Artificial Intelligence | News | Insights | AiThority
[bsfp-cryptocurrency style=”widget-18″ align=”marquee” columns=”6″ coins=”selected” coins-count=”6″ coins-selected=”BTC,ETH,XRP,LTC,EOS,ADA,XLM,NEO,LTC,EOS,XEM,DASH,USDT,BNB,QTUM,XVG,ONT,ZEC,STEEM” currency=”USD” title=”Cryptocurrency Widget” show_title=”0″ icon=”” scheme=”light” bs-show-desktop=”1″ bs-show-tablet=”1″ bs-show-phone=”1″ custom-css-class=”” custom-id=”” css=”.vc_custom_1523079266073{margin-bottom: 0px !important;padding-top: 0px !important;padding-bottom: 0px !important;}”]

NVIDIA vComputeServer with NGC Containers Brings GPU Virtualization to AI, Deep Learning and Data Science

VMware, Cisco, Dell, Red Hat Among Industry Leaders That Support vComputeServer for Streamlining Deployment and Management of GPU Servers.

NVIDIA vComputeserver with NGC Containers Brings GPU Virtualization to AI, Deep Learning and Data ScienceToday NVIDIA announced that its virtual GPU (vGPU) technology, which has already transformed virtual client computing, now supports server virtualization for AI, deep learning and data science. Previously limited to CPUs, now AI workloads can 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.

vComputeServer gives data center administrators the option to run AI workloads on GPU servers in virtualized environments for improved security, utilization and manageability. IT administrators can use hypervisor virtualization tools like VMware vSphere, including vCenter and vMotion, to manage all their data center applications, including AI applications running on NVIDIA GPUs. Many companies deploy GPUs in the data center, but GPU-accelerated workloads such as AI training and inferencing run on bare metal. These GPU servers are often isolated, with the need to be managed separately. This limits utilization and flexibility.

Read More: NVIDIA RTX On: New Wave of Blockbuster Games Showcase Ray Tracing at Gamescom

With vComputeServer, IT admins can better streamline management of GPU accelerated virtualized servers while retaining existing workflows and lowering overall operational costs. Compared to CPU-only servers, vComputeServer with four NVIDIA V100 GPUs accelerates deep learning 50 times faster, delivering performance near bare metal.

NVIDIA vComputeserver with NGC Containers Brings GPU Virtualization to AI, Deep Learning and Data ScienceToday’s announcement brings support to VMware vSphere along with existing support for KVMbased hypervisors including Red Hat and Nutanix, allowing admins to use the same management tools for their GPU clusters as they do for the rest of their data center.

Virtual GPUs Boost Performance for Any Workload

By expanding the vGPU portfolio with NVIDIA vComputeServer, NVIDIA is adding support for data analytics, ML, AI, deep learning, HPC and other server workloads. The vGPU portfolio also includes virtual desktop offerings – NVIDIA GRID Virtual PC and GRID Virtual Apps for knowledge workers and Quadro Virtual Data Center Workstation for professional graphics.

Read More: Daily AI Roundup: The 5 Coolest Things on Earth Today

Related Posts
1 of 20,123

NVIDIA vComputeServer provides 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 results in maximized utilization and affordability.

Features of vComputeServer include:

  • GPU Performance: Up to 50 times faster DL training than CPU-only, similar performance to running GPU 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, VM 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 NGC Adds Support for VMware vSphere 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.

RAPIDS offers a range of open-source libraries to accelerate the entire data science pipeline including data loading, ETL, model training, and inference, enabling data scientists to get their work done more quickly and significantly expand the type of models they are able to create.

All NGC software can be deployed on virtualized environments like VMware vSphere with vComputeServer. IT administrators can use hypervisor virtualization tools like VMware vSphere to manage all their NGC containers in VMs running on NVIDIA GPUs. In addition, NVIDIA helps IT roll out GPU servers faster in production with validated NGC-Ready servers.

Read More: Salesforce Completes Acquisition of Tableau

And enterprise-grade support provides users and administrators with direct access to NVIDIA’s experts for NGC software, minimizing risk and improving productivity.

Leave A Reply

Your email address will not be published.