What is a vGPU? The Mechanics of Virtual GPUs
A vGPU enables multiple users or virtual machines to share the resources of a single physical GPU, ensuring that no individual user monopolizes the entire card. This approach is particularly efficient when numerous workloads require GPU acceleration but do not justify the cost of assigning a dedicated physical GPU to each user.
What Is a vGPU?
A virtual GPU, or vGPU, represents a specific segment of a physical GPU allocated to a virtual machine or user. By partitioning the physical hardware into distinct slices, each user is granted access to their own isolated VRAM and GPU capabilities.
For instance, a single physical GPU can host several vGPUs simultaneously. Each virtual machine perceives only its assigned portion of the GPU rather than the full physical hardware, allowing multiple users to operate concurrently on the same device.
This method differs from basic GPU sharing between applications. Here, the GPU is segmented into independent resources that can be individually assigned to specific virtual machines.
How Does vGPU Work?
Once a physical GPU is installed in a host system, virtualization software and compatible GPU technologies segment its resources into multiple virtual GPUs.
- Physical GPU: The host server contains the actual GPU hardware.
- GPU partitioning: The physical GPU is divided into multiple dedicated slices.
- Virtual machines: Each VM is allocated a specific vGPU.
- Dedicated VRAM: Each vGPU comes with its own reserved VRAM.
- Isolation: Users interact exclusively within their assigned GPU resources, without accessing another user’s vGPU.
The specific number and size of available vGPUs are determined by the underlying physical GPU and the chosen virtualization technology.
vGPU vs a Dedicated GPU
| Features | Dedicated GPU | vGPU |
|---|---|---|
| GPU allocation | A single user or VM exclusively utilizes the physical GPU. | Multiple users or VMs share one physical GPU via separate vGPUs. |
| VRAM | The user has access to the GPU's available VRAM. | Each vGPU receives its own allocated VRAM. |
| Users per GPU | Typically one. | Multiple, depending on the GPU and configuration. |
| Best suited for | Workloads that need substantial GPU resources. | Multiple workloads that need dedicated portions of a GPU. |
Dedicated GPUs are ideal when a workload requires the majority or entirety of a card's resources. Conversely, vGPUs are beneficial when multiple users need GPU acceleration but do not each require an entire physical GPU.
What Can You Use a vGPU For?
vGPUs can support a wide range of workloads that benefit from GPU acceleration. The appropriate vGPU size is determined by the specific software and workload requirements.
- AI and machine learning workloads
- 3D applications and engineering software
- Video editing
- Software development that uses GPU acceleration
- Remote workstations
- Cybersecurity and other technical workloads
For demanding tasks such as large AI models, complex video projects, or intensive 3D applications, the amount of available VRAM is a critical factor when selecting a GPU or vGPU configuration.
Why Use vGPUs in Cloud Desktops?
Cloud desktops leverage vGPUs to deliver GPU-accelerated virtual machines to multiple users from shared physical hardware. This maximizes GPU utilization, especially when individual users do not need the full capacity of a single card.
For example, a team can operate on separate virtual desktops while sharing the resources of a physical GPU through dedicated vGPU allocations. This ensures each user has their own virtual GPU and isolated VRAM, rather than contending for resources in a shared desktop environment.
Try on DaDesktop
DaDesktop offers cloud desktops equipped with dedicated GPUs and vGPU options, tailored for workloads requiring GPU acceleration. Learn more about DaDesktop cloud GPU desktops.