# Speed and Conversational Large Language Models: Not All Is About Tokens per Second

arXiv: https://arxiv.org/abs/2502.16721 · HTML: https://mlsanity.com/paper/2502.16721/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 12 references that are in the map.
- First posted on arXiv 23 Feb 2025.

## Builds on (most cited first, 5 of 5)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)

## Built on by (most cited first, 2 of 2)

- [La Leaderboard: A Large Language Model Leaderboard for Spanish Varieties and Languages of Spain and Latin America](https://mlsanity.com/paper/2507.00999/) (2025)
- [The Generative Energy Arena (GEA): Incorporating Energy Awareness in Large Language Model (LLM) Human Evaluations](https://mlsanity.com/paper/2507.13302/) (2025)

## Abstract (arXiv)

The speed of open-weights large language models (LLMs) and its dependency on the task at hand, when run on GPUs, is studied to present a comparative analysis of the speed of the most popular open LLMs.