asiai bench

Cross-engine benchmark with standardized prompts.

Usage

asiai bench [options]

Options

Option	Description
`-m, --model MODEL`	Model to benchmark (default: auto-detect)
`-e, --engines LIST`	Filter engines (e.g., `ollama,lmstudio,mlxlm`)
`-p, --prompts LIST`	Prompt types: `code`, `tool_call`, `reasoning`, `long_gen`
`-r, --runs N`	Runs per prompt (default: 3, for median + stddev)
`--power`	Measure GPU power via powermetrics (sudo required)
`--context-size SIZE`	Context fill prompt: `4k`, `16k`, `32k`, `64k`
`--export FILE`	Export results to JSON file
`-H, --history PERIOD`	Show past benchmarks (e.g., `7d`, `24h`)

Example

asiai bench -m qwen3.5 --runs 3 --power

  Mac Mini M4 Pro — Apple M4 Pro  RAM: 64.0 GB (42% used)  Pressure: normal

Benchmark: qwen3.5

  Engine       tok/s (±stddev)    Tokens   Duration     TTFT       VRAM    Thermal
  ────────── ───────────────── ───────── ────────── ──────── ────────── ──────────
  lmstudio    72.6 ± 0.0 (stable)   435    6.20s    0.28s        —    nominal
  ollama      30.4 ± 0.1 (stable)   448   15.28s    0.25s   26.0 GB   nominal

  Winner: lmstudio (2.4x faster)
  Power: lmstudio 13.2W (5.52 tok/s/W) — ollama 16.0W (1.89 tok/s/W)

Prompts

Four standardized prompts test different generation patterns:

Name	Tokens	Tests
`code`	512	Structured code generation (BST in Python)
`tool_call`	256	JSON function calling / instruction following
`reasoning`	384	Multi-step math problem
`long_gen`	1024	Sustained throughput (bash script)

Use --context-size to test with large context fill prompts instead.

Cross-engine model matching

The runner resolves model names across engines automatically — gemma2:9b (Ollama) and gemma-2-9b (LM Studio) are matched as the same model.

JSON export

Export results for sharing or analysis:

asiai bench -m qwen3.5 --export bench.json

The JSON includes machine metadata, per-engine statistics (median, CI 95%, P50/P90/P99), raw per-run data, and a schema version for forward compatibility.

Regression detection

After each benchmark, asiai compares results against the last 7 days of history and warns about performance regressions (e.g., after an engine update or macOS upgrade).

Thermal drift detection

When running 3+ runs, asiai detects monotone tok/s degradation across consecutive runs. If tok/s drops consistently (>5%), a warning is emitted indicating possible thermal throttling buildup.