Combined recall↔QPS Pareto (tiling × prefix × C)

Perf record: 018-combined-pareto.json. Cohere v3, 1M × 1024, cosine, Granite box. --quant binary --batch 16 --scan-bits N --rerank C.

What we did

Compose the two batch levers — tiling (016, free) and prefix truncation (014, recall-for-bandwidth) — and sweep rerank C, to draw the actual recall-vs-QPS frontier and pick the best operating point at each recall target. All runs at tile=16.

The frontier

Conclusions

1. Tiling is the universal lever; prefix is a low-recall lever. At high recall (≳0.97) the full 1024-bit scan + tiling dominates — 0.993 @ 850, 0.9975 @ 731. Truncation only wins below ~0.97 (768/C=1000 → 0.970 @ 893), because at high recall you must crank C so hard to recover the lost bits that the prefix's bandwidth saving is eaten by extra rerank. So: always tile; truncate only when you're willing to live under ~0.97 recall for more QPS.
2. The whole effort beats the 009 baseline on both axes. Original 009 was 0.994 @ 585 QPS. With tiling we now hit 0.9975 @ 731 (higher recall and +25% QPS) or 0.993 @ 850 (+45% QPS at the same recall tier). Recommended default: tile=16, full bits, C=1000 → 0.993 @ 850.
3. Higher C only buys recall at high bits. 768/C=4000 reaches 0.9947 but at 588 QPS — dominated by 1024/C=1000 (0.993 @ 850) and 1024/C=2000 (0.9975 @ 731). Over-retrieving on a truncated index is the worst of both.

Caveats

• Batch path (rayon over queries) — these are throughput numbers; single-request serving latency is the separate axis (017, and the prefix latency knob next).
• One back-to-back run, CV < 0.3%; spot-level absolutes drift between entries.