Benchmarks

Real numbers from uteke bench on Oracle Cloud ARM (Ampere A1, 4 vCPU, 24GB RAM). Embedding model: EmbeddingGemma Q4 (768d, ONNX Runtime, CPU-only).

Results

Scale	Insert ops/s	Insert Total	Recall Avg	Recall P95	DB Size	Index Size
100 memories	18.5/s	5.4s	40ms	46ms	708KB	319KB
1,000 memories	21.8/s	45.9s	45ms	51ms	5.3MB	3.2MB
10,000 memories	6.0/s	28.0 min	42ms	50ms	81.3MB	30.3MB

Key Takeaways

Recall Latency: Flat ~40-45ms from 100 to 10K memories

The killer stat: recall latency barely changes as the store grows.

• 100 memories → 40ms
• 1,000 memories → 45ms
• 10,000 memories → 42ms ← actually faster than 1K (warm ONNX cache)

HNSW search is O(log N), so even at 10K memories, the vector index adds <1ms. The ~40ms floor is dominated by ONNX embedding inference, not search.

The full pipeline:

1. Query → ONNX embedding generation
2. HNSW vector search
3. FTS5 full-text search
4. Reciprocal Rank Fusion (k=60)

No network round-trip. No API call. Everything in-process.

Insert Throughput: 6-22 ops/s (CPU-bound)

Each insert requires an ONNX embedding pass (CPU inference). Throughput drops at scale because HNSW graph traversal grows as the index expands:

• 100 memories → 18.5 ops/s
• 1,000 memories → 21.8 ops/s
• 10,000 memories → 6.0 ops/s

At 6 ops/s, inserting 10K memories takes ~28 minutes. For bulk ingestion, use uteke import (batch mode) which pipelines embeddings.

Storage Efficiency

• 100 memories → 708KB DB + 319KB index = ~10KB per memory
• 1,000 memories → 5.3MB DB + 3.2MB index = ~8.5KB per memory
• 10,000 memories → 81.3MB DB + 30.3MB index = ~11.2KB per memory

Storage scales linearly (~10KB/memory). SQLite + HNSW both grow predictably.

How to Reproduce

uteke bench --counts 100,1000,10000 --json

Or with a custom store path:

uteke bench --counts 100,1000 --store /tmp/bench --json

External Evaluation

See LongMemEval retrieval harness for accuracy evaluation against standard benchmarks.

Environment

Component	Details
Hardware	Oracle Cloud ARM (Ampere A1, 4 vCPU, 24GB RAM)
OS	Linux 6.8.0 (aarch64)
Rust	1.85+
Embedding	EmbeddingGemma Q4, 768d, ONNX Runtime CPU
Uteke	v0.12.0

Methodology

The benchmark uses uteke bench which:

1. Generates deterministic synthetic memories (seeded PRNG)
2. Inserts them one-by-one with embedding
3. Runs recall queries at each scale
4. Measures wall-clock time for insert and recall
5. Reports ops/s, latency percentiles, and storage footprint

No external services. No network. No Docker. Just the binary.