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Llmfit Hardware Model Matcher

If you've ever wasted an afternoon downloading a 70B model only to find it won't run on your GPU, this is for you. It detects your actual hardware (RAM, CPU, GPU, even multi-GPU setups), scores hundreds of LLM models across fit/quality/speed dimensions, and tells you exactly which ones will work. Ships as both a TUI and CLI, handles quantization selection automatically, and works with Ollama, llama.cpp, MLX, and Docker runners. The REST API is particularly clever for cluster scheduling. You can also flip it around with the plan command to ask "what hardware do I need for model X at 8K context?" Honest take: the override flags for broken nvidia-smi and VM passthrough scenarios show this was built by someone who's actually deployed models in messy real-world environments.

skills.sh installs
1.4K
GitHub stars
61
last updated
Jul 14, 2026

Install

$ npx -y skills add aradotso/trending-skills --skill llmfit-hardware-model-matcher --agent claude-code

Installs into .claude/skills of the current project.

Files

llmfit Hardware Model Matcher

Skill by ara.so — Daily 2026 Skills collection.

llmfit detects your system's RAM, CPU, and GPU then scores hundreds of LLM models across quality, speed, fit, and context dimensions — telling you exactly which models will run well on your hardware. It ships with an interactive TUI and a CLI, supports multi-GPU, MoE architectures, dynamic quantization, and local runtime providers (Ollama, llama.cpp, MLX, Docker Model Runner).


Installation

macOS / Linux (Homebrew)

brew install llmfit

Quick install script

curl -fsSL https://llmfit.axjns.dev/install.sh | sh

# Without sudo, installs to ~/.local/bin
curl -fsSL https://llmfit.axjns.dev/install.sh | sh -s -- --local

Windows (Scoop)

scoop install llmfit

Docker / Podman

docker run ghcr.io/alexsjones/llmfit

# With jq for scripting
podman run ghcr.io/alexsjones/llmfit recommend --use-case coding | jq '.models[].name'

From source (Rust)

git clone https://github.com/AlexsJones/llmfit.git
cd llmfit
cargo build --release
# binary at target/release/llmfit

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Details

Repository
github.com/aradotso/trending-skills
First seen
Apr 16, 2026

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