Understanding vGPUs: How Virtual GPUs Function
A vGPU enables multiple users or virtual machines to utilise a single physical GPU, allowing each to access shared resources rather than requiring exclusive use of the entire card. This approach is particularly beneficial when various workloads require GPU acceleration but dedicating a physical unit to every user would be inefficient.
What Is a vGPU?
A virtual GPU (vGPU) represents a designated segment of a physical GPU allocated to a specific virtual machine or user. The physical hardware is partitioned into distinct slices, ensuring each user receives isolated VRAM and dedicated GPU resources.
For instance, a single physical GPU can support multiple vGPUs. Each virtual machine perceives its assigned portion of the resources, rather than the full capacity of the physical card, facilitating simultaneous usage by multiple users.
This mechanism differs from basic GPU sharing between applications. Instead of simple multiplexing, the GPU is subdivided into discrete resources that can be individually assigned to distinct virtual machines.
How Does a vGPU Work?
Once a physical GPU is installed in a host system, virtualization software and compatible GPU technologies partition its resources into multiple virtual instances.
- Physical GPU: The host system contains the underlying GPU hardware.
- GPU partitioning: The physical GPU is divided into multiple dedicated slices.
- Virtual machines: Each VM is assigned a specific vGPU instance.
- Dedicated VRAM: Each vGPU is allocated its own reserved VRAM.
- Isolation: Users operate within their specific resource allocation, preventing access to other users' vGPUs.
The specific number and size of available vGPUs are determined by the physical GPU model and the virtualization technology in use.
vGPUs vs. Dedicated GPUs
| Features | Dedicated GPU | vGPU |
|---|---|---|
| GPU allocation | One user or VM exclusively utilises the physical GPU. | Multiple users or VMs share a single physical GPU via separate vGPU instances. |
| VRAM | The user has access to the GPU's full available VRAM. | Each vGPU is allocated a specific portion of VRAM. |
| Users per GPU | Typically one. | Multiple, contingent on the GPU capabilities and configuration. |
| Best suited for | Workloads requiring substantial, exclusive GPU resources. | Multiple workloads needing dedicated, partitioned GPU resources. |
A dedicated GPU is generally the preferred choice when a workload demands the majority or entirety of the card's resources. Conversely, vGPUs are ideal when several users require GPU acceleration but do not need the full capacity of a physical GPU individually.
What Can You Use a vGPU For?
vGPUs support a wide range of workloads that benefit from GPU acceleration. The optimal vGPU size depends on the specific software requirements and workload intensity.
- AI and machine learning workloads
- 3D applications and engineering software
- Video editing
- Software development leveraging GPU acceleration
- Remote workstations
- Cybersecurity and other technical workloads
For demanding tasks such as large AI models, complex video projects, or heavy 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 desktop environments can leverage vGPUs to deliver GPU-accelerated virtual machines to multiple users from the same physical hardware. This strategy optimises resource utilisation, particularly when individual users do not require the full capability of a dedicated card.
For example, a team can operate separate virtual desktops while sharing the resources of a single physical GPU through dedicated vGPU allocations. Each user receives their own virtual GPU and isolated VRAM, avoiding the need to share a single desktop environment.
Try on DaDesktop
DaDesktop offers cloud desktops equipped with dedicated GPUs and vGPU options, catering to workloads that require GPU acceleration. Learn more about DaDesktop cloud GPU desktops.