Skip to content
#edge computing Conference

Application of loop heat pipe cooling technology in multimodal large model servers

Sep 2026 · International Conference on Internet of Things, Communication Engineering, and Artificial Intelligence · Vol 14373, pp. 143730D - 143730D-8 · 0 citations · 7 references
Engineering

Abstract

With the implementation of multimodal large models in various industries such as military, medical care, industry, education, finance, and transportation, the demand for large-model computing power servers has witnessed explosive growth. The rapid iteration of artificial intelligence technologies and the surge of AIGC have further accelerated the growth in computing demand, driving continual increases in the power consumption and heat flux density of AI servers, while traditional air cooling is increasingly unable to meet the thermal management requirements of high-power-density chips. Since multimodal large models process data types like text, images, and voice, their high-computing-power servers need to be equipped with a large number of CPUs, high-performance GPUs/NPUs, and large-capacity memory to enable rapid model training and accurate logical reasoning. Therefore, there are extremely high requirements for the stable and reliable operation of high-computing-power servers. Performance fluctuations caused by prolonged overheating may lead to training interruptions. The greater the computing power, the higher the heat flux density, and efficient heat dissipation can control the chip temperature within a reasonable range. In view of the fact that liquid cooling is not suitable for some application scenarios, this paper applies loop heat pipe heat dissipation technology, innovates the form of loop heat pipe heat dissipation, designs the reservoir cavity in the evaporator diagonally, forms a loop heat pipe plate module, and constructs a loop heat pipe heat dissipation system to solve the heat dissipation problem of multimodal large-model computing power servers. Through thermal simulation analysis experiments, the effectiveness and feasibility of the loop heat pipe heat dissipation technology method are verified. Through high-temperature tests, the training and reasoning tasks of the multimodal large model proceed normally, and the computing power server operates in good condition, indicating that loop heat pipe cooling can effectively solve the heat dissipation problem of high-energy-consuming large-model computing power servers, and effectively support the promotion, deployment, and application of multimodal large models in environments such as "cloud-edge-end".

View source

Similar papers

#computer vision Review Sep 2017

Agile Software Development Methods: Review and Analysis

This publication proposes a definition and a classification of agile software development approaches and analyses ten software development methods that can be characterized as being "agile" against the defined criterion.

P. Abrahamsson, O. Salo, Jussi Ronkainen et al. · 727 citations · ⚡54
#computer vision Jun 2008

The impact of agile practices on communication in software development

The study shows that agile practices improve both informal and formal communication, but indicates that, in larger development situations involving multiple external stakeholders, a mismatch of adequate communication mechanisms can sometimes even hinder the communication.

M. Pikkarainen, Jukka Haikara, O. Salo et al. · 401 citations · ⚡48
#machine learning Review Open access Oct 2014

Software development in startup companies: A systematic mapping study

The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.

Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al. · 394 citations · ⚡54

Related blog posts

Microsoft Research Blog Oct 6, 2026

What AI gets wrong and what failure teaches us

Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity.  The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.