Homebench – Benchmark local LLMs for speed, memory, and quality
Homebench is a new open-source, local-first tool designed to benchmark local LLMs for speed, memory usage, and quality. It provides a terminal-based leaderboard to help users evaluate model performance on their own hardware without needing cloud APIs.
Why it matters
As local LLM adoption grows, developers need standardized, transparent ways to measure performance on consumer hardware to optimize their workflows.
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.
There are great tools for one half of this problem, but nothing local-first that does both:
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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