llmfit

Find out which open-source Large Language Models (LLMs) your hardware can comfortably run. llmfit inspects your CPU, system RAM, GPU(s), VRAM, and accelerator configuration to recommend models across popular quantizations.

📊 New: benchmark & share — real numbers from your machine, better estimates for everyone. Download a model, serve it, and measure real tok/s on your hardware — then contribute the results back to the project as a PR, straight from the TUI. No gh CLI, no third-party account. Every run is saved locally first, your own measurements replace estimates in the fit table, and each merged submission ships in the next release: anyone on identical hardware gets measured ✓ numbers before they ever run a benchmark.

Features

• Hardware Auto-Detection: Detects CPU cores, system RAM, available discrete/integrated GPUs, VRAM, and unified memory architecture (NVIDIA CUDA, Apple Silicon, AMD ROCm, Intel OneAPI).
• Model Compatibility Engine: Analyzes model parameter counts, context lengths, and quantization formats (GGUF, AWQ, GPTQ, EXL2) to project memory footprints and tokens-per-second performance.
• Interactive TUI & Web Dashboard: Choose between a lightweight, zero-dependency terminal interface or a feature-rich web dashboard.
• REST API Endpoint: Exposes standard HTTP JSON endpoints (/api/v1/system, /api/v1/models) for integration into orchestrators, dashboards, and automated deployment pipelines.
• Multi-Platform Support: macOS (Apple Silicon & Intel), Linux (x86_64 & ARM64), and Windows (x86_64).
• 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).

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.
• llmfit-gui — a Windows desktop GUI (PowerShell + WinForms) for llmfit: browse recommendations, download into LM Studio/Ollama, and benchmark, all point-and-click.

Install

Windows

scoop install llmfit

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

Pre-built Binaries

Download signed release binaries for Linux, macOS, and Windows directly from the GitHub Releases page.

Container Deployment

llmfit provides a multi-architecture Docker image (ghcr.io/alexsjones/llmfit) supporting both interactive CLI/TUI and headless Web UI / API server modes.

Interactive TUI

To launch the interactive TUI instead, pass the global --tui flag:

docker run -it --rm ghcr.io/alexsjones/llmfit --tui

Non-Interactive

This prints JSON from llmfit recommend command.

docker run ghcr.io/alexsjones/llmfit

Usage

Terminal Interface (TUI)

Launch llmfit in your terminal without flags to start the interactive browser:

llmfit          # interactive TUI: your hardware, every model, ranked

Keybindings inside the TUI:

• b: Open community benchmarks; I: Open live inference benchmarks
• h: Show help and keybindings
• ↑ / ↓ or k / j: Navigate list items
• /: Filter models by name, family, or quantization
• Esc: Clear search / Back

Command Line Options

# Print hardware telemetry and recommended models to standard output
llmfit recommend

# Output system profile and recommendations in raw JSON format
llmfit recommend --json

# Start the native HTTP API server
llmfit serve --host 0.0.0.0 --port 8787

Community & Benchmarks

llmfit includes hardware detection and performance benchmarks contributed by the community. You can share your hardware benchmark results using:

llmfit bench --share

How it works

llmfit detects your hardware (RAM, CPU, GPU/VRAM, backend), then scores every model in its catalog across four dimensions: memory fit, estimated speed, quality, and context. Speed estimates come from a memory-bandwidth model grounded in runtime sampling and real community measurements.

Contributing

Contributions are welcome, especially new models. Please run cargo fmt before pushing your changes.

Alternatives

If you're looking for a different approach, check out llm-checker — a Node.js CLI tool with Ollama integration that can pull and benchmark models directly. It takes a more hands-on approach by actually running models on your hardware via Ollama, rather than estimating from specs.

Code signing

llmfit's Windows release binaries are digitally signed (Authenticode) via SignPath.io, with a free code signing certificate provided by the SignPath Foundation.

License

MIT