rUvector Performance Benchmarks

Date: November 25, 2025 Test Environment: Linux 4.4.0, Rust 1.91.1

---

⚠️ Important Disclaimer

This document contains internal rUvector benchmark results only.

The previous version of this document made unfounded performance claims comparing rUvector to other vector databases (e.g., "100-4,400x faster than Qdrant"). These claims were based on fabricated data and hardcoded multipliers in test code, not actual comparative benchmarks.

We have removed all false comparison claims. This document now only reports verified rUvector internal benchmark results.

---

Verified rUvector Benchmark Results

1. Distance Metrics Performance (SimSIMD + AVX2)

rUvector uses SimSIMD with custom AVX2 intrinsics for SIMD-optimized distance calculations:

Dimensions	Euclidean	Cosine	Dot Product
128D	25 ns	22 ns	22 ns
384D	47 ns	42 ns	42 ns
768D	90 ns	78 ns	78 ns
1536D	167 ns	135 ns	135 ns

Batch Processing (1000 vectors × 384D): 278 µs total = 3.6M distance ops/sec

2. HNSW Search Performance

Benchmarked with 1,000 vectors, 128 dimensions:

k (neighbors)	Latency	QPS Equivalent
k=1	45 µs	22,222 QPS
k=10	61 µs	16,393 QPS
k=100	165 µs	6,061 QPS

3. rUvector Internal Scaling Tests

10,000 Vectors, 384 Dimensions

Configuration	Insert (ops/s)	Search QPS	p50 Latency
rUvector	34,435,442	623	1.57 ms
rUvector (quantized)	29,673,943	742	1.34 ms

50,000 Vectors, 384 Dimensions

Configuration	Insert (ops/s)	Search QPS	p50 Latency
rUvector	16,697,377	113	8.71 ms
rUvector (quantized)	35,065,891	143	6.86 ms

4. Quantization Performance

Scalar Quantization (4x compression)

Operation	384D	768D	1536D
Encode	605 ns	1.27 µs	2.11 µs
Decode	493 ns	971 ns	1.89 µs
Distance	64 ns	127 ns	256 ns

Binary Quantization (32x compression)

Operation	384D	768D	1536D
Encode	625 ns	1.27 µs	2.5 µs
Decode	485 ns	970 ns	1.9 µs
Hamming Distance	33 ns	65 ns	128 ns

Compression Ratios:

• Scalar (int8): 4x memory reduction
• Product Quantization: 8-16x memory reduction
• Binary: 32x memory reduction (with ~10% recall loss)

---

Architecture

rUvector

Component	Technology	Benefit
Core	Rust + NAPI-RS	Zero-overhead bindings
Distance	SimSIMD + AVX2/AVX-512	4-16x faster than scalar
Index	hnsw_rs	O(log n) search
Storage	redb (memory-mapped)	Zero-copy I/O
Concurrency	DashMap + RwLock	Lock-free reads
WASM	wasm-bindgen	Browser support

---

Features

Feature	rUvector
HNSW Index	✅
Cosine/Euclidean/DotProduct	✅
Scalar Quantization	✅
Product Quantization	✅
Binary Quantization	✅
Filtered Search	✅
Hybrid Search (BM25)	✅
MMR Diversity	✅
Hypergraph Support	✅
Neural Hashing	✅
Conformal Prediction	✅
AgenticDB API	✅
Browser/WASM	✅

---

Use Cases

rUvector is ideal for:

• Embedded/Edge deployment - Single binary, no external dependencies
• Low-latency requirements - Sub-millisecond search times
• Browser/WASM - Need vector search in frontend
• AI Agent integration - AgenticDB API, hypergraphs, causal memory
• Research/experimental - Neural hashing, TDA, learned indexes

---

Reproducing Benchmarks

# rUvector Rust benchmarks
cargo bench -p ruvector-core --bench hnsw_search
cargo bench -p ruvector-core --bench distance_metrics
cargo bench -p ruvector-core --bench quantization_bench

References

• rUvector Repository
• SimSIMD SIMD Library
• hnsw_rs Rust Implementation

---

Note on Comparisons

We do not currently have verified comparative benchmarks against other vector databases.

If you need to compare rUvector with other solutions, please run your own benchmarks in your specific environment with your specific workload. Performance characteristics vary significantly based on:

• Vector dimensions and count
• Search parameters (k, ef_search)
• Hardware configuration
• Dataset distribution
• Query patterns

We welcome community contributions of fair, reproducible comparative benchmarks.