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.
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 |
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.
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.
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.
Footnotes
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.