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. Follow the step-by-step benchmarking guide →
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.
Documentation
Install
Windows
WinGet
winget install --exact --id AlexsJones.llmfit
Scoop
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 in the normal way with tools such as pip or uv.
Pre-built Binaries
Download release binaries for Linux, macOS, and Windows directly from the GitHub Releases page. Windows binaries are only published once that release's sign-windows job has signed, repackaged, and re-checksummed them; if signing is skipped or fails, the Windows assets are held back and attached by a re-run of the release workflow (with its fresh signing requests approved) rather than shipped unsigned. Releases before v1.1.19 did not have this guard, so verify the executable signature on older versions if you require a signed binary.
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
This is the beginning of the README. Read the rest at the original. Read the full README ↗
