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  • NVIDIA vComputeServer

    Power the Most Compute-Intensive Server Workloads with Virtual GPUs

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    Virtualize Compute for AI, Deep Learning, and Data Science

    NVIDIA Virtual Compute Server (vComputeServer) enables data centers to accelerate server virtualization with GPUs so that the most compute-intensive workloads, such as artificial intelligence, deep learning, and data science, can be run in a virtual machine (VM).

    Features

    GPU Sharing

    GPU Sharing

    GPU sharing (fractional) is only possible with NVIDIA vGPU technology. It enables multiple VMs to share a GPU, maximizing utilization for lighter workloads that require GPU acceleration.

    GPU Aggregation

    GPU Aggregation

    With GPU aggregation, a VM can access more than one GPU, which is often required for compute-intensive workloads. vComputeServer supports both multi-vGPU and peer-to-peer computing. With multi-vGPU, the GPUs aren’t directly connected; with peer-to-peer, they are through NVLink for higher bandwidth.

    Management and Monitoring

    Management and Monitoring

    vComputeServer provides support for app-, guest-, and host-level monitoring. In addition, proactive management features provide the ability to do live migration, suspend and resume, and create thresholds that expose consumption trends impacting user experiences, all through the vGPU management SDK.

    NGC

    NGC

    NVIDIA GPU Cloud (NGC) is a hub for GPU-optimized software that simplifies workflows for deep learning, machine learning, and HPC, and now supports virtualized environments with NVIDIA vComputeServer.

    Peer-to-Peer Computing

    Peer-to-Peer Computing

    NVIDIA? NVLink? is a high-speed, direct GPU-to-GPU interconnect that provides higher bandwidth, more links, and improved scalability for multi-GPU system configurations—now supported virtually with NVIDIA virtual GPU (vGPU) technology.

    ECC & Page Retirement

    ECC & Page Retirement

    Error correction code (ECC) and page retirement provide higher reliability for compute applications that are sensitive to data corruption. They’re especially important in large-scale cluster-computing environments where GPUs process very large datasets and/or run applications for extended periods.

    NVIDIA vComputeServer

    GPU Recommendations

      NVIDIA T4 NVIDIA V100 (SXM2)
    RT Cores 48 -
    Tensor Cores 320 640
    CUDA? Cores 2,560 5,120
    Memory 16 GB GDDR6 32 GB HBM2
    FP 16/FP 32 (mixed precision) 64 TFLOPS 125 TFLOPS
    FP 32 (single precision) 8.1 TFLOPS 15.7 TFLOPS
    FP 64 (double precision) - 7.8 TFLOPS
    NVLink: Number of GPUs per VM - Up to 8
    ECC and Page Retirement
    Multi-GPU per VM Up to 16 Up to 16

    Virtualization Partners

    Frequently Asked Questions

    Learn More About NVIDIA Virtual GPU Software

    View product release notes and supported third-party software products.