When Your Collection Outgrows RAM

Qdrant recommends quantization to keep vector search responsive when full-precision dense vectors no longer fit in RAM, using TurboQuant to retain compressed vectors in memory while storing originals on disk for optional rescoring. The guidance suggests estimating vector memory needs, starting with 4-bit quantization, pinning the smaller quantized representation, leaving originals cold or cached, and selecting rescoring and oversampling through held-out quality and latency tests. Measurements show that rescoring is inexpensive when original vectors remain cached but can substantially increase latency and disk reads under tight memory limits, while asynchronous I/O may reduce the cost of cold reads. In tests on DBPedia-entity, 4-bit TurboQuant with rescoring recovered most approximate-search recall, and 1-bit quantization with a single rescoring pass achieved near-float32 ranking quality with much higher compression, though greater oversampling offered diminishing quality gains while increasing disk activity. The article distinguishes quantization from reduced datatypes such as float16 and turbo4, notes alternative scalar, binary, and product quantization methods, and emphasizes validating configurations against exact search, labeled relevance metrics, deployed memory limits, hybrid-search behavior, and shard layouts before production use.

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