What Is a Virtual GPU?

A virtual GPU (vGPU) is a technology that simulates the functionality of a physical graphics processing unit (GPU) within a virtualized environment. This allows multiple virtual machines (VMs) to share a single GPU, optimized for handling graphical tasks such as rendering images and processing complex computations.

Unlike traditional setups where each GPU is dedicated to a single machine, vGPU technology enables more efficient use of resources by distributing GPU power across multiple VMs. This setup helps in achieving higher performance without the need for additional hardware. Through shared usage, vGPUs ensure greater availability of resources.

By emulating physical GPU functions, vGPUs make graphical processing accessible in virtual environments. Organizations can leverage these capabilities for virtual desktop infrastructure (VDI), cloud-based applications, and other demanding tasks. This forms a crucial part of modern IT infrastructure, enabling high-end graphics processing on demand.

This is part of a series of articles about GPU applications.

Benefits of Virtual GPUs

Virtual GPUs offer a range of advantages that enhance performance, scalability, and cost efficiency in virtualized environments:

  • Improved resource utilization: vGPUs allow multiple virtual machines to share a single physical GPU, maximizing the usage of hardware resources. This leads to better allocation of processing power across tasks, reducing waste and increasing efficiency.
  • Cost efficiency: By enabling resource sharing, vGPUs eliminate the need for dedicated GPUs for each VM. This reduces hardware expenses and overall operational costs while delivering comparable performance.
  • Enhanced scalability: Organizations can scale their virtual environments more easily by allocating GPU resources dynamically as per workload requirements. This adaptability makes it easier to handle fluctuating demands without over-provisioning.
  • Support for high-performance applications: vGPUs enable virtual machines to handle graphically intensive applications, such as CAD tools, machine learning models, and 3D rendering. This ensures that users experience smooth and responsive performance even in demanding scenarios.
  • Centralized management: Administrators can monitor and manage GPU resources centrally within a virtualized infrastructure. This simplifies maintenance, troubleshooting, and performance tuning.

Related content: Read our guide to GPU for rendering

How Virtual GPUs Work

The foundation of GPU virtualization starts with the hypervisor. In a virtual environment, the hypervisor manages the distribution of hardware resources to various virtual machines. When using vGPU technology, a physical GPU is partitioned into several virtual instances. Each instance acts as a discrete GPU to the VM it is assigned to.

The process involves a graphics driver installed on the guest operating system and a vGPU manager running within the hypervisor, such as VMware ESXi. The manager intercepts commands from the VM and schedules them on the physical GPU. This ensures that each VM receives its allocated share of GPU resources without interfering with other instances. This architecture supports both Microsoft Windows and Linux environments, making it versatile for various applications.

NVIDIA Virtual GPU Solutions

NVIDIA leads the market with its comprehensive vGPU software stack. The NVIDIA virtual GPU ecosystem provides several distinct profiles designed for different types of users. These profiles allow administrators to customize the user experience by defining the amount of frame buffer and the number of supported displays.

NVIDIA vGPU Software Tiers

NVIDIA classifies its offerings into specific editions to meet diverse needs:

  • NVIDIA Virtual PC: Designed for knowledge workers using office productivity applications and web browsers. It provides a smooth virtual desktop experience.
  • NVIDIA Virtual Workstations: Built for professional designers, architects, and engineers using compute intensive applications like CAD or 3D modeling.
  • NVIDIA Virtual Compute Server: Tailored for data scientists and researchers running GPU workloads and machine learning simulations in a data center.

By using these specific software editions, organizations can ensure that every user has the right level of performance, from basic desktop tasks to high performance engineering simulations.

NVIDIA vGPU Architecture and Profiles

The core of the NVIDIA vGPU system is the ability to split a single physical Nvidia GPU into multiple Virtual GPU (vGPU). Each vGPU has a fixed amount of GPU memory. For example, an NVIDIA RTX card with 24GB of memory could be partitioned into six virtual instances of 4GB each.

These profiles are not just about memory; they also dictate the type of tasks the virtual machine can perform. Some profiles prioritize graphics rendering, while others focus on compute intensive workloads like AI training. When an IT team deploys a vGPU, the guest VM uses the native NVIDIA driver, which allows for near native performance and access to the latest features of the hardware.

GPU Virtualization Techniques

There are several ways to provide GPU resources to a virtual machine. The choice of technique depends on the required performance level and the number of users sharing the hardware.

API Remoting

API remoting is a software-based approach where the graphics API calls (such as OpenGL or DirectX) are intercepted at the guest OS level. These calls are then sent over the network to a server that has a physical GPU, which processes the request and sends the rendered frames back.

This technique is useful for office productivity applications and basic tasks. However, it often results in lower performance compared to other methods because of the overhead of intercepting and redirecting API calls. It is rarely used for demanding workloads due to increased latency.

Pass Through

In a pass through configuration, a single physical GPU is mapped directly to a single virtual machine. The hypervisor steps aside, allowing the guest OS to have exclusive control over the hardware.

Advantages of Pass Through:

  • Bare metal performance: Since there is no virtualization layer, the VM performs exactly like a physical machine.
  • Low latency: Ideal for the most compute intensive applications.
  • Full hardware support: The multiple VMs can access every feature of the GPU.

