AI Lane

Inspect your hardware and find out which local AI models it can run.

ailane is a zero-config CLI that answers the questions you have before running AI models locally:

• What CPU, RAM, and GPU does this machine have?
• Can it run llama3.1:8b? At which quantization?
• Which local models should I run on it?
• Is my AI tooling (Ollama, CUDA, PyTorch, ...) set up correctly?

Works on Windows, Linux (including WSL, with GPU-passthrough detection), and macOS (including Apple Silicon unified memory).

Usage

No install needed:

npx ailane systeminfo

Or install globally:

npm install -g ailane

Commands

ailane systeminfo

CPU, memory, GPU, OS, and disk overview.

CPU
  Model              Intel Core Ultra 9 185H
  Cores              10 physical / 20 logical
  Architecture       x64

Memory
  Total              23.5 GiB
  Available          19.4 GiB
...

ailane gpu

Deep GPU detail: vendor, VRAM, driver version, CUDA/ROCm/Metal availability, WSL GPU passthrough, Apple Silicon unified memory. Uses nvidia-smi / rocm-smi / system_profiler when available for accurate numbers.

ailane recommend

Evaluates a catalog of popular open-weight models (Llama 3.x, Qwen 2.5/3, Mistral, Mixtral, Phi, Gemma, DeepSeek-R1 distills) against your hardware and tiers them:

Best picks
  ★ Qwen 2.5 32b          q4_k_m, 19.8 GiB
  ...
Runs on GPU (14)
Runs on CPU — slow (12)
Won't fit (6)

For each model it picks the highest-quality quantization that fits, preferring GPU > Apple unified memory > CPU.

ailane check <model>

Can this machine run a specific model?

ailane check llama3.1:8b          # default q4_k_m
ailane check qwen2.5:14b-q8_0     # explicit quantization
ailane check mystery-13b          # unknown models estimated from parameter count

llama3.1:8b (8.03B params, q4_k_m, 8192 ctx)
  Memory needed      6.3 GiB
  Budget             11.0 GiB — NVIDIA GeForce RTX 3060 (12 GiB VRAM)

  Fits — runs on GPU with 4.7 GiB headroom.

Exit codes are script-friendly: 0 fits, 2 does not fit, 1 error. When a model doesn't fit at the requested quantization, check suggests the best one that does.

ailane doctor

Checks your local AI tooling: Ollama (installed and running?), Python, CUDA toolkit, PyTorch (with CUDA?), Docker, and llama.cpp — with install suggestions for anything missing.

Options

Every command supports:

• --json — machine-readable output, nothing else on stdout
• --context <tokens> (recommend, check) — context length to budget KV-cache memory for (default 8192)

How the estimates work

Memory requirements are estimated from parameter count and quantization bits-per-weight (measured from llama.cpp GGUF files), plus a GQA-era KV-cache heuristic and runtime overhead:

required = params × bpw / 8  +  kv_cache(params, context)  +  overhead

Budgets are conservative: 92% of VRAM (driver reservation), 75% of Apple Silicon unified memory (Metal wired limit), 80% of system RAM (leave room for the OS). Real-world usage varies with runtime and settings — treat results as a good first approximation, not a guarantee.

Supported platforms

Windows, Linux (incl. WSL2), macOS. On any other OS the CLI prints an informative message and exits.

License

MIT © 2026 Zareef Ahmed

About the Developer

Built by Zareef Ahmed, a programmer and author passionate about making AI accessible on everyday hardware. Find more of his work at zareef.com.