Unlocking Proxmox vGPU: Optimize GPU for Rendering and AI

The world of virtualization is evolving fast, and if you’ve ever thought your GPU deserved more than a simple passthrough, then the latest Proxmox vGPU support might just be the upgrade your data center (or homelab) has been waiting for. In this deep dive, we’ll explore the nitty-gritty of configuring NVIDIA’s vGPU software with Proxmox VE, examining prerequisites, installation commands, and benchmark results to help you determine if this solution is the right fit for your rendering, AI, and VDI workloads.

The vGPU Paradigm: Sharing Without Sacrifice​

NVIDIA’s vGPU technology is a game changer. Instead of dedicating your entire GPU to a single virtual machine, vGPU partitions the computing and memory resources so that multiple VMs can share them simultaneously. Imagine slicing a giant pizza where every guest gets a piece, yet the overall flavor remains intact—even if the slices get smaller as more guests join the table. This approach helps ensure peak GPU utilization even when workloads fluctuate, making it ideal for environments that demand flexibility, be it for complex rendering tasks, AI inference, or virtual desktop infrastructure.
Key points to note about vGPU:

• It partitions GPU resources among multiple VMs.
• Ideal for varied workloads—AI, VDI, and rendering.
• Offers a compromise between performance and resource sharing.

Prerequisites: Before You Dive In​

Setting up a successful GPU virtualization environment requires some groundwork. Before you can unleash the full power of NVIDIA’s vGPU software on Proxmox VE, ensure that you have these essentials in place:

• Licensing and Software: You’ll need an NVIDIA Enterprise account to access the evaluation or full versions of the vGPU software. For our setup, the following versions are recommended:
• vGPU Software: 18.0
• Host driver: 570.124.03
• Linux guest driver: 570.124.06
• Windows guest driver: 572.60
• Hardware Requirements: A vGPU-compatible card is mandatory. In the reviewed setup, an NVIDIA L40S paired with a Dell PowerEdge R760 exemplified the kind of hardware that can handle such enhancements.
• UEFI (BIOS) Settings: Check that your server’s UEFI has these features enabled:
• VT-d or AMD-v: for virtualization acceleration
• SR-IOV: to support multiple virtual instances of a PCIe device
• Above 4G decoding (Resizable BAR) and ARI: though not always visible, these can be crucial for proper operation.
• Proxmox VE Version: Your environment must run a version of Proxmox VE with at least pve-manager 8.3.4 and kernel 6.18.12-8-pve or newer.

The checklist might look like this:

• [x] Valid NVIDIA Enterprise account and license
• [x] vGPU-compatible GPU and relevant drivers
• [x] UEFI settings checked and enabled (VT-d, SR-IOV, above 4G decoding)
• [x] Compatible Proxmox VE version

Configuring the Proxmox Host for vGPU​

Once your prerequisites are verified, the next step is preparing the Proxmox host. The process starts by installing the Proxmox vGPU setup tool and loading the proper drivers.

Installing the vGPU Helper Tool​

Access your Proxmox VE server’s web interface and navigate to the “Shell” tab. The first command installs the helper tool:
apt install pve-nvidia-vgpu-helper
After the installation completes, run:
pve-nvidia-vgpu-helper setup
When prompted, answer “Y” to any questions, letting the tool configure your environment accordingly. Finally, a quick reboot is necessary to finalize these changes—either through the “Reboot” button in the “Summary” tab or by typing reboot in the Shell.

Loading the NVIDIA vGPU Host Driver​

Once the server is back online, the next step is to load the NVIDIA vGPU host driver. Transfer the host driver (downloaded from NVIDIA’s licensing portal) to the server’s /home directory. Then, execute these commands:
cd /home
chown root NVIDIA-Linux-x86_64-570.124.03-vgpu-kvm.run
chmod +x NVIDIA-Linux-x86_64-570.124.03-vgpu-kvm.run
./NVIDIA-Linux-x86_64-570.124.03-vgpu-kvm.run --dkms
If your drivers came in a compressed archive, ensure you extract and choose the “.run” file from the Host_Drivers folder. Verify the file name using the ls command before running the installer.

