llmfit tutorial: choose models and set up llama.cpp

This tutorial uses llmfit to choose a local LLM before downloading model files. The presenter demonstrates it on a Windows computer with 32 GB of RAM and Intel UHD Graphics 630, then runs a prompt through llama.cpp on the CPU.

The walkthrough explains how llmfit detects hardware and ranks models using quality, estimated speed, memory fit and context scores. Its bundled model database works offline, according to the presenter. Recommendations account for quantization and context length, but the speed figures in the main view are estimates rather than measured results.

Windows setup uses Scoop to install llmfit. The presenter navigates its terminal interface, filters by fit, searches model families and compares candidates side by side. Plan Mode estimates hardware requirements for a selected model. Hardware simulation recalculates recommendations for a proposed upgrade, while the community leaderboard supplies submitted benchmarks. The demonstrated computer has no matching benchmark results.

The final section installs llama.cpp with Windows Package Manager, sets an environment variable and restarts the terminal so llmfit can detect the runtime. It then downloads a model and tests a Python prompt. The narration gives conflicting parameter counts, so it does not establish the exact size of the model used.