mlsanityData to 14 Oct 2026

Deploying Open-Source Large Language Models: A performance Analysis

arXiv 2409.14887 ↗First posted 23 Sep 2024cs.PF cs.AI cs.LGcited by 2

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Abstract (arXiv)

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'Universit'e 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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 13 of its 17 references that are in the map. First posted on arXiv 23 Sep 2024.

Builds on

13 of 17 references.

References · 13

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1 more
  1. 2023The Impact of AI on Developer Productivity: Evidence from GitHub Copilotcited by 274

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