SOTA benchmark: quantized-space HNSW construction and hybrid SSD vector residency at trillion-scale patterns

Finding

UBASE: An AI Search Engine for Trillion-Scale Vector Data Management at ByteDance (arXiv:2608.30607, submitted 2026-08-31) describes a production system spanning more than 7,000 clusters and 300 PB of indexed data, with its largest deployment approaching one trillion high-dimensional vectors. The originating team reports up to 3x throughput, 80% lower indexing memory, and 86% lower operating cost versus prior systems.

The two reusable ideas worth testing are:

1. quantization-aware vector kernels that construct the graph directly in quantized space without retaining a full-precision copy
2. a hybrid storage engine with memory, mixed, and SSD residency plus record-level caching

RuV mapping

RuVector core, RuVector WASM for constrained memory variants, Cognitum placement, distributed infrastructure, Core Memory, and RuView large temporal/spatial indexes.

Reproduction before architecture change

Do not copy the UBASE system wholesale. Isolate the two claims against current RuVector HNSW and quantization paths.

Experiment A: quantized-space index construction

Compare current full-precision build, current quantized path if present, and a SymRaBitQ-inspired quantized-space build under identical vectors and HNSW parameters.

Report recall@10, build throughput, peak RSS, index size, query p50/p95/p99, update throughput, CPU time, energy where measurable, and determinism across seeds.

Experiment B: hybrid residency

Compare memory-resident, hybrid hot-record cache plus SSD, and SSD-heavy layouts under matched working sets and request traces.

Report recall, throughput, tail latency, cache hit rate, memory footprint, SSD bytes read/write, compaction cost, ingestion latency, and operating-cost model.

Workloads

At minimum use 1M, 10M, and the largest affordable synthetic or public corpus. Include write-heavy, read-heavy, bursty, skewed-hotset, cold-start, random-access, and deletion/update cases.

Falsification

The published benefits are production-system results at ByteDance scale. They may depend on workload locality, custom storage, hardware, or engineering not captured by the paper. A simpler scalar/product quantization plus mmap baseline must be included.

Acceptance

Advance quantized-space construction only if peak build memory falls at least 50% while recall@10 loses no more than 0.5 percentage points and query p95 does not regress more than 10%.

Advance hybrid residency only if memory footprint falls at least 60% at matched recall while p95 latency remains within the declared SLA and total modeled infrastructure cost falls at least 30%.

Governance and migration

Keep the existing index format readable. New formats require explicit versioning, dual-read migration, corruption tests, checksums, crash recovery, and full rollback. No destructive format migration or default switch without independent MetaHarness benchmark receipts.

Start by locating the current RuVector HNSW and quantization paths, then review the MetaHarness benchmark entry point. Compare the existing baselines with the proposed quantized-space and hybrid-residency experiments across the stated workloads. Done means meeting the recall, memory, latency, cost, migration, and rollback acceptance criteria without changing the default format or behavior prematurely.