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CLI to tell you if an ML model will fit and run on your device, using real benchmarks + lightweight estimation.

4 starsPython

Willitrun – check if any ML model runs on any device (benchmark-backed)

by smoothyy·Apr 7, 2026·1 point·2 comments

AI Analysis

●●●BangerSolve My ProblemDark Horse

Real benchmark database for edge ML when most tools only guess at performance.

Strengths
  • 482 benchmarks across 88 devices including Jetson, Apple Silicon, and desktop GPUs.
  • Tiered lookup system uses real data first, falls back to FLOPs estimation with confidence markers.
  • Offline-capable SQLite database means no API calls or network dependency during checks.
Weaknesses
  • Coverage gaps inevitable for newer devices and models not yet in the benchmark database.
  • Estimation fallback with 20% overhead margin may still produce false positives on edge cases.
Category
Target Audience

Edge ML developers, embedded engineers, ML practitioners deploying to hardware

Similar To

MLPerf · Papers With Code · HuggingFace Model Hub

Post Description

I kept running into the same problem with local/edge ML: I would read through model cards or start downloading a model, and only later realize it barely didn't fit on my device or would run too slowly to be useful.

So I built willitrun, a small CLI that tries to answer that upfront.

It checks whether a model is likely to fit and run on a given device. When benchmark data exists, it uses that first; otherwise it falls back to a lightweight estimate. Currently covers 482 benchmarks across 88 devices (desktop GPUs, server hardware, Apple Silicon, and NVIDIA Jetson) with HuggingFace model name resolution built in.

Right now the goal is not to be perfect, but to be useful enough to avoid obviously bad choices before spending time downloading or testing models manually. It's also useful for edge devices like a Jetson Orin because you can check performance without physically accessing the hardware.

Most public benchmarks focus on LLMs, but out of personal interest I tried to include other categories as well.

I would be very interested in feedback, especially around cases where the estimates are off or where benchmark coverage is missing.

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