Enterprises are looking to simulation and modeling, artificial intelligence (AI), and big data analytics to help them achieve breakthrough discoveries and innovation. They understand these workloads benefit from a high performance computing (HPC) infrastructure, yet they might still believe that separate HPC, AI, and big data clusters are the best choice for running these workloads. Contributing ...to this belief are two challenges. The first challenge is a fundamental diﬀerence in how workloads request resources and how HPC systems allocate them. AI and analytics workloads request compute resources dynamically, an approach that isn't compatible with the batch scheduling software used to allocate system resources in HPC clusters. The second challenge is the pattern of using computing systems based on graphics processing units (GPUs) as dedicated solutions for AI workloads. Enterprises might not realize that adding these workloads to an existing HPC cluster is feasible without the use of GPUs.