## 5 Discussion  
The evaluation of HPC infrastructure for artificial intelligence tasks, specifically using convolutional neural networks (CNNs) for classification, is a widely explored and validated topic. Currently, distributed computing systems are used in large-scale research in areas such as medicine, aerospace, remote sensing, agribusiness, physics, mathematics, and quantum computing. Many of these systems have high processing capacities and are listed in the TOP500 [53], unlike the system proposed in this study, which is poorly scalable and represents less than 1% of the capacity of some of the infrastructures listed in the TOP500. As mentioned above, the purpose of this study is precisely to demonstrate that, even without access to the computing capabilities offered by high-performance infrastructures, it is possible to leverage the theoretical foundations and tools used in large-scale distributed systems to implement low-scalability solutions. This can be achieved using hardware considered obsolete, which, in many cases, has been discarded by educational institutions or by society in general to make way for new generations of devices, which implies an increase in costs and budgets. Our experiments show that PyTorch DDP consistently outperformed TensorFlow in terms of processing time on all datasets (Table 4), especially in the LSDS-HPC setting. For example, training ResNet50 on the 38-Cloud dataset using PyTorch DDP was 27% faster than using TensorFlow (3 h 02 min vs. 3 h 37 min). This is consistent with previous studies [18] highlighting PyTorch's resource efficiency, although our work extends this observation to the context of obsolete hardware. PyTorch's faster performance can be attributed to its low communication overhead and dynamic computation graph, which are better suited to heterogeneous GPU clusters [9]. However, TensorFlow achieved higher accuracy in specific scenarios (e.g., 93.05% vs. 76.87% for 38-Cloud on LSDS-HPC; see Table 6), suggesting greater robustness in gradient synchronization for complex datasets. This dichotomy is consistent with the findings  
Figure 7: Learning curves of the CIFAR-100 dataset for accuracy and loss evaluations. of [19], who observed that the centralized parameter server architecture in TensorFlow could improve convergence, albeit at the cost of higher latency. Despite its limitations, the LSDS-HPC infrastructure (composed of three GTX 1050 Ti GPUs) demonstrated empirical speedups of up to 1.68\(\times\) (MSLD dataset; Table 5) compared to single-GPU environments. This challenges the common belief that outdated GPUs are unsuitable for modern deep distributed learning (DDL) tasks. Notably, these results contrast with those of studies such as that of [PERSON] et al. [10], which emphasize the need for high-end GPUs (e.g., NVIDIA V100) for efficient training. In contrast, our findings validate that low-scalability systems can reduce costs while maintaining acceptable usability, which is crucial for resource-constrained institutions. However, the results on the CIFAR-100 dataset revealed significant limitations: both frameworks struggled to achieve high accuracy (\(<\)70%) and achieved very low speedups (e.g., 0.03\(\times\) for PyTorch on Google Colab). This is likely due to the high interclass variability and small image size (32 \(\times\) 32 pixels), which exacerbates communication bottlenecks in distributed environments. This observation is consistent with that reported by [PERSON] and [PERSON] [20], who noted that the efficiency of data parallelism decreases if the batch size is not scaled properly to the hardware capacity. Our study fills a gap in the literature by evaluating distributed training frameworks in low-resource environments, while most previous work [9; 18] focused on high-performance clusters. Unlike [PERSON] et al. [13], who used NVIDIA K80/V100 GPUs for medical image analysis, our experiment achieved comparable accuracy (97% for 38-Cloud) with older GPUs, highlighting the potential of hardware recycling.