mlsanityData to 14 Oct 2026

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

arXiv 2509.03263 ↗First posted 3 Sep 2025cs.LG cs.AI cs.PFcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 26 references that are in the map. First posted on arXiv 3 Sep 2025.

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References · 12

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  4. 2015An Introduction to Convolutional Neural Networkscited by 514
  5. 2023LoRA Fine-tuning Efficiently Undoes Safety Training in Llama 2-Chat 70Bcited by 195
  6. 2019MLPerf Training Benchmarkcited by 115
  7. 2019Benchmarking TPU, GPU, and CPU Platforms for Deep Learningcited by 52
  8. 2024Hardware Scaling Trends and Diminishing Returns in Large-Scale Distributed Trainingcited by 8
  9. 2022Benchmarking Resource Usage for Efficient Distributed Deep Learningcited by 7
  10. 2024Achieving Peak Performance for Large Language Models: A Systematic Reviewcited by 4
  11. 2017Scaling Deep Learning on GPU and Knights Landing clusterscited by 3
  12. 2022A Container-Based Workflow for Distributed Training of Deep Learning Algorithms in HPC Clusterscited by 3

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