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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.03263 |
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| _version_ | 1866915477753692160 |
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| author | Cortes, David Juiz, Carlos Bermejo, Belen |
| author_facet | Cortes, David Juiz, Carlos Bermejo, Belen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_03263 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Estudio de la eficiencia en la escalabilidad de GPUs para el entrenamiento de Inteligencia Artificial Cortes, David Juiz, Carlos Bermejo, Belen Machine Learning Artificial Intelligence Performance 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. |
| title | Estudio de la eficiencia en la escalabilidad de GPUs para el entrenamiento de Inteligencia Artificial |
| topic | Machine Learning Artificial Intelligence Performance |
| url | https://arxiv.org/abs/2509.03263 |