With AI and machine learning being an increasingly indispensable part of modern computing, Nvidia’s business fundamentals remain strong. They have spearheaded their effort to be a prominent force in the server business forging partnerships with multiple tech companies. Recently Nvidia and VMware announced a tie-up, which will bring Nvidia’s vGPU (Virtual GPU Technology) to VMware’s vSphere stack on AWS.

GPUs are designed with data-parallel computing in mind, which accelerates vector and matrix operations. This makes them ideal for AI workloads.

Previously limited to CPU-only, AI workloads can now be easily deployed on virtualized environments like VMware vSphere with new vComputeServer software and NVIDIA NGC. Through our partnership with VMware, this architecture will help organizations to seamlessly migrate AI workloads on GPUs between customer data centers and VMware Cloud on AWS.

– ANNE HECHT (Nvidia)

GPU-accelerated workloads are often run on single-tenant physical servers, but with vComputeServer companies can run AI workloads in a  virtualized environment, this offers more flexibility and monetary savings( Up To a certain scale). Nvidia already supports a few KVM-based hypervisors, including Red Hat and Nutanix. VMware’s vSphere is the latest addition.

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 monitoringAdmins 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.

– Nvidia

VMware vSphere users will also get Nvidia GPU Cloud support, which is a GPU-accelerated cloud platform optimized for deep learning and scientific computing. Anne Hecht from Nvidia writes “NVIDIA NGC, our hub for GPU-optimized software for deep learning, machine learning and HPC, offers over 150 containers, pre-trained models, training scripts and workflows to accelerate AI from concept to production, including RAPIDS, our CUDA-accelerated data science software”. 

VMware Partnership In-Line With Recent Aquisition

VMware is going to acquire Bitfusion, which will add a lot of value to its vSphere cloud platform. As we discussed earlier in the article, virtualization can offer companies a lot of benefits with a minimal performance hit. With Bitfusion’s technology, companies will be able to move away from bare-metal servers and virtualize their GPUs, as such an arrangement can result in better utilization of available resources. With accelerated computing taking center stage companies will look for ways to virtualize their hardware stack, VMware is well aware of this and they are moving to make the vSphere platform vital in data centers.