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llmfit
Rust tool that checks which LLMs fit your RAM, CPU and GPU
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.
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
- 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