While the next chapter addresses cloud platforms, this one focuses on the process of building and managing your own hardware. An on-premise setup provides direct control over performance, security, and configuration, but it also requires a detailed understanding of how physical components integrate into a cohesive system. This approach involves balancing initial capital expenditure (CapEx) against long-term operational efficiency.

In this chapter, you will learn to translate machine learning workload needs into concrete hardware specifications. We will cover the selection of server chassis, motherboards, and GPUs, paying close attention to interconnect technologies like NVLink and their effect on multi-GPU performance. The choice of interconnect directly impacts the communication overhead in distributed tasks, a key factor in the total training time equation, $T_{total} = T_{compute} + T_{communication}$. We will also address critical support systems, including high-speed storage configurations with NVMe drives, networking for fast data access, and the significant requirements for power and cooling.

By the end of this chapter, you will be able to assess your requirements, select appropriate components, and plan for the physical deployment of a dedicated AI server. You will put this into practice by creating a detailed hardware specification sheet for a given workload scenario.