Enabling GPU-as-a-Service

The ability to leverage data in today's computationally intensive business environment is essential for a business's success. As AI adoption in the enterprise grows, Hewlett Packard Enterprise delivers the compute and storage power needed to meet the challenges posed by machine learning (ML), deep learning (DL), and advanced data analytics. Now, HPE offers a new GPU-as-a-service (GPUaaS) solution for on-premises deployments.

GPU-accelerated workloads for enterprise AI deployments

The development of ML and DL predictive models is compute intensive. The use of accelerators such as graphics processing units (GPUs) provides a performance boost that significantly speeds up development as compared to CPU-only systems making GPUs a common infrastructure choice for ML and DL.
However, in most enterprises today, IT teams find it challenging to meet the growing demand for GPUs from multiple data science teams for multiple different ML/DL applications and use cases.
Furthermore, the complexity in standing up the right software components together with the underlying infrastructure is very time consuming and the process has to be repeated each time a new ML/DL application is requested.