Organizations in the healthcare industry are adopting virtual desktop infrastructure (VDI) to benefit from centralized management, better security and compliance, and worker mobility. In the healthcare industry, graphics processing units (GPUs) are used in VDI to enhance the quality of the visual experience for medical professionals in clinical imaging. The healthcare industry uses GPUs for use cases that require advanced graphic acceleration, such as PACS imaging technology and other graphics intensive use cases. These non-diagnostic use cases can include remote radiology, remote cardiology, and digital pathology.
The healthcare industry, such as academic medical centers and research organizations, also benefits from the use of GPUs for running artificial intelligence (AI) workloads, including learning and inferencing, that use parallel computation. The healthcare industry is poised to reap the benefits of AI to improve patient outcomes, reduce costs, and speed up diagnoses.
However, organizations in the healthcare industry need to find a cost-effective and optimal method for using GPUs. Not all workloads and use cases use GPUs to the fullest capacity, and processing demand can vary with time of day, depending on the workload. Healthcare organizations are looking for a solution that provides optimal and shared use of GPUs while running mixed workloads such as VDI and AI and that increases overall GPU utilization in the data center. Using this solution eliminates the cost of investing in dedicated GPU hardware for each workload.