qdrant-minimize-latency

# Scaling for Query Latency Latency of a single query is determined by the slowest component in the query execution path. It is sometimes correlated with throughput, but not always — throughput and latency are opposite tuning directions. Low latency optimization is aimed at utilising maximum resource saturation for a single query, while throughput optimization is aimed at minimizing per-query resource usage to allow more parallel queries. ## Performance Tuning for Lower Latency - Increase segment count to match CPU cores (`default_segment_number: 16`) [Minimizing latency](https://skills.qdrant.tech/md/documentation/ops-optimization/optimize/?s=minimizing-latency) - Keep quantized vectors and HNSW in RAM: `memory: pinned` on Qdrant 1.19 or newer, `always_ram: true` on 1.18 or older - Reduce `hnsw_ef` at query time (trade recall for speed) [Search params](https://skills.qdrant.tech/md/documentation/ops-optimization/optimize/?s=fine-tuning-search-parameters) - Use local NVMe, avoid network-attached storage ## Memory Pressure and Latency RAM is the most critical resource for latency. If working set exceeds available RAM, OS cache eviction causes severe, sustained latency degradation. - Vertical scale RAM first. Critical if working set >80%. - Use quantization: scalar (4x reduction) or binary (16x reduction) [Quantization](https://skills.qdrant.tech/md/documentation/manage-data/quantization/) - Move payload indexes to disk if filtering is infrequent: `memory: cold` on Qdran...