Enabling SR-IOV: The Backbone of vGPU​

Single Root I/O Virtualization (SR-IOV) is a critical component for enabling vGPU support. It allows the physical GPU to advertise multiple virtual instances on the PCIe bus. Enable SR-IOV by executing:
systemctl enable --now pve-nvidia-sriov@ALL.service
This command ensures that SR-IOV is automatically turned on during boot, setting the stage for GPU resource mapping.

Mapping GPU Resources in Proxmox VE​

With the host prepared and the required drivers loaded, it’s time to map the GPU’s resources so they’re available for your VMs.

• Open the Proxmox VE web interface and click on “Datacenter” in the top-left corner.
• Navigate to the “Resource Mappings” tab.
• Click the “Add” button under the “PCI Devices” section.
• In the “Name:” field, enter a descriptive name for the GPU.
• Ensure that the “Use with Mediated Devices” checkbox is ticked. Verify that the server’s GPU appears in the “Mapping on Node” drop-down list.
• Review the device list, making sure that each device has “NVIDIA Corporation” as its vendor. If multiple GPUs are present, use the lspci command in the Shell to determine the proper device IDs.
• Once verified, select the appropriate device and click “Create” to confirm the mapping.

This process activates the GPU’s virtualization capability, preparing it for allocation to individual virtual machines.

Serving the Guests: Assigning vGPUs to Virtual Machines​

Now that the host is finely tuned, it’s time to extend the power of the GPU to your VMs. For demonstration purposes, let’s consider a Windows Server 2025 VM.

Steps to Attach a vGPU to a Windows VM​

• Shut down the Windows VM via any method (noVNC console, power menu, etc.).
• Navigate to the VM’s “Hardware” tab.
• Click the “Add” button, then select “PCI Device.”
• In the presented window, choose the GPU resource you mapped earlier from the “Device:” drop-down field.
• Next, select the “MDev Type” field. Here you’ll find multiple options detailing the amount of VRAM allocation and use case:
• A: Application solutions (NVIDIA Virtual Applications license required)
• B: Virtual desktops (NVIDIA Virtual PC license or RTX vWS license required)
• Q: Virtual workstations with RTX Enterprise drivers (RTX vWS license required)
• For this setup, the “NVIDIA L40S-12Q” profile is a popular choice.
• Check the “PCI-Express” checkbox and click the blue “Add” button.

Post-Attachment Steps: Driver Installation and Licensing​

After the vGPU is assigned, power up the VM. Inside the Windows environment, you must install the guest driver:

• Transfer the NVIDIA vGPU Windows guest driver from your licensing portal to the VM.
• Run the installer and choose the “Express” installation option, ensuring the driver is extracted to the default location.

Activation is the final step. Depending on your configuration (dedicated license server or cloud license server), activate the vGPU license. In our example, a token file received from a cloud license server is manually placed into the folder:
C:\Program Files\NVIDIA Corporation\vGPU Licensing\ClientConfigToken
A reboot of the VM completes the activation process and readies the system for GPU-accelerated tasks.

Putting Proxmox vGPU to the Test: Benchmarks and Performance Insights​

Once everything is set up, it’s time to see the virtualized GPU in action. Performance testing plays a vital role in determining whether the benefits of sharing a physical GPU match your operational needs.

Cinebench 2024 Benchmarks​

Using Maxon’s Cinema 4D-based benchmark, performance testing reveals how the NVIDIA L40S performs under different configurations:

• Full GPU Allocation: In a scenario where the vGPU is not partitioned (48Q profile, using all available VRAM), a single VM achieves an impressive 21,147 points.
• Shared GPU Scenario: When the GPU is split four ways and all VMs run the benchmark simultaneously, individual scores drop quite drastically into the 2,514 to 2,567 range. This is a clear indication of the overhead incurred due to NVIDIA’s time-slicing methodology.
• Isolated VM Test: When only one VM is active with the “12Q” profile and the others are idling, the score jumps to 15,133—demonstrating a significant recovery in performance when the GPU’s resources are not being shared concurrently.

