## 1 Introduction
The rapid expansion of deep learning in areas such as computer vision, robotics, and navigation has led to a surge in demand for high-performance graphics cards. Such a state of affairs has also translated into increased research interest. However, many existing comparisons of DL (Deep Learning) performance against hardware architectures focus on super throughput or benchmark results but do not take into account the practical  
Figure 1: Vision systems as a tool for detecting navigational signs. limitations of real-world applications. In particular, there is a lack of comprehensive studies that simultaneously assess learning performance, energy efficiency, and overall hardware compatibility in realistic scenarios. Also noticeable nowadays is the differentiated approach to graphics card testing, encompassing different environments and objectives. The following are examples of various tests related to the use of graphics cards. [PERSON] et al. [39] compared the performance of different deep neural networks (DNNs) for image recognition, taking into account accuracy, complexity, computation time, and memory consumption. The experiments were conducted on two different hardware platforms (NVIDIA Titan X Pascal and NVIDIA Jetson TX1). [PERSON] et al. [40] presented a performance comparison of four popular distributed deep learning frameworks (Caffe-MPI, CNTK, MXNet, and TensorFlow) when training neural networks, such as AlexNet, GoogleNet, and ResNet-50 [41], in single GPU, multi-GPU, and multi-node environments. Nvidia Tesla P40 graphics cards were used in the study. [PERSON] et al. [42] presented a performance analysis of computer systems designed for deep learning workloads, such as NVIDIA DGX-2, AWS P3, IBM Power System AC922, and the Exxact TensorEX TS4 server, using AlexNet, ResNet, and BERT models. The impact of hardware configurations on GPU communication, computational performance, and scalability of the different models was evaluated. NVIDIA Tesla V100 and NVIDIA GeForce RTX 2080 Ti graphics cards were used in the tests. [PERSON] et al. [43] analyzed the programmability, computational performance, and precision loss of NVIDIA Tensor Core units on the Volta architecture during mixed-precision matrix multiplication operations. The authors tested various programming interfaces such as CUDA WMMA, CUTLASS, and cuBLAS. [PERSON] and [PERSON] [44] analyzed the suitability of AMD graphics cards (AMD RX 570) as an alternative to NVIDIA in real-time systems. They discussed the advantages and disadvantages of the AMD platform and highlighted the benefits of using the open-source ROCm software for real-time GPU management research, especially in the context of flexibility and repeatability of experiments. [PERSON] et al. [45] presented an analysis of the scalability of learning deep neural networks (DNNs) using the example of training AlexNet and ResNet-50 models on the Ima-geNet dataset, using very large batch sizes and the adaptive learning rate scaling algorithm LARS. Different types of NVIDIA graphics cards (Tesla P100, K20, and M40) were used in the study. [PERSON] et al. [46] analyzed the differences in runtime, memory usage, and CPU and GPU efficiency when training deep learning models using the PyTorch framework. In the experiment, they used the DenseNet121 model to classify images of cats and dogs, training it on an Intel Xeon Gold 6256 CPU and two NVIDIA A6000 graphics cards (48 GB VRAM each). [PERSON] et al. [47] compared two popular GPU architectures, NVIDIA Fermi (Tesla C2050) and AMD/ATi Radeon 5870, and two GPU programming models, CUDA and OpenCL, using them to implement and accelerate spiking neural networks (SNNs). The performance of the different implementation versions (with memory and instruction optimizations) was compared and analyzed, among other things, including computation time, data transfer, cache, and GPU resource utilization. When analyzing the studies, it is noticeable that many of them focus on the performance of enterprise-class GPUs.