August 4, 2026
Bench, please
Homebench – Benchmark local LLMs for speed, memory, and quality
This tool rates the AI models on your own laptop, and commenters are already cheering the no-nonsense flex
TLDR: homebench is a new tool that compares the AI models already sitting on your computer by checking speed, memory use, and answer quality in one place. Commenters loved the practical idea, with the biggest laugh coming from praise that it saves people from building their own “vibe coded” mess.
A new little app called homebench has entered the local AI chat with a very simple promise: stop guessing which downloaded chatbot is actually worth keeping. Instead of making people juggle multiple tools, it checks how fast each model responds, how much memory it eats, and whether its answers are actually any good — all from a single command, right on your own machine, with no account signup or cloud drama. For people drowning in a pile of downloaded AI models, that pitch clearly hit a nerve.
And the comments? Instantly relatable. The creator basically opened with a confession that sounds like half the internet right now: they had too many local models sitting around and no easy way to tell which ones were smart versus just slow and bloated. That honesty gave the project a very "finally, someone said it" energy. The warmest reaction came from a commenter who admitted they had wanted this exact thing but were trying to avoid, in their words, a "vibe coding janky solution" to their own problem — which is both a compliment and a wonderfully savage summary of modern hobby coding culture.
The drama here is mild but juicy: this is a classic "why didn’t this already exist?" moment, mixed with gentle shade at older tools that only solved half the problem. No huge flame war yet, but the mood is clear: people are excited, slightly exasperated, and very ready for a leaderboard to settle arguments between their favorite local AIs once and for all.
Key Points
- •Homebench is a local-first terminal benchmarking tool that compares installed LLMs on speed, memory footprint, and quality in a live leaderboard.
- •It supports multiple local and API-compatible backends, including Ollama, LM Studio, llama.cpp, vLLM, and OpenAI-compatible servers.
- •The tool measures tokens per second, time-to-first-token, memory usage, and quality using a built-in suite of 31 deterministically graded tasks.
- •Homebench can be installed with pip, pipx, or from source, requires Python 3.9+, and does not require API keys, cloud services, or manual configuration.
- •Its CLI includes options for benchmarking all models or selected models, exporting reports, comparing historical runs, testing throughput across concurrency levels, and checking model fit for local hardware.