Andyyyy64

    Andyyyy64/whichllm

    Find the local LLM that actually runs and performs best on your hardware. Ranked by real, recency-aware benchmarks, not parameter count. One command, run it instantly.

    ai
    cli
    graphics
    nlp
    llm
    localllm
    Python
    MIT
    6.8K stars
    377 forks
    24 GitHub watchers
    Updated Nov 6, 2026
    View on GitHub

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    Where it stands

    Rank, growth pace and release cadence relative to everything GStars tracks.

    5 releases in the last 90 days, the latest 1 day ago, with 30 contributors on GitHub.

    Peaked at #29 in the weekly ranking the week of Jun 28, its best in the past year.

    Use Cases & Benefits

    • Finds and ranks local large language models (LLMs) that best run on your hardware using real, recency-aware benchmarks.
    • Provides evidence-based, live benchmark rankings rather than relying on model size or parameter count heuristics.
    • Use for selecting the optimal LLM for your existing GPU/CPU/RAM setup to maximize performance and compatibility.
    • Use for simulating different GPU configurations before hardware purchase to plan LLM deployment effectively.
    • Use for instantly running or generating Python snippets to deploy top-ranked LLMs without manual setup.

    Should you depend on it?

    License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.

    License

    MIT

    Permissive: commercial use, modification and redistribution are allowed with attribution.

    Community files

    • README: present
    • Contributing guide: present
    • Code of conduct: present

    Watch-outs

    No watch-outs from GStars' checks.

    General guidance from repository metadata, not legal advice. How we score

    From the README

    Find the best local LLM that actually runs on your hardware.

    Auto-detects your GPU/CPU/RAM and ranks the top models from HuggingFace that fit your system.

    Run the recommendation command once, with no project setup.

    Simulate a GPU before you buy hardware.

    Install it when you use it often.

    Other install paths.

    After install, run whichllm directly. For one-off runs, replace whichllm with uvx whichllm@latest.

    Excerpt from the Andyyyy64/whichllm README on GitHub (MIT license). © its authors.Read the full README on GitHub

    Daily snapshots

    Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots

    Forks

    377+50

    Open issues

    5-16

    Compared with peers in AI

    Fastest-growing repositories GStars tracks in this cohort for the week of Nov 1, by 7-day star growth

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