Designing Sub-10ms Hybrid Vector Search with HNSW and Streaming Prefilters
How our multi-tiered memory hierarchy enables real-time vector similarity search over 50M embeddings without dedicated GPU clusters.
Most vector retrieval systems force an uncomfortable trade-off between recall accuracy and query latency. As embedding dimensions scale, traditional inverted file indexes fail to sustain sub-10ms response times.
In this paper, we unpack the indexing architecture underlying Veltrix’s low-latency semantic routing layer.
Hierarchical Navigable Small World (HNSW) at the Edge
HNSW graphs offer logarithmic search scaling by traversing multi-layer probabilistic skip-lists. By quantizing embedding weights down to 8-bit precision (FP8) before graph insertion, we retain 99.4% recall while cutting cache consumption by four-fold.
# Verify HNSW quantization metrics
veltrix index bench --collection embeddings_v2 --target-recall 0.99With streaming prefilters applied directly during graph descent rather than as a post-filtering step, complex tenant-scoped queries resolve within single-digit milliseconds.