## 3 Results
### Compilation of Standardized Results for Measuring the Performance of the Criteria  
Table 11 shows a summary of the normalized scores, weights, and weighted performance values for the criteria analyzed. The normalization process of the results was \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Price [PLN]** & **VRAM [GB]** & **AI Compute Units** & **TGP [W]** \\ \hline AMD: Acer Predator BiFrost Radeon RX 7600 & 1049 & 8 & 64 (AI cores) & 165 \\ \hline Intel: ASRock Intel ARC A310 Low Profile & 415 & 4 & 96 (XMX Engines) & 75 \\ \hline Nvidia: MSI GeForce RTX 4060 Ventus 2X Black & 1257 & 8 & 96 (Tensor Cores) & 115 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Prices and specifications of the cheapest models of current generation consumer graphics cards. carried out as described in Section 2.3.2, while the final results based on the weighted scoring method are presented in the last row of Table 11, following the procedure described in Section 2.3.1. The highest score of 90.94 was achieved by the Nvidia graphics card, achieving the maximum normalized value in 17 out of 21 criteria. The only criterion in which this card showed by far the weakest performance was the threshold for entering the technology. The high initial cost of this card means that choosing this solution is associated with increased financial outlays to equip additional workstations with lower-segment devices. Despite this, a clear advantage in criteria related to energy efficiency, computing performance, \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Normalized Performance**} & \multicolumn{2}{c}{**Weight**} & \multicolumn{2}{c}{**Weighted Performance**} \\ \hline  & **AMD** & **Intel** & **Nvidia** & & **AMD** & **Intel** & **Nvidia** \\ \hline \multicolumn{2}{c}{**Compatibility**} & & & & & & \\ \hline Pytorch & 1.00 & 0.50 & 1.00 & 4.00 & 4.00 & 2.00 & 4.00 \\ \hline Tensorflow & 1.00 & 0.50 & 1.00 & 4.00 & 4.00 & 2.00 & 4.00 \\ \hline JAX & 1.00 & 0.50 & 1.00 & 4.00 & 4.00 & 2.00 & 4.00 \\ \hline SciKit & 0.00 & 0.50 & 1.00 & 4.00 & 0.00 & 2.00 & 4.00 \\ \hline ONNX & 1.00 & 0.50 & 1.00 & 4.00 & 4.00 & 2.00 & 4.00 \\ \hline Opencv & 1.00 & 0.50 & 1.00 & 4.00 & 4.00 & 2.00 & 4.00 \\ \hline Numba & 0.50 & 0.50 & 1.00 & 4.00 & 2.00 & 2.00 & 4.00 \\ \hline \multicolumn{2}{c}{**Idle Power Draw**} & 0.41 & 0.16 & 1.00 & 8.00 & 3.25 & 1.27 & 8.00 \\ \hline \multicolumn{2}{c}{**Training Energy Efficiency**} & & & & & & \\ \hline YOLO 11 PyTorch & 0.24 & 0.19 & 1.00 & 6.00 & 1.46 & 1.14 & 6.00 \\ \hline ResNet50 PyTorch & 0.50 & 0.17 & 1.00 & 6.00 & 3.00 & 1.00 & 6.00 \\ \hline MobileNetV2 TensorFlow & 0.38 & 0.18 & 1.00 & 6.00 & 2.30 & 1.10 & 6.00 \\ \hline ResNet50 TensorFlow & 0.44 & 0.22 & 1.00 & 6.00 & 2.65 & 1.30 & 6.00 \\ \hline \multicolumn{2}{c}{**Inference Energy Efficiency**} & & & & & & \\ \hline YOLO 11 PyTorch & 0.38 & 0.37 & 1.00 & 4.00 & 1.53 & 1.47 & 4.00 \\ \hline ResNet50 PyTorch & 0.35 & 0.31 & 1.00 & 4.00 & 1.38 & 1.22 & 4.00 \\ \hline MobileNetV2 Tensorflow & 1.00 & 0.58 & 0.57 & 4.00 & 4.00 & 2.33 & 2.28 \\ \hline ResNet50 Tensorflow & 1.00 & 0.49 & 0.69 & 4.00 & 4.00 & 1.95 & 2.75 \\ \hline YOLO 11 ONNX & 0.29 & 0.48 & 1.00 & 4.00 & 1.15 & 1.93 & 4.00 \\ \hline ResNet50 ONNX & 0.46 & 0.05 & 1.00 & 4.00 & 1.84 & 0.20 & 4.00 \\ \hline YOLO v4 OpenCV & 0.06 & 1.00 & 0.82 & 4.00 & 0.26 & 4.00 & 3.27 \\ \hline EfficientNet B0 OpenCV & 0.01 & 0.61 & 1.00 & 4.00 & 0.02 & 2.46 & 4.00 \\ \hline \multicolumn{2}{c}{**Technology Entry Threshold**} & 0.40 & 1.00 & 0.33 & 8.00 & 3.16 & 8.00 & 2.64 \\ \hline \multicolumn{2}{c}{Total:} & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Summary of standardized results and weights. and compatibility with popular frameworks made this card the most effective in the overall ranking. The second-place ranking was taken by the AMD graphics card, with a score of 52.00 points. The main differences to the detriment of this solution were observed in the criteria related to the energy efficiency of inference in popular frameworks.