Buyer Expectations for Short-Term Growth in Spending for HPC Servers, Public Clouds, and Desktops

Since the dawn of the Supercomputer Era in the 1960s, a succession of base processors (CPUs) has attempted to satisfy HPC users' nearly insatiable demands for fast solution times on a wide spectrum of challenging problems, combined with affordability and energy efficiency. Chief among these entrants were vector processors, which ruled the HPC market through the 1990s, then RISC processors, and then x86-based processors, which began their steep ascendancy at that time and continue to dominate the market today, often in tandem with GPUs or other accelerators.

The purpose of this study was to gain a better understanding of the activities and use behaviors of AI users leveraging HPC-centric cloud resources. Key goals included creating a picture of user goals for AI integration, their current and planned methodologies, budget allocations, model lifecycle expectations, and preferred hardware and cloud platforms for their HPC-centric AI endeavors. This study also sought to gain a greater understanding of inferencing activities among organizations currently or planning to integrate AI into their workflow. Highlights from the study results include: •     Public cloud resources are considered a valuable asset in exploration and integration of AI into HPC or compute-intensive environments. •     Respondent organizations are leveraging a wide range of public cloud offerings. •     There are numerous architectures and device types currently used to meet inferencing needs. •     There is a plethora of desired qualities for the future of cloud computing expected by current and prospective AI users. •     Budgets among respondent organizations are expected to increase to meet training and inference needs, both in the cloud and on-premises.