To truly grasp the intricate composition of an AI server, disassembling its hardware provides invaluable insight into its printed circuit board (PCB) architecture. The most compelling option we have found for companies. Operators across the IT asset disposition (ITAD) sector say the next wave of decommissioned systems will differ significantly from earlier generations of enterprise hardware because of the concentration of high-value components packed into AI-focused server racks, including graphic processing units. This article explains the internal PCB composition of an AI server by disassembling the server hardware, so readers can gain a clearer understanding of the PCB types and their relative value within a system. The analysis focuses on representative NVIDIA DGX systems to illustrate the basic. As the world races to build massive AI data centres with GPUs scaling into the millions, what happens to the old ones? Was chatting this morning with an executive from a leading data centre operator and the topic of GPU obsolescence came up. Is anyone even thinking of this? We are relatively new to. Sustainable AI hardware design, focusing on disassembly and reuse, redefines our relationship with technology, moving from consumption to stewardship. Using the NVIDIA DGX A100 as a primary reference, given its detailed documentation, and acknowledging the similar design principles.