AlexsJones / llmfit

# llmfit
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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).
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## 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.
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## Documentation
| | |
|---|---|
| **Get started** | Install · Usage · How it works |
| **Guides** | TUI guide · Benchmarking step-by-step · CLI & automation · Runtime providers · OpenClaw integration |
| **Reference** | How it works (full) · Platform & GPU support · Custom models · Development |
| **Project** | Contributing · Alternatives · Code signing · License |
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