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

arXiv: https://arxiv.org/abs/2509.03263 · HTML: https://mlsanity.com/paper/2509.03263/

## 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.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [LoRA Fine-tuning Efficiently Undoes Safety Training in Llama 2-Chat 70B](https://mlsanity.com/paper/2310.20624/) (2023)
- [MLPerf Training Benchmark](https://mlsanity.com/paper/1910.01500/) (2019)
- [Benchmarking TPU, GPU, and CPU Platforms for Deep Learning](https://mlsanity.com/paper/1907.10701/) (2019)
- [Hardware Scaling Trends and Diminishing Returns in Large-Scale Distributed Training](https://mlsanity.com/paper/2411.13055/) (2024)
- [Benchmarking Resource Usage for Efficient Distributed Deep Learning](https://mlsanity.com/paper/2201.12423/) (2022)
- [Achieving Peak Performance for Large Language Models: A Systematic Review](https://mlsanity.com/paper/2409.04833/) (2024)
- [Scaling Deep Learning on GPU and Knights Landing clusters](https://mlsanity.com/paper/1708.02983/) (2017)
- [A Container-Based Workflow for Distributed Training of Deep Learning Algorithms in HPC Clusters](https://mlsanity.com/paper/2208.02498/) (2022)

## 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.