llmfit

llmfit answers the question everyone running local AI asks first: will this model fit on my machine? It reads your CPU, RAM, and GPU, then scores every model in its catalog on memory fit, estimated speed, quality, and context length. No more downloading a huge model to find out it will not load. MIT licensed, free, one Rust binary.

Install with Homebrew, Scoop, MacPorts, uv, Docker, or a curl script, on macOS, Linux, or Windows. It detects NVIDIA, Apple Silicon, AMD ROCm, and Intel GPUs, understands GGUF, AWQ, GPTQ, and EXL2 quantization, and works with Ollama, llama.cpp, MLX, Docker Model Runner, and LM Studio. No account, and the README says nothing leaves your machine unless you ask. There is also a web dashboard and a REST API through llmfit serve.

Free at every size with no paid tier. Solo builders get the most out of it. Teams can run the API on shared GPU boxes to see what fits where. Once you have picked a model, ollama/ollama or ggml-org/llama.cpp does the actual running.

The catch: speeds are estimates from a formula unless you run the benchmark against a live runtime, and the GPU bandwidth table only covers about 80 cards. Windows and Intel Macs get weaker detection. The model catalog is baked in at compile time, so new models only show up when you update.