LLM local
llama.cpp
LLM inference in C/C++
★ 130,2k estrellasMIT
Ver la guía →whatcanirun es un proyecto de LLM local de código abierto de fiveoutofnine (fiveoutofnine/whatcanirun en GitHub). Tiene 247 estrellas en GitHub y 23 bifurcaciones, se publica bajo la licencia MIT y se actualizó por última vez en GitHub el 26 de agosto de 2026.
Find the best local models for your hardware — and how to run them.
Clone the repo or visit the official site to get started.
The README documents install requirements and configuration.
Connect your own API keys where the project requires them.
Launch the app and start using it.
Recommends models that fit your RAM and chip — no VRAM guesswork.
Focused on the two stacks that run best on consumer hardware.
Get the exact run command for each recommended model.
Tuned for M-series Macs where most local inference happens.
Sets expectations on tokens-per-second before you download gigabytes.
Free and open — fork it or self-host the picker.
Download the source and move into the project folder.
git clone https://github.com/fiveoutofnine/whatcanirun.git
cd whatcanirunEvery project documents its own setup; start with install requirements.
cat README.md | head -60Follow the project's launch command — Docker, pip, npm, or a binary release.
# see README for the exact launch commandYes — MIT licensed and free to use.
Built primarily around Apple Silicon Macs and llama.cpp-compatible setups.
It recommends and gives commands; you run them yourself.
MLX is fastest on Apple Silicon; llama.cpp runs almost anywhere. The tool covers both.
It is open source — check the repo for contribution notes.
LLM inference in C/C++
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LLM inference in C/C++
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LLM inference in C/C++
Gratis y abierto: consigue el código fuente, dale una estrella al repositorio y empieza a crear.