First benchmark comparing all ruvector-core quantization methods against
TurboQuant on standard vector search datasets. 8 configurations, 3 datasets
(GloVe d=200, SIFT d=128, PKM d=384), 3 trials per config with variance.
Key findings:
- Int4 beats TurboQuant MSE on recall at 8x compression (91.2% vs 89.6% R@1)
- QJL correction hurts recall for vector search (9-41% loss)
- PQ with 8 subspaces fails at d=200 (18.2% R@1)
- TurboQuant MSE 3-bit fills unserved 10.7x compression tier (82.0% R@1)
- QuantizedVector::distance() never called during HNSW search
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
