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Find the AI ​​Model Best Suitable for Your Hardware

Whichllm is a tool that allows you to identify the native large language models that perform best on your hardware. Focusing on current benchmarks rather than the number of parameters, this Python-based software allows you to choose the most suitable model with a single command.

Updates

  • August 15, 2026: Stars 6,101 → 6,263, latest release v0.5.16 (August 14, 2026).
  • August 2, 2026: Stars 3,679 → 6,101, latest release v0.5.15 (July 3, 2026).

What you get

  • It determines the native language model best suited to your hardware with up-to-date benchmark data.
  • It predicts performance by automatically detecting GPU and system features.
  • It offers the opportunity to download models, start chats and create Python code samples with a single command.

Installation

Vehicle installation
uv tool install whichllm
uv tool upgrade whichllm  # update an existing install
Alternative installation methods
brew install andyyyy64/whichllm/whichllm
pip install whichllm

Running it

Let me list the models that best suit your hardware.
whichllm
Start a chat with a specific model
whichllm run "qwen 2.5 1.5b gguf"

If you don't write code

🤖 Paste this into your AI agent (Claude Code · Codex · Antigravity)

Using the Whichllm tool, I want to list the local big language models that best suit my hardware specifications and see the performance estimates of these models. How can I filter the models with the highest benchmark scores based on the GPU or CPU capacity in my system, as well as plan the hardware required for a particular model?

Related dictionary terms

Who it is forIt is for users who are looking for native AI models that will run most efficiently on their hardware and want to stay away from technical complexity.
LicenseMIT

Links

TreScout did not build this tool · we found it in GitHub trends and wrote it up. This page describes the repository as of 2026-06-09: The star count and our text belong to that day, the repository may have changed since. Check the repository link for the current state. This page was machine-translated from the Turkish original · the Turkish version prevails.