With the growth in demand of Intelligent Video Analytics (IVA), NVIDIA virtual GPUs provides a secure solution while optimizing GPU utilization for inference-based deep learning applications for loss prevention, facial recognition, pose estimation, and many other use cases.

Today, containers are an easy way to deploy GPU accelerated libraries on development environment. RAPIDS is a set of DataScience libraries accelerated on GPU available today to a data scientist within an organization. Deploying RAPIDS on cloud in containers hepls democratizing access to accelerated computing. Deploying these containers in data centers on vGPUs help maximize hardware utilization and optimize budget-driven capital expenditures by sharing hardware resources among a team of users. With GPU-accelerated CUDA-X AI, data scientists can realize value from insights faster than with CPUs systems.