Homebench provides a simple terminal-based interface to benchmark locally installed large language models. It measures tokens-per-second, memory consumption, and output quality, then presents results in a clear leaderboard format. Built for users of tools like Ollama, it helps developers and enthusiasts quickly evaluate and compare different local LLMs on their own laptops without complex setup.
homebench is a Developer Tools project. Lack of an easy, single-command way to compare performance and quality of locally installed LLMs on personal hardware. homebench is an open-source project aimed at developers and AI enthusiasts running local models. homebench is open source under the MIT license. It ships for the command line, and it can be self-hosted.
david-g-3654 builds and maintains homebench, and it first shipped in 2026. The project is developed in the open on GitHub with 12 commits in the last 90 days. Key capabilities include LLM Benchmarking, Speed Testing, and Memory Measurement.
Summary written by a language model from the project’s public pages.
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