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 →](docs/benchmarking.md)

![llmfit demo: searching for a model, simulating different hardware, and planning a deployment](assets/demo.gif)

## 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