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llmfit: Find the Best Local AI Models for Your Hardware

Running local AI models can be frustrating. Some models are too large for your GPU, others run too slowly, and it is often hard to know which one is the best match before you download anything. llmfit solves that problem by checking your system hardware and showing which local models are most likely to run well on your machine. It detects your RAM, CPU, and GPU, then ranks models based on fit, speed, quality, and context length. (Mintlify)

llmfit is a terminal-based tool with both an interactive TUI and a classic CLI mode. It also supports local runtime providers such as Ollama, llama.cpp, MLX, Docker Model Runner, and LM Studio. (GitHub)

Why use llmfit?

If you want to run AI models locally, the biggest challenge is choosing a model that matches your hardware. A model that is too large may not run at all, while a smaller model might waste the capability of your system. llmfit helps remove that guesswork by automatically analyzing your available hardware and selecting the best-fit quantization and model options for your setup. (Mintlify)

It is especially useful for:

  • developers testing local LLMs

  • AI enthusiasts comparing models

  • users with limited VRAM who want the best working option

  • anyone planning hardware upgrades for AI workloads. (Mintlify)

Key features

llmfit automatically detects system hardware, scores models across multiple dimensions, estimates speed, and shows the best run mode for your machine. It supports an interactive interface for browsing models and a CLI mode for scripting and automation. It also includes planning tools to estimate what hardware would be needed for a specific model. (Mintlify)

How to install llmfit on Windows with Scoop

llmfit is distributed as a single binary and does not require runtime dependencies just to install and inspect models. On Windows, the recommended installation method is Scoop. (Mintlify)

Open Windows PowerShell or Windows Terminal and run:

Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser
Invoke-RestMethod -Uri https://get.scoop.sh | Invoke-Expression
scoop install llmfit

After installation, verify that it is working:

llmfit --version

To confirm hardware detection, run:

llmfit system

To launch the main interface, run:

llmfit

The official docs also note that if you want llmfit to download models or detect already installed models through supported runtimes, you should install a provider such as Ollama. llmfit works without it, but provider-based downloads and installed-model detection depend on those runtimes being present. (Mintlify)

How to use llmfit

When you run llmfit, it opens the interactive TUI. At the top, you will see your system specs, including CPU, RAM, GPU, VRAM, and backend. The main section shows a ranked list of models, along with useful details such as score, estimated tokens per second, quantization, run mode, memory usage, context length, and use case. (Mintlify)

Basic controls

Inside the interface, you can use:

  • Up / Down or j / k to move through models

  • / to search

  • f to filter by fit level

  • s to sort

  • Enter to view more details

  • q to quit. (Mintlify)

Useful commands

If you prefer command-line output instead of the full interface, llmfit also supports CLI commands such as:

llmfit --cli
llmfit fit --perfect -n 5
llmfit recommend --json --use-case coding --limit 3
llmfit search "llama 8b"
llmfit plan "Qwen/Qwen3-4B-MLX-4bit" --context 8192

These commands let you list the best-fitting models, search the catalog, get recommendations for a specific use case, or estimate the hardware needed for a particular model. (Llmfit Documentation)

Keep llmfit updated

If you installed llmfit with Scoop, you can update it with:

scoop update llmfit

Updating llmfit gives you the latest release and its current built-in model catalog. (Mintlify)

Final thoughts

llmfit is a practical tool for anyone who wants to run local AI models without trial and error. Instead of downloading random models and hoping they fit, you can quickly see what works best on your machine and make better decisions before committing time and storage. For Windows users, the Scoop install method makes setup fast and simple. (Mintlify)

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