and operate in real production. In early May of 2026, Qdrant released TurboQuant, a new quantization method. And they claimed that “TurboQuant can reduce memory use without making retrieval quality too unstable “. TurboQuant sounds like the kind of feature vector search teams want. However, I wondered whether TurboQuant still holds up when we test it across different dataset sizes. Does it give a real improvement over common quantization methods, or does its advantage depend on the data?…