Diversification of digital applications and workloads has driven the development of diverse datacenter architectures on ever-larger scales. These datacenters consist of complex IT, power, and cooling systems with interdependencies that influence configuration and performance. As datacenters scale and power density increase, designing realistic models becomes more difficult, particularly for research, because it requires understanding all layers of the datacenter and how they interact. Consequently, many studies rely on outdated or unrealistic designs. To support research in datacenter hardware design principles, operational dynamics, cooling mechanisms, and interactions of these facilities with the electrical grid, we have designed DCGen, a tool which can generate a variety of datacenter configurations (including IT hardware, cooling and power distribution infrastructures) at various electrical power, compute capability, and area targets.The tool captures power and space characteristics of IT, cooling, and power infrastructures at both the rack and datacenter levels, enabling modeling of power, energy, and space. DCGen leverages specific use cases such as AI training, AI inference, and cloud services, to select reference and canonical IT hardware configurations, producing realistic mixes of server types. It can target datacenter scale in terms of both power (e.g., 10 MW, 100 MW, 1 GW) and compute capability. For cooling and power distribution infrastructures, DCGen chooses components from a production equipment catalog that optimizes for space or power efficiency while meeting the datacenter capacity requirements. This tool supports research using realistic datacenter designs through ``what-if'' scenario exploration, including studies of power density evolution over time, grid interconnection capacity planning, datacenter-grid interactions, and space management.

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