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arXiv · 2509.03263

Estudio de la eficiencia en la escalabilidad de GPUs para el entrenamiento de Inteligencia Artificial

David Cortes·Carlos Juiz·Belen Bermejo
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Abstract

Training large-scale deep learning models has become a key challenge for the scientific community and industry. While the massive use of GPUs can significantly speed up training times, this approach has a negative impact on efficiency. In this article, we present a detailed analysis of the times reported by MLPerf Training v4.1 on four workloads: BERT, Llama2 LoRA, RetinaNet, and Stable Diffusion, showing that there are configurations that optimise the relationship between performance, GPU usage, and efficiency. The results point to a break-even point that allows training times to be reduced while maximising efficiency.

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David Cortes, Carlos Juiz, Belen Bermejo. 2025-09-03. Estudio de la eficiencia en la escalabilidad de GPUs para el entrenamiento de Inteligencia Artificial. https://arxiv.org/abs/2509.03263

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