Estudio de la eficiencia en la escalabilidad de GPUs para el entrenamiento de Inteligencia Artificial
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866915477753692160 |
|---|---|
| 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 |