RaBitQ-SSD: Split Codes and Pipelined I/O for SSD Vector Search

Yuexuan Xu, Jianyang Gao, Michael Norris, Alibek Zhakubayev, Pankaj Singh, Junjie Qi, Matthijs Douze, Cheng Long · NTU, ETH Zurich, Meta · 2026
arXiv:2610.02652

Query path: prove a read is unnecessary before issuing it

IVF toy: probe a few lists, not all Illustrative data

Synthetic 2-D points, 8 clusters. Recall@10 is computed on this toy set, not from the paper.

Split-RaBitQ: keep a prefix of each 1-bit code in DRAM

Suffix bits, ex-code and re-rank factors live on SSD. Grid is a D=128 schematic.

Looser bound with fewer bits Illustrative numbers

Error half-width scales as 1/√D_m (rescaled by D/D_m keeps the estimate unbiased). Positions below are made up to show the pruning test.

Memory dial at 10B × 1024-d (reported)

No lookup table: position in RAM gives the page Illustrative layout

Hover a record in memory. Records are in the same order on SSD, fixed size, never crossing a 4 KB page.

Pipeline ladder (throughput relative to Naive)

Why Overlapped collapses at 64 threads

Pages issued (reported figures, different settings)

Reported comparisons

Throughput model: QPS ≈ r_max / pages per query Illustrative grid

Only r_max = 1.63 M reads/s is measured. Other columns are hypothetical devices.
Hover a cell.

Scaling 100K → 10B (reported endpoints)

“Less than a millionth” is a denominator effect

Hold the cost at 509.4 pages (4 KB each, ≈2.09 MB) and vary the corpus. Store size = N × 1024 B, matching the 10.24 TB store. Hypothetical for N below 10B.

The 3,210 QPS is the SSD ceiling ÷ pages

Caveats (click)

Pick a caveat.

References

  1. RaBitQ-SSD — Xu, Gao, Norris, Zhakubayev, Singh, Qi, Douze, Long, 2026
  2. RaBitQ — Gao, Long, 2024
  3. Extended RaBitQ — Gao et al., 2025
  4. DiskANN — Subramanya et al., 2019
  5. SPANN — Chen et al., 2021
  6. PipeANN — Guo, Lu, 2025
  7. Starling — Wang et al., 2024
  8. Product Quantization / Quicker ADC — Jégou, Douze, Schmid; André et al.
  9. ScaNN — Guo et al., 2020
  10. Filtered-DiskANN / FreshDiskANN — Gollapudi et al.; Singh et al.
  11. HELMSMAN, AlayaLaser, VeloANN, SkipDisk — as cited in the paper (search)