Recommended NVIDIA GPUs for NVIDIA RTX vWS

Table 1 lists the hardware specification for the most recent generation NVIDIA data center GPUs recommended for NVIDIA RTX Virtual Workstation.

Table 1 NVIDIA GPUs Recommended for RTX vWS

	RTX PRO 6000 Blackwell Server Edition	L40S	L4	A40	A10	A16 [1]
GPUs/Board (Architecture)	Blackwell	Ada Lovelace	Ada Lovelace	Ampere	Ampere	Ampere
Memory Size and Type	96GB GDDR7 with ECC	48GB GDDR6 with ECC	24GB GDDR6 with ECC	48GB GDDR6 with ECC	24GB GDDR6 with ECC	4x 16GB GDDR6 with ECC
vGPU Profile Sizes (GB)	2GB, 3GB, 4GB, 6GB, 8GB, 12GB, 16GB, 24GB, 32GB, 48GB, 96GB	1GB, 2GB, 3GB, 4GB, 6GB, 8GB, 12GB, 16GB, 24GB, 48GB	1GB, 2GB, 3GB, 4GB, 6GB, 8GB, 12GB, 24GB	1GB, 2GB, 3GB, 4GB, 6GB, 8GB, 12GB, 16GB, 24GB, 48GB	1GB, 2GB, 3GB, 4GB, 6GB, 8GB, 12GB, 24GB	1GB, 2GB, 3GB (since vGPU 19.1), 4GB, 8GB, 16GB
Form Factor	PCIe 5.0 Dual Slot FHFL	PCIe 4.0 Dual Slot FHFL	PCIe low-profile, single slot	PCIe 4.0 Dual Slot FHFL	PCIe 4.0 Single Slot FHFL	PCIe 4.0 Dual Slot FHFL
Power (W)	600W	350W	72W	300W	140W	250W
Thermal	Passive	Passive	Passive	Passive	Passive	Passive
Use Case	High-end 3D visualization applications, AI training and inference workloads, as well as vPC (VDI) deployments that benefit from high user density and excellent graphics performance	Accelerates deep learning and machine learning training and inference. Also supports high-end 3D design, creative workflows, and mixed workloads	End-to-end acceleration for next-gen AI and 3D graphics, rendering, and video	High-end 3D design and creative workflows with compute capabilities	Mainstream professional visualization on high-performance mid-range VWs	Entry-level virtual workstations

Note

It is essential to resize your environment when switching to newer GPUs. For example, the NVIDIA T4 leverages ECC memory which is enabled by default. When enabled, ECC has a 1/15 overhead cost due to the need to use extra VRAM to store the ECC bits themselves; therefore, the amount of frame buffer usable by vGPU is reduced. Additional information for each hypervisor can be found in the respective NVIDIA documentation accessible here.

vGPU Profile Selection Considerations

With the NVIDIA Blackwell, Ada, and Ampere architectures, expanding workload requirements can be accommodated given the evolution of GPU memory and available compute resources.

Smaller profiles (for example, 1,2 or 3 GB) can quickly become insufficient for demanding tasks such as 3D visualization, AI inference, or data-intensive simulations. Using larger profiles helps ensure that each vGPU instance has the required GPU memory and channels to deliver consistent performance across a variety of use cases.

The Blackwell architecture introduces hardware and software improvements that address channel limitations through support for MIG; larger profiles also have more GPU channels, improving the ability to run more GPU-intensive applications. For more details on GPU channel limitations, see Understanding GPU Channels.

Important points to consider:

• Lightweight Workload Demands: Although smaller profiles (1,2 or 3 GB) are not recommended for graphics-intensive or compute-heavy workloads, they remain suitable for lightweight visualization tasks such as simple 3D model viewing, or schematic editing. These profiles are also useful in environments that prioritize user density over high graphics performance.

• Modern Workload Demands: Applications such as 3D modeling, rendering, AI inference, and simulation require significant GPU memory. Smaller profiles are inadequate for these applications, leading to frequent memory overflows and degraded performance.

• Channel Limitations: GPUs have a limited number of channels. Smaller profiles quickly exhaust these channels, preventing efficient parallel processing and leading to application errors.

• Performance Optimization: Larger profiles provide the necessary memory bandwidth and capacity to handle complex workloads efficiently. With larger profiles, you can run fewer vGPUs simultaneously, but they receive more channels, making it less likely to encounter channel limitations and ensuring smooth and consistent performance.

• Scalability and Stability: Investing in larger profiles not only meets current demands but also provides a buffer for future workload increases, reducing the need for frequent upgrades.

The following examples illustrate typical workstation workloads and applications best suited to each profile size.

Table 2 Example Profile Recommendations by Use Case

Profile Size	Example Use Cases
1 GB	Basic 2D visualization
2 GB	Entry-level CAD model viewing, schematic editing
3 GB	Small CAD assemblies, basic 3D modeling, simple content creation or animation tasks
4 GB	Basic 3D design, light graphics work, office applications
6 GB	Medium 3D design, simple modeling, image processing
8 GB	3D modeling, AI-enhanced productivity apps, video editing
12 GB	Medium size CAD models, rendering
16 GB	Complex CAD models, high-end 3D modeling and animation, simulation and analysis, VR/AR
24 GB	Data analytics, game development, 3D visualization
32 GB	Large-scale simulations, high-end rendering, data visualization
48 GB	Multi-application workloads, rendering, AI workloads
96 GB	Very large simulations, advanced rendering, AI training

For detailed configurations and additional guidelines, please refer to the NVIDIA vGPU User Guide.

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[1]

NVIDIA A16 is recommended only for entry level virtual workstations with light weight users. A minimum 8GB (8Q) profile is recommended when deploying NVIDIA RTX Virtual Workstations with A16.