With NVIDIA's 2019 GTC event right around the corner, our thoughts focus on the extreme innovation for which NVIDIA has become known, and how to solve the resource challenges that sometimes accompany these groundbreaking advancements. Advancing IT workloads like AI and analytics requires cutting-edge hardware and specialized skills, not only from the teams who use them, but from the data centers and platforms which host them. These compute- and data-heavy workloads are designed to provide answers to some of our world's biggest challenges. To get there, a new generation of tools bring increased requirements for physical resources like power and cooling, in addition to coming in smaller form factors, which results in greater overall density. This combination of capability and resource demand is often met by data center constraints as facilities struggle to meet airflow, power, and cooling requirements.

So, you want to scale your computing muscle to train bigger deep learning models. Can your data center handle it? According to Nvidia, which sells more of the specialized chips used in machine learning than any other company, it most likely cannot. These systems often consume so much power, a conventional data center doesn't have the capacity to remove the amount of heat they generate. It's easy to see how customers without infrastructure that can support a piece of Nvidia hardware is a business problem for Nvidia.