This combination of leading-edge technologies makes it possible to adopt the latest NVIDIA Ampere GPUs using the predictability and security of vSphere for virtualization with VMware-optimized infrastructure. This validated design provides the following key benefits:
- No siloed infrastructure for AI—Customers can use the same data center tools and processes with which they are familiar for building and operating AI infrastructure. With integration to the VMware ecosystem, customers can avoid silos of AI-specific systems that are difficult to manage and secure. They can also mitigate the risks of shadow AI deployments, where data scientists and machine learning engineers procure resources outside of the IT ecosystem.
- Consistent tools for management and operations—GPU resources can now be virtualized similarly to CPU, memory, network, and storage resources. This virtualization and container orchestration allows IT administrators to use the same tools for management and operations for both their AI workloads and other data center workloads.
- AI workload orchestration—Through integration with NVIDIA AI Enterprise and VMware Tanzu, this validated design enables automation of the AI workload’s life cycle, including provisioning, deployment, scaling, networking, and load balancing. Administrators can now simplify their complex AI deployment through production-grade Kubernetes container orchestration.
- Curated end-to-end AI software with Enterprise grade support—The NVIDIA AI Enterprise software suite includes AI and data science tools and frameworks that are packaged as containers for easy and rapid deployment. These containers support end-to-end AI development and are validated on VMware vSphere. NVIDIA Support Services for the NVIDIA AI Enterprise software suite provides access to comprehensive software patches, updates, upgrades, and technical support. These services help customers with an easy and reliable way to improve productivity and reduce downtime for their AI infrastructure.
- Near bare-metal performance and scalability—AI workloads can run at near bare-metal performance on virtualized GPUs. These workloads can scale across multiple GPUs and multiple nodes, allowing training of even the largest deep learning models.