asiai speedometer logo Open source · Apache 2.0 · Zero dependencies

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$pip install asiai
$asiai bench# 15s → your first comparison
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Python 3.11+ · stdlib only · 9 engines auto-detected · agent-ready API
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GET /api/status ≤ 500ms 
{
  "chip": "Apple M4 Pro",
  "ram_gb": 64.0,
  "memory_pressure": "normal",
  "gpu_utilization_percent": 45.2,
  "engines": {
    "ollama": { "running": true, "models_loaded": 2 },
    "lmstudio": { "running": true, "models_loaded": 1 }
  }
}
GET /api/snapshot Full state 
{
  "system": {
    "chip": "Apple M4 Pro",
    "gpu_cores": 20,
    "gpu_utilization_percent": 45.2,
    "thermal_state": "nominal"
  },
  "engines": [{
    "name": "ollama",
    "models": [{ "name": "qwen3.5:latest", "size_params": "35B" }]
  }]
}

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01

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02

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03

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asiai Benchmark screen — one-click Quick Bench with live progress

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asiai bench
asiai live web dashboard — CPU load, memory, GPU power and loaded models

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asiai web
asiai fleet cockpit — engines, models, KV cache and power across every Mac on the network

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asiai fleet

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asiai leaderboard

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{% for i in range(1, 13) %}
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{% endfor %}

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1

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$ brew install druide67/tap/asiai
# or: pip install asiai
2

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$ asiai detect
✔ ollama (11434)
✔ lmstudio (1234)
✔ mlx-lm (8080)
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3

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$ asiai bench -m qwen3.5
ENGINETOK/STTFT
lmstudio71.242ms
ollama54.861ms
mlx-lm30.138ms

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2.3×

MLX vs llama.cpp

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Flat

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30 vs 71

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{% set engines = [ ("Ollama", "engines/ollama/", ":11434", "native", ["GGUF"], true), ("LM Studio", "engines/lmstudio/", ":1234", "compat", ["GGUF + MLX"], true), ("mlx-lm", "engines/mlxlm/", ":8080", "compat", ["MLX"], false), ("llama.cpp", "engines/llamacpp/", ":8080", "compat", ["GGUF"], false), ("oMLX", "engines/omlx/", ":8000", "compat", ["MLX"], false), ("vllm-mlx", "engines/vllm-mlx/", ":8000", "compat", ["MLX"], false), ("vMLX", "engines/vmlx/", ":8000", "compat", ["MLX", "Mamba/SSM"], false), ("Rapid-MLX", "engines/rapidmlx/", ":8000", "compat", ["MLX", "Homebrew"], false), ("Exo", "engines/exo/", ":52415", "compat", ["MLX", "Distributed"], false), ] %}
{% for name, href, port, api, formats, vram in engines %}
{{ name }}{{ port }}
{% if api == "native" %}{{ tr("eng_native") }}{% else %}{{ tr("eng_compat") }}{% endif %} {% for f in formats %}{{ f }}{% endfor %} {% if vram %}✓ VRAM{% endif %}
{% endfor %}

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{% set metrics = [ ("tok/s", "m_toks"), ("TTFT", "m_ttft"), ("Power (W)", "m_power"), ("tok/s/W", "m_eff"), ("Stability", "m_stab"), ("VRAM", "m_vram"), ("Thermal", "m_therm"), ("Context", "m_ctx"), ] %}
{% for label, key in metrics %}

{{ label }}

{{ tr(key) }}

{% endfor %}

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{{ tr("lb_full") }} →
01 llama-3.2-3b-instruct-4bit mlx-lm Apple M4 Max · 64 GB 228.0 88 ms
02 qwen3.6-35b-a3b · nvfp4 ollama Apple M5 Max · 128 GB 124.9 39 ms
03 gemma-4-31b-it ollama Apple M5 Max · 128 GB 115.8 64 ms
{{ tr("lb_contribute") }} asiai bench --share

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asiai bench --card
asiai throughput card — mtplx-qwen36-27b-optimized-speed on mlx-lm at 48.2 tok/s median (±1 CI95, n=5), Apple M5 Max
asiai agentic card — prefix-cache reuse fraction 0.80 across cold, warm and prefix phases, Qwen3.6-27B on llama.cpp asiai code-quality card — tool_call, recovery and thinking suites at 100%, deterministic grading, Qwen3.6-27B on llama.cpp
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