Deploying Open-Source Large Language Models: A performance Analysis

Yannis Bendi-Ouis; Dan Dutartre; Xavier Hinaut

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arXiv · 2409.14887

Deploying Open-Source Large Language Models: A performance Analysis

Yannis Bendi-Ouis·Dan Dutartre·Xavier Hinaut

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Abstract

Since the release of ChatGPT in November 2022, large language models (LLMs) have seen considerable success, including in the open-source community, with many open-weight models available. However, the requirements to deploy such a service are often unknown and difficult to evaluate in advance. To facilitate this process, we conducted numerous tests at the Centre Inria de l'Université de Bordeaux. In this article, we propose a comparison of the performance of several models of different sizes (mainly Mistral and LLaMa) depending on the available GPUs, using vLLM, a Python library designed to optimize the inference of these models. Our results provide valuable information for private and public groups wishing to deploy LLMs, allowing them to evaluate the performance of different models based on their available hardware. This study thus contributes to facilitating the adoption and use of these large language models in various application domains.

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BibTeX RIS

Yannis Bendi-Ouis, Dan Dutartre, Xavier Hinaut. 2025-06-12. Deploying Open-Source Large Language Models: A performance Analysis. https://arxiv.org/abs/2409.14887

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