The rapid expansion of global data infrastructure has significantly increased the production of high-performance AI servers. These units are characterized by a high density of internal components, where the correct placement and seating of every part are vital for the system to function. Because these servers are visually crowded, traditional industrial inspection systems often struggle to distinguish between correctly installed parts and defects. This study presents an automated optical inspection framework using the You Only Look Once (YOLO) algorithm to solve this problem by verifying the final product assembly through computer vision. The methodology involves a comprehensive analysis of multiple YOLO model scales. Nano-, medium-, and large-scale algorithms are tested to assess their effectiveness in evaluating fully assembled products. This approach identifies the most reliable configuration for recognizing intricate component features and assembly defects in a high-density environment. Experimental results demonstrate that larger models provide the necessary accuracy, while smaller models offer the high throughput required for rapid line speeds. Ultimately, this framework provides a standardized verification protocol that minimizes human dependency in quality audits. By automating the identification of assembly defects, the system ensures consistent inspection throughput and prevents defective units from progressing to the final testing phase, thereby offering a scalable solution that improves the reliability of high-performance computing hardware relative to standard industrial methods.