GraphCanon

llmfit

AlexsJones/llmfit

Terminal tool for right-sizing LLM models to system hardware

GitHub
29k
Stars
1.8k
Forks
55
Open issues
82
Watchers
Rust MITLast pushed Jul 7, 2026

Overview

llmfit is a Rust-based terminal application that evaluates Large Language Models (LLMs) against user hardware specifications. It assesses model compatibility based on RAM, CPU, and GPU capabilities, offering a command-line solution to determine which LLMs will run well locally.

Categories

Model TrainingInference & Serving

Tags

llmskillmlxlocalaiggufunsloth

Similar tools

transformers

huggingface/transformers

🤗 Transformers: the model-definition framework for state-of-the-art machine learning models

162k 34kPython

open-webui

open-webui/open-webui

User-friendly AI Interface (Supports Ollama, OpenAI API, ...)

145k 21kPython

LLMs-from-scratch

rasbt/LLMs-from-scratch

Implement a ChatGPT-like LLM in PyTorch from scratch

99k 15kJupyter Notebook

vllm

vllm-project/vllm

A high-throughput and memory-efficient inference and serving engine for LLMs

86k 19kPython

rtk

rtk-ai/rtk

CLI proxy reducing LLM token consumption by 60-90%

69k 4.3kRust

unsloth

unslothai/unsloth

Unsloth Studio is a web UI for training and running open models locally.

68k 6.1kPython

Install

cargo add llmfit
crates.io

README

llmfit

English · 中文 · 日本語

CI Crates.io License Signed with SignPath

New: Community Leaderboard — Browse real-world performance data from actual users. Press b to see measured tok/s, TTFT, and VRAM for any GPU — not just yours. Pick from 27+ hardware presets (RTX 5090 to Apple M1) with H to compare real numbers before you buy or build.

Hundreds of models & providers. One command to find what runs on your hardware.

A terminal tool that right-sizes LLM models to your system's RAM, CPU, and GPU. Detects your hardware, scores each model across quality, speed, fit, and context dimensions, and tells you which ones will actually run well on your machine.

Ships with an interactive TUI (default) and a classic CLI mode. Supports multi-GPU setups, MoE architectures, dynamic quantization selection, speed estimation, and local runtime providers (Ollama, llama.cpp, MLX, Docker Model Runner, LM Studio).

New: Community Leaderboard (b) — See real-world tok/s, TTFT, and VRAM usage from other users running the same hardware as you. Powered by localmaxxing.com, this bridges the gap between estimated and actual performance.

Also: Download Manager (D), Advanced Configuration (A), and Hardware Simulation — Press D to manage downloads, view history, delete models, and configure the download directory. Press A to tune TPS efficiency, run mode factors, and scoring weights. Press S to simulate different hardware.

Sister projects:

  • sympozium — managing agents in Kubernetes.
  • llmserve — a simple TUI for serving local LLM models. Pick a model, pick a backend, serve it.
  • llama-panel — a native macOS app for managing local llama-server instances.

Install

Windows

scoop install llmfit

If Scoop is not installed, follow the Scoop installation guide.

macOS / Linux

Homebrew

Prebuilt binary (recommended, works on all macOS/Linux versions):

brew install AlexsJones/llmfit/llmfit

Or from the homebrew-core formula, which builds from source on macOS versions without a bottle:

brew install llmfit

MacPorts

port install llmfit

Quick install

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

Downloads the latest release binary from GitHub and installs it to /usr/local/bin (or ~/.local/bin if no sudo).

Install to ~/.local/bin without sudo:

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

uv / pip

To install or update llmfit:

uv tool install -U llmfit

To run without installing:

uvx llmfit

You can also install llmfit as a Python package i