11st of 5 in Open models
llmfit
Rust tool that checks which LLMs fit your RAM, CPU and GPU
Documentation ↗
(opens in a new tab)Website ↗
(opens in a new tab)Repository on GitHub ↗
(opens in a new tab)
- Stars
- 37.9k
- License
- MIT
- Last commit
- Oct 2026
- Last release
- Oct 2026
- Language
- Rust
18,735 on 25 Mar 202637,925 on 14 Oct 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
llmfit detects CPU cores, RAM, GPU and VRAM (NVIDIA CUDA, AMD ROCm, Intel OneAPI, Apple Silicon), then scores a model catalog on fit, speed, quality and context across quantizations such as GGUF, AWQ, GPTQ and EXL2. It runs as a TUI, a CLI with JSON output, or an HTTP API and web dashboard via `llmfit serve`. Speed figures are estimates from a memory-bandwidth model, and `llmfit bench` can measure real tok/s against a running provider.
Who it is for: Self-hosters choosing which local LLMs and quantizations their hardware can run
Strengths
- Detects NVIDIA, AMD, Intel and Apple Silicon hardware, including multi-GPU and unified memory
- Handles MoE models, so memory estimates reflect active parameters rather than total size
- JSON CLI output and REST endpoints (/api/v1/system, /api/v1/models) suit scripts and pipelines
- Installs via Scoop, Homebrew, MacPorts, uv/pip, cargo or a multi-arch Docker image
Weaknesses
- Speed and fit numbers are estimates unless measured on your hardware or matched to community results
- Does not serve models itself; it recommends and benchmarks against Ollama, llama.cpp, MLX and similar
- Windows binaries may ship unsigned if the signing job fails
- Model catalog is built in, so new models need a release or a custom-model entry
What it needs
- GPU optional
- Docker
- Needs Ollama, llama.cpp, MLX, Docker Model Runner or LM Studio (optional, for downloads and benchmarks)
- Models: GGUF, AWQ, GPTQ, EXL2
- port 8787
Head to head
Also in Open models
See all 5| Rank | Project | Score |
|---|---|---|
| 2 | layaSingle-pass multilingual classifier for typed choice, score and yes/no decisions | 67 out of 100 |
| 3 | timesfmPretrained time-series foundation model for zero-shot forecasting from Google Research | 65 out of 100 |
| #4 | kronosPretrained decoder-only models that forecast financial candlestick (K-line) series | 47 out of 100 |
| #5 | clipOpenAI's reference PyTorch code and weights for contrastive image-text embeddings | 29 out of 100 |