GoKawiilhomebench
Benchmark the local LLMs you already have — speed, memory, and quality — as a live terminal leaderboard.
homebench is a single-command TUI that discovers the models installed in your local runner (Ollama, LM Studio, llama.cpp, vLLM, or any OpenAI-compatible server), runs a curated quality suite, measures tokens/sec, time-to-first-token, and memory footprint on your actual machine, and renders a live comparison leaderboard.
pip install homebench homebench
That's it. No config, no API keys, no cloud.
Why
There are great tools for one half of this problem, but nothing local-first that does both:
llama-bench (inside llama.cpp) measures speed only .
(inside llama.cpp) measures . lm-evaluation-harness measures quality but has no polished laptop UX and isn't built around the model runners most people actually use locally.
homebench fills the gap: local-first, zero-config, UX-driven. Clone-and-run, point it at the models you already pulled, and get an at-a-glance answer to "which of my local models is actually good, and how fast is it on this laptop?"
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