Maximizing GPU Utilization for AI, Virtualization, and Enterprise Workloads with MIG

About the Virtual Event

Join PNY and NVIDIA for an in-depth look at NVIDIA Multi-Instance GPU (MIG) technology featured on NVIDIA RTX PRO™ Blackwell GPUs, and how organizations are using it to maximize GPU utilization across AI, virtualization, and data center environments. As AI workloads continue to grow, businesses need ways to efficiently allocate GPU resources while maintaining predictable performance, security, and scalability. MIG addresses these challenges by enabling a single GPU to be partitioned into multiple isolated instances, allowing multiple users and workloads to run simultaneously on the same hardware.

In this session, we'll explore how MIG helps organizations improve infrastructure efficiency, reduce costs, and accelerate AI adoption by delivering dedicated GPU resources to multiple applications and users. Attendees will also learn how technologies like MIG can be evaluated and validated through resources such as the PNY AI Lab, helping teams test AI workflows and infrastructure strategies before deployment.

TOPICS TO BE COVERED

• What Multi-Instance GPU (MIG) is and how it enables a single NVIDIA RTX PRO Blackwell GPU to be partitioned into multiple isolated GPU instances
• How MIG improves GPU utilization, resource allocation, and workload consolidation across AI, machine learning, virtualization, and enterprise environments
• Understanding hardware-level isolation of compute, memory, cache, and bandwidth resources to ensure predictable application performance
• Best practices for deploying MIG in multi-user and multi-tenant environments while maintaining security and workload separation
• Real-world use cases for AI inference, data science, virtual workstations, containerized applications, and cloud-native infrastructure
• How MIG can help organizations optimize infrastructure investments and reduce total cost of ownership while scaling AI initiatives
• An overview of the PNY AI Lab and how organizations can evaluate AI infrastructure, workflows, and deployment strategies in a hands-on environment