The primary disadvantage is the lack of scalability, because the GPU is dedicated to one VM, you cannot share it among multiple users, which reduces cost efficiency.

Mediated Pass Through

Mediated pass through is the most advanced technique and serves very common in shared GPU environments. It pairs the speed of direct hardware access with the ability to share a single physical card across many users. Setting this up is difficult and requires specific hardware support, but it is the best way to deliver high-end graphics to multiple virtual machines from a single hardware resource.

In this model, the vGPU manager creates a mediated device that represents a portion of the physical GPU. The guest VM interacts with this mediated device using a native driver. This allows for high performance while still enabling multiple virtual machines to share the same physical hardware. If your hosting provider sells GPUs in 1/8 or 1/4 plans, mediated pass through is most likely being used.

Use Cases for Virtual GPUs

Virtualizing GPU resources is no longer just for high-end rendering. It has become a standard requirement for several industries.

Machine Learning and AI Workloads

Data science requires massive parallel processing power. By using virtual GPUs, organizations can create multiple environments for AI training and machine learning without purchasing a separate physical server for every user.

IT teams can spin up virtual machines with specific GPU resources, run a training job, and then reallocate those resources once the job is complete. This flexibility is essential for managing the high costs associated with high performance hardware like the NVIDIA S40 or A100 series.

Games Development

Game development involves constant iteration on graphics-heavy assets. Developers and artists can use virtual workstations to access powerful GPU hardware from any location. This allows teams to collaborate on game development projects using existing infrastructure while maintaining data security, as the source files and assets remain in the data center rather than on local devices.

Virtual Desktop infrastructure

As modern applications become more graphics-heavy, even standard knowledge workers require GPU acceleration. Web browsers, video conferencing tools, and video playback all benefit from virtual GPUs. Implementing NVIDIA Virtual PC ensures that the virtual desktop remains responsive, which improves user satisfaction and productivity.

Hardware and Infrastructure Considerations

To successfully deploy virtual GPUs, the underlying hardware must support specific virtualization features. This includes Single Root I/O Virtualization(SR-IOV) and appropriate BIOS settings.

GPU Selection

The choice of physical GPU determines the density of the virtual environment. High-end data center GPUs are designed to support dozens of concurrent users.

When selecting GPU hardware, its important to consider:

  • Total GPU memory: This is usually the limiting factor for the number of virtual machines.
  • Compute cores: More cores allow for faster processing of AI workloads and rendering tasks.
  • Thermal and power limits: Data center GPUs are built for 24/7 operation under demanding workloads.

VMWare ESXi and Hypervisor Support

The hypervisor plays a critical role in managing GPU resources. VMware ESXi is widely regarded as a leading platform for vGPU deployment. It provides robust tools for monitoring GPU utilization and fine tuning the allocation of resources. IT teams can monitor temperature, power consumption, and memory usage for each vGPU instance directly from the management console.

Maximizing Performance and Efficiency

Achieving optimal performance in a virtualized environment requires careful configuration and ongoing management.

Minimize Latency

Latency is the enemy of a good user experience, especially in virtual desktops and game development. To minimize latency, ensure that the network infrastructure can handle the high bandwidth required for streaming graphics data. Using high-speed interconnects and optimizing the display protocol settings can significantly improve responsiveness.

Resource Efficiency and Utilization

One of the main goals of virtualization is to avoid wasted resources. Administrators should regularly review GPU utilization metrics. If a group of virtual machines is consistently using only 10% of their allocated GPU power, those vGPU profiles can be downsized, allowing more users to fit on the same physical GPU hardware.

Data Security

Using virtual GPUs enhances data security by keeping sensitive data in the data center. Because only the pixels are sent to the end-user device, the actual data never leaves the secure server environment. This is particularly important for industries like healthcare and finance where data protection is a primary concern.

Implementation Strategies for IT Teams

Successfully integrating virtual GPUs into an existing infrastructure requires a phased approach. Start by identifying the specific workload requirements of your users.

  1. Assess User Needs: Determine if your users are knowledge workers, professional designers, or data scientists.
  2. Select the Right Software: Choose the appropriate NVIDIA vGPU software edition (Virtual PC, Workstation, or Compute Server).
  3. Test with a Pilot Program: Deploy a small number of VMs to test application performance and fine tuning settings before a full rollout.
  4. Monitor and Scale: Use hypervisor tools to track performance and scale the environment as demand grows.

GPU Accelerated Applications

A wide range of software now supports or requires GPU acceleration to function correctly. This includes everything from the Adobe Creative Suite to specialized CAD software and machine learning frameworks like TensorFlow or PyTorch. By providing virtual GPUs, you ensure that these various applications running in your cloud or on-premise servers have the necessary power to perform at their best.

High performance computing is no longer restricted to those with physical access to specialized workstations. Through the intelligent application of vGPU technology and mediated pass through techniques, organizations can provide bare metal performance to users anywhere in the world. This approach balances the need for high-end compute power with the practical requirements of cost efficiency and centralized management.

As the demand for AI and complex data analysis increases, the role of the virtual GPU will only grow. By understanding the underlying technology and following best practices for deployment, IT teams can build a scalable, efficient, and powerful computing environment that meets the needs of any modern workload.

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