Local LLM quote-drafting evals (Mac mini, Ollama)
Collection
Tests, graded answers and speed behind Staffbox.ai: an AI on a Mac mini that drafts quotes from your price list. Fictional test shops. • 6 items • Updated
date stringdate 2026-09-23 00:00:00 2026-10-01 00:00:00 | machine stringclasses 2
values | model stringclasses 4
values | streams int64 1 4 | per_stream_tok_s float64 20.2 20.6 ⌀ | wall_for_220_tokens stringclasses 7
values | notes stringclasses 5
values |
|---|---|---|---|---|---|---|
2026-09-23 | Mac mini M4, 16 GB | qwen3:8b via Ollama, default parallelism | 1 | 20.6 | 65 s | includes model load |
2026-09-23 | Mac mini M4, 16 GB | qwen3:8b | 2 | 20.4 | 112 s | requests queued |
2026-09-23 | Mac mini M4, 16 GB | qwen3:8b | 4 | 20.5 | 127 s | requests queued |
2026-10-01 | Mac mini M4, 16 GB, freshly erased, Staffbox installer | qwen3:8b via Ollama 0.35.0, flash attention, KV cache q8_0 | 1 | 20.2 | 11.2 s | model already warm |
2026-10-01 | Mac mini M4, 16 GB, freshly erased, Staffbox installer | qwen3:8b | 2 | 20.3 | 21.8 s | requests queued (one at a time) |
2026-10-01 | Mac mini M4, 16 GB, freshly erased, Staffbox installer | qwen3:8b | 4 | 20.3 | 43.5 s | requests queued |
2026-09-23 | Mac mini M4, 16 GB | qwen3:8b at 64K context, Hermes Agent one-shot task | 1 | null | 5 to 10 min | read a vault file, answer, write a log line; browser also running |
Staffbox bench
Ollama inference speed for a local LLM (qwen3:8b) on a 16 GB Mac mini M4, in dated rows.
By Staffbox.ai, an AI worker on a Mac mini that drafts quotes from your own price list. Measurements on Staffbox's own Mac mini only, no customer data; no customer has run Staffbox yet. hadi@staffbox.ai
Dated measurements of local LLM inference speed on the hardware Staffbox runs on, taken with scripts/bench.py. Rows are added, never edited.
Status, 2 Oct 2026: speed measured on one machine, a Mac mini M4 16 GB; other hardware not yet measured. Ollama serves one request at a time, so concurrent requests queue. Rows are in measurements.csv; per-answer times for the quote desk (average 1.1 to 2.9 s on the 2 Oct runs) are in the scorecards.
Accuracy lives in staffbox/staffbox-scorecards.
To contribute a row, run bench.py, then open an issue on Staffbox-ai/staffbox with the machine, model and output. Built from docs/measurements.md at commit 86343d9.
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