## 1 Introduction
As a result, there is a noticeable gap in systematic, multi-criteria analyses of consumer-grade GPUs, especially those that prioritize real-time performance, energy efficiency, and affordability. In most cases, the tests involve advanced GPU hardware architectures that are accessible to a small group of researchers. The second gap is the lack of sustainability of the tests. Given the constant technological development of GPUs, developing a methodology for their post-comparison, taking into account a wide range of criteria, would make it possible to bring a sustainable aspect to research work. Filling the above gaps should ensure that practitioners and researchers can choose hardware configurations best suited to the performance, energy consumption, and cost constraints in the ever-changing consumer card environment. The motivation for undertaking the work in this article was to fill the aforementioned research gaps in the field:  
* Consumer-grade GPUs: high-end GPUs are not always available or necessary for medium-sized research labs, small and medium-sized companies, or individuals developing real-time systems. * There is a lack of practical multi-criteria analysis that systematically compares consumer card GPUs along multiple dimensions (performance, cost of entry, power consumption, and broader compatibility), which are critical factors in real system solutions. This is especially true for methodologies that make a lasting contribution to the post-comparison process of graphics cards. * Requirements for specific applications: many of the existing comparisons are not directly applicable to real-time or near-real-time tasks, where both the speed of conversion and energy efficiency are most important (e.g., USV-type autonomous navigation systems). The aim of the article was to conduct a comparative analysis of the usefulness of consumer graphics cards in the process of deep learning in the detection of navigational marks in inland navigation. The analysis was based on the WSM (weighted scoring method) [48], which allows many criteria to be taken into account when assessing the efficiency and effectiveness of the tested devices. The criteria used included compatibility, quiescent power, energy efficiency of the learning process, energy efficiency of the inference process, and the threshold for entering the technology. A total of 21 features of graphics cards were analyzed. The study included devices from three leading manufacturers, AMD, Intel, and Nvidia, tested on a set of image data related to the detection of notice marks. The main contributions of the present work are as follows:  
1. Methodological contribution: We introduce a multi-criteria framework to objectively evaluate GPUs using 21 different characteristics, including performance in the learning and inference phases and energy efficiency. The framework provides a repeatable and robust approach that others can adapt and extend to different tasks or hardware configurations. 2. Application to inland navigation: We demonstrate the practical utility of our approach by applying it to the detection of navigational marks in inland navigation. Although navigational sign detection serves as a case study, the methodology and findings can be generalized to many real-time image-based tasks where timely and efficient processing is crucial. 3. Sustained comparison value: Covering devices from three leading GPU manufacturers, AMD, Intel, and Nvidia, our comparison provides a sustainable benchmark for researchers and practitioners seeking consumer-oriented GPU solutions. Because we evaluate factors such as power consumption and compatibility that are not quickly becoming obsolete, our analysis maintains validity beyond mere peak performance metrics.