Today NVIDIA announced its new vComputeServer software. Through a partnership with VMware, these new technologies are said to bring GPU virtualization to AI, Deep Learning, and Data Science. Customers will be able to seamlessly migrate AI workloads on GPUs between customer data centers and VMware Cloud on AWS. The announcement today not only supports VMware vSphere, but also KVM-based hypervisors including Red Hat and Nutanix.
Today NVIDIA announced its new vComputeServer software. Through a partnership with VMware, these new technologies are said to bring GPU virtualization to AI, Deep Learning, and Data Science. Customers will be able to seamlessly migrate AI workloads on GPUs between customer data centers and VMware Cloud on AWS. The announcement today not only supports VMware vSphere, but also KVM-based hypervisors including Red Hat and Nutanix.
AI, Deep Learning, and Data Science are compute-intensive server workloads. Up until now these workloads were limited to CPU-only. Now with vComputeServer software and NVIDIA NGC, AI workloads can be easily deployed on virtualized environments like VMware vSphere. This works by allowing admins to run AI workloads on GPU servers in virtualized environments. NVIDIA states that this will improve security, utilization, and manageability. Leveraging vComputeServer with four NVIDIA V100 GPUs accelerates deep learning 50x faster than CPU-only servers.
The release of vComputeServer expands NVIDIA’s vGPU portfolio to encompass support for data analytics, machine learning, AI, deep learning, HPC and other server workloads. The new software also provides several features like GPU sharing, so multiple virtual machines can be powered by a single GPU, and GPU aggregation, so one or multiple GPUs can power a virtual machine. This allows for maximum utilization while leverage existing technology in a cost effective manner.
Features of vComputeServer include:
- GPU Performance: Up to 50x faster deep learning training than CPU-only, similar performance to running GPU on bare metal.
- Advanced compute: Error correcting code and dynamic page retirement prevent against data corruption for high-accuracy workloads.
- Live migration: GPU-enabled virtual machines can be migrated with minimal disruption or downtime.
- Increased security: Enterprises can extend security benefits of server virtualization to GPU clusters.
- Multi-tenant isolation: Workloads can be isolated to securely support multiple users on a single infrastructure.
- Management and monitoring: Admins can use the same hypervisor virtualization tools to manage GPU servers, with visibility at the host, virtual machine and app level.
- Broad Range of Supported GPUs: vComputeServer is supported on NVIDIA T4 or V100 GPUs, as well as Quadro RTX 8000 and 6000 GPUs, and prior generations of Pascal-architecture P40, P100 and P60 GPUs.
Availability
NVIDIA vComputeServer is expected to be available this month.
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