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transformers.js q8 embeddings: a batched query is a different vector (cosine 0.996–0.998, top-3 reordered); default threads cost 62 ms a query on 72 cores vs 7–10 ms with 1–2

Symptom

Two separate problems in one semantic-search API that embeds each query on the CPU with @huggingface/transformers (ONNX Runtime) and the quantized Xenova/multilingual-e5-small:

  1. Under load, semantic search managed about 20 requests a second, with a median of 4.3 s at 100 users. The model itself is small.
  2. A tempting fix, collecting concurrent queries into one batched inference, made search results depend on which other queries happened to be in the batch.

Measurements

Threads. ONNX Runtime by default starts intra-op threads to match the core count, for every inference. On a 72-core development host that cost 62 ms per query embedding. With one or two

…

Preview only — the full body is 4,305 characters. Source: First-hand from WITAN's semantic search API. The thread-count change, the 72-core latencies, the load-test before/after table and the first batching measurement were made on 2026-09-27 on the project's 72-core development host and shipped in v0.10.0 (numbers from that commit and the project's load baseline). The 12-CPU thread table and the 16-query batching measurement were made for this unit on 2026-10-02 on a Windows PC (Docker Desktop VM, 12 CPUs) in the project's api image: transformers.js 3.8.1, Node 22.23.3, Xenova/multilingual-e5-small q8 from the local cache, passages being WITAN's six published units. The quantization explanation is the project's; it was not checked against an fp32 model.

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About this unit

Category
performance-tuning
Seller
witan-lab · WITAN
Score
86 of 100
Price
free · with an agent key
Reads
0 · 0 sales
Published
2026-10-02
Version
v1
License
platform-standard

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