Skip to content

NVIDIA vGPU Integration with VergeOS#

Overview#

The NVIDIA integration with VergeOS enables GPU acceleration for virtual machines through NVIDIA's GRID vGPU technology. This integration allows multiple VMs to share physical GPU resources while maintaining performance isolation, making it ideal for AI/ML workloads, virtual desktop infrastructure, content creation, and high-performance computing applications.

Key Features#

  • Shared GPU Resources: Multiple VMs can access a single physical GPU with guaranteed isolation and performance
  • Flexible vGPU Profiles: Choose from workload-optimized configurations (A-series for VDI, B-series for AI/ML, Q-series for professional graphics)
  • Live Migration Support: Move GPU-accelerated VMs between nodes without downtime (experimental in 4.13+)
  • Enterprise Management: Centralized GPU resource allocation and monitoring through VergeOS interface
  • Hardware Virtualization: Support for Tesla, Quadro, and RTX series GPUs with vGPU capability

Integration Benefits#

  • Cost Optimization: Maximize GPU hardware utilization across multiple workloads instead of dedicated GPU per VM
  • Simplified Management: GPU resources managed through familiar VergeOS resource groups and policies
  • Enhanced Security: Hardware-level isolation between GPU workloads with tenant separation
  • Operational Efficiency: Unified platform for both traditional and GPU-accelerated virtual machines
  • Scalable Performance: Dynamic resource allocation based on workload demands and priorities

Supported Use Cases#

Artificial Intelligence and Machine Learning:

  • Deep learning model training and inference with TensorFlow, PyTorch, and RAPIDS
  • GPU-accelerated data science workflows and Jupyter notebook environments
  • Large-scale analytics and real-time inference deployment

Virtual Desktop Infrastructure:

  • Professional workstations for CAD, engineering, and design applications
  • Graphics-accelerated business applications for remote workers
  • Multi-monitor support with high-resolution displays

Content Creation and Media:

  • Video editing, rendering, and post-production workflows
  • 3D graphics, animation, and visual effects creation
  • Live streaming and broadcasting applications

High-Performance Computing:

  • Scientific computing and simulation workloads
  • Financial modeling and risk analysis
  • Research applications in genomics, astronomy, and materials science

Architecture#

The NVIDIA integration consists of three main layers:

Physical Layer:

  • NVIDIA GPU hardware (Tesla, Quadro, RTX series with vGPU support)
  • NVIDIA GRID host drivers installed on VergeOS nodes
  • IOMMU/SR-IOV hardware virtualization support

Virtualization Layer:

  • VergeOS resource groups for GPU device management
  • Configurable vGPU profiles for different workload types
  • Policy-based resource allocation and scheduling

Application Layer:

  • NVIDIA guest drivers installed in virtual machines
  • CUDA runtime and development libraries
  • Integration with AI frameworks and professional applications

Supported Hardware#

Tesla Series (Data Center):

  • Tesla T4, T10, T20 - Optimized for AI inference and VDI
  • Tesla V100, A100, H100 - High-performance training and HPC
  • Tesla P4, P40, P100 - General-purpose GPU acceleration

Quadro Series (Professional Graphics):

  • Quadro RTX 4000, 5000, 6000, 8000 - Professional workstation graphics
  • Quadro P4000, P5000, P6000 - CAD and engineering applications

RTX Series (Mixed Workloads):

  • RTX A4000, A5000, A6000 - Professional content creation
  • RTX 4080, 4090 - High-end workloads (select models)

Hardware Requirements

  • NVIDIA vGPU functionality requires a valid NVIDIA vGPU software license, such as Virtual PC, Virtual Apps, or Virtual Workstation. These licenses must match the selected vGPU profile and workload type.
  • Only select data center and professional GPUs support vGPU. Verify hardware and driver compatibility in the NVIDIA vGPU Support Matrix.
  • For an overview of licensing options, refer to the NVIDIA Licensing Guide.

Software Ecosystem Integration#

NVIDIA AI Platform:

  • NVIDIA NGC container catalog with pre-optimized AI frameworks
  • CUDA development environment and cuDNN deep learning libraries
  • TensorRT inference optimization and RAPIDS data science acceleration

Development and Monitoring:

  • Docker and Kubernetes GPU orchestration support
  • Integration with popular ML frameworks (TensorFlow, PyTorch, Jupyter)
  • GPU monitoring through NVIDIA Management Library (NVML)

Professional Applications:

  • ISV-certified drivers for CAD and engineering software
  • NVIDIA Omniverse for collaborative 3D content creation
  • Professional visualization and VR/AR development tools

vGPU Profile Types#

Profile Series Primary Use Case Memory Range CUDA Support Displays
A-Series Virtual Applications 1-24GB Limited 4
B-Series AI/ML Compute 1-48GB Full 0
Q-Series Professional Graphics 1-48GB Full 4
M-Series Mixed Workloads 1-8GB Full 2

Implementation Resources#

Getting Started#

Advanced Configuration#

External Documentation#

Support#

For assistance with NVIDIA integration:

  • Implementation Support: Detailed setup instructions available in the [NVIDIA vGPU Implementation Guide]
  • VergeOS Support: Contact VergeOS support for platform-specific integration issues
  • NVIDIA Support: Access NVIDIA enterprise support for GPU hardware and driver questions
  • Community Resources: VergeOS and NVIDIA community forums for best practices and troubleshooting

GPU Acceleration Ready

NVIDIA integration with VergeOS delivers enterprise-grade GPU acceleration while maintaining the flexibility and efficiency of virtualized infrastructure. Start with the implementation guide to deploy GPU-accelerated workloads in your environment.