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/ ____| (_)
| | __ _ _ __ __ _ _ __ _ _ _ _ __ ___
| | / _` | '_ \ / _` | '__| | | | | '_ ` _ \
| |___| (_| | | | | (_| | | | | |_| | | | | | |
\_____\__,_|_| |_|\__,_|_| |_|\__,_|_| |_| |_|
caniarun 🚀
Not sure if your rig can handle that shiny new AI model? That's exactly what this tool is for!
caniarun checks out your hardware (GPU, CPU, and RAM) and tells you straight up which open-source AI models will actually run on your machine. Instead of throwing confusing metrics at you, it gives you a fun, easy-to-understand vibe check for over 80 models across different compression levels.
You'll get ratings like:
- 🧈 Smooth as butter
- 🔥 Dope run
- 😐 Meh usable
- 🐌 Laggy vibes
- 🗑️ Trash mode
Note: This project is a fork from the original canirun.ai, simplified into a script that anyone can easily run directly from their terminal.
Installation
Getting started is super easy. Just drop this in your terminal:
pip install caniarun
(Optional) If you're rocking an NVIDIA GPU and want more precise detection, use this instead:
pip install caniarun[nvidia]
How to Use
Just type this in and hit enter. We'll handle the rest!
caniarun
🌟 Check out the source code & contribute:
github.com/netssv/can_ia_run
1 maintainer
Metadata
1 maintainer
Release files for caniarun 0.1.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| caniarun-0.1.1.tar.gz | 170.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| caniarun-0.1.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 239.8 kB
Release files / caniarun-0.1.1.tar.gz
| Download URL | caniarun-0.1.1.tar.gz |
|---|---|
| Size | 170.4 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Yes |
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Transparency logRelease files / caniarun-0.1.1-py3-none-any.whl
| Download URL | caniarun-0.1.1-py3-none-any.whl |
|---|---|
| Size | 69.4 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.12
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Jul 5, 2026.
Transparency log
