# TokenRace: compare local LLM servers on your own hardware > Source: > Published: 2026-11-12 10:57:09+00:00 **Note: The Show and Tell category is for sharing and discussing projects, showcasing your Spaces, Models, Datasets and more. We value open-source and technical details over promotional content, so focus on sharing the intricate aspects of your work**.Which local inference server is fastest for your model? On your machine, it depends, and benchmark posts rarely transfer. I kept failing to reproduce other people’s tok/s numbers, so I built TokenRace: a small open-source app that sends the same streaming request to whichever local servers you run and times everything on the client, with the same code for all of them. You pick a server and any model you have, start the test and get TTFT, tok/s, total time and RAM live, then a summary with median, min and max, a chart and a CSV. It works with Ollama, LM Studio, llama.cpp, vLLM, SGLang, MLX servers and others (15 out of the box, any OpenAI-compatible server can be added), and runs locally with one command: uvx tokenrace Repo: [GitHub - ernext/tokenrace: Compare local LLM inference servers (Ollama, LM Studio, MLX, llama.cpp, vLLM…) on your own hardware, with the same client-side timing for all of them. · GitHub](https://github.com/ernext/tokenrace) I also wrote up the method here: [Why your local LLM tok/s numbers don't match mine - DEV Community](https://dev.to/ernext/why-your-local-llm-toks-numbers-dont-match-mine-5dn6) If you measure a model on NVIDIA, AMD or Linux, I’d love to see the numbers.