---
description: Install asiai and run your first LLM benchmark in under 2 minutes. One command, zero dependencies, works on any Apple Silicon Mac.
---

# Getting Started

**Apple Silicon AI** — Multi-engine LLM benchmark & monitoring CLI.

asiai compares inference engines side-by-side on your Mac. Load the same model on Ollama and LM Studio, run `asiai bench`, get the numbers. No guessing, no vibes — just tok/s, TTFT, power efficiency, and stability per engine.

## Quick start

```bash
pipx install asiai        # Recommended: isolated install
```

Or via Homebrew:

```bash
brew tap druide67/tap
brew install asiai
```

Other options:

```bash
uvx asiai detect           # Run without installing (requires uv)
pip install asiai           # Standard pip install
```

### First launch

```bash
asiai setup                # Interactive wizard — detects hardware, engines, models
asiai detect               # Or jump straight to engine detection
```

Then benchmark:

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

Example output:

```
  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)
```

## What it measures

| Metric | Description |
|--------|-------------|
| **tok/s** | Generation speed (tokens/sec), excluding prompt processing |
| **TTFT** | Time to first token — prompt processing latency |
| **Power** | GPU power draw in watts (`sudo powermetrics`) |
| **tok/s/W** | Energy efficiency — tokens per second per watt |
| **Stability** | Run-to-run variance: stable (<5%), variable (<10%), unstable (>10%) |
| **VRAM** | Memory footprint — native (Ollama, LM Studio) or estimated via `ri_phys_footprint` (all engines) |
| **Thermal** | CPU throttling state and speed limit percentage |

## Supported engines

| Engine | Port | API |
|--------|------|-----|
| [Ollama](https://ollama.com) | 11434 | Native |
| [LM Studio](https://lmstudio.ai) | 1234 | OpenAI-compatible |
| [mlx-lm](https://github.com/ml-explore/mlx-examples) | 8080 | OpenAI-compatible |
| [llama.cpp](https://github.com/ggml-org/llama.cpp) | 8080 | OpenAI-compatible |
| [oMLX](https://github.com/jundot/omlx) | 8000 | OpenAI-compatible |
| [vllm-mlx](https://github.com/vllm-project/vllm) | 8000 | OpenAI-compatible |
| [Exo](https://github.com/exo-explore/exo) | 52415 | OpenAI-compatible |

## Custom ports

If your engine runs on a non-standard port, asiai will usually find it automatically via process detection. You can also register it manually:

```bash
asiai config add omlx http://localhost:8800 --label mac-mini
```

Manually added engines are persisted and never auto-pruned. See [config](commands/config.md) for details.

## Requirements

- macOS on Apple Silicon (M1 / M2 / M3 / M4)
- Python 3.11+
- At least one inference engine running locally

## Zero dependencies

The core uses only the Python standard library — `urllib`, `sqlite3`, `subprocess`, `argparse`. No `requests`, no `psutil`, no `rich`.

Optional extras:

- `asiai[web]` — FastAPI web dashboard with charts
- `asiai[tui]` — Textual terminal dashboard
- `asiai[mcp]` — MCP server for AI agent integration
- `asiai[all]` — Web + TUI + MCP
- `asiai[dev]` — pytest, ruff, pytest-cov