Blender Rendering Benchmarks​

Benchmarking with Blender further underscores the trade-offs:

• In multi-VM scenarios (e.g., Monster, Junkshop, and Classroom benchmarks), performance dips are evident in three out of the four VMs.
• Interestingly, one VM occasionally outperforms the others—up to 2.4 times higher than the lowest score in a given test. This suggests that under certain conditions, the vGPU’s scheduling might momentarily favor one instance over the others.

The differing performance metrics raise a compelling question: Is the slight degradation in per-VM performance worth the flexibility and cost-efficiency of sharing a GPU? For many organizations—especially hyperscalers and space-constrained data centers—the consensus is that the benefits outweigh the drawbacks.

Balancing Act: The Pros and Cons of vGPU​

The implementation of NVIDIA vGPU in Proxmox VE offers significant advantages:

• Flexibility: Multiple VMs can share the powerful resources of a single GPU, making it ideal for workloads that do not require constant peak performance.
• Cost-efficiency: By virtualizing a GPU, organizations can reduce hardware expenditures and optimize space—critical factors in modern data centers.
• Scalability: This approach is particularly appealing for environments where numerous virtual desktops or AI applications need to run concurrently.

However, it’s not all smooth sailing:

• Performance Trade-offs: When the GPU is time-sliced among multiple VMs, individual performance suffers. Workloads that need unfettered GPU power might not benefit as much.
• Licensing Complexity: NVIDIA’s licensing model for vGPU software can be intricate, and managing license tokens (whether via a dedicated or cloud licensing server) adds another layer of administration.
• Scheduling Oddities: The benchmarks illustrate that NVIDIA’s time-slicing might occasionally prioritize one VM over the others, making performance less predictable in heavily loaded environments.

Organizations must weigh these pros and cons in the context of their operational requirements. For many, the efficiency gains and reduced physical footprint justify the occasional performance hiccup.

Looking Ahead: Future Prospects for Proxmox vGPU​

The rapid expansion of Proxmox Server Solutions’ features—from software-defined networking to improved kernel support—underscores its ambition to remain competitive in the hypervisor landscape. Although the current implementation of vGPU support might feel a bit rough around the edges compared to competing platforms, it’s a significant step forward.
Imagine a future where these scheduling intricacies are fine-tuned, where licensing is more streamlined, and where the performance gap between dedicated and virtualized GPU workloads narrows even further. For organizations operating in environments where every inch of server space matters, vGPUs offer a tantalizing prospect: maximized resource utilization without the need to invest in a separate GPU for each VM.

Key Takeaways​

• vGPU technology allows you to partition the GPU’s resources among multiple virtual machines, making it a versatile tool for rendering, AI, and VDI.
• Setting up vGPU in Proxmox VE requires attention to both hardware prerequisites (UEFI settings, compatible GPU) and software versions (Proxmox 8.3.4, kernel 6.18.12-8-pve or newer).
• The process involves installing a specialized helper tool, loading NVIDIA’s host drivers, enabling SR-IOV, and mapping GPU resources through the Proxmox web interface.
• Assigning a vGPU to a Windows VM entails selecting the correct mediated device profile, installing the appropriate guest driver, and activating a licensing token.
• Performance benchmarks reveal that while a single VM can extract impressive scores from a full GPU allocation, sharing GPU resources results in noticeable performance drop-offs due to time-slicing.
• For many data centers and homelabs, the trade-offs are acceptable because the benefits of increased flexibility and reduced cost outbalance minor performance penalties.

Final Thoughts​

Proxmox vGPU support is a testament to how virtualization technologies continue to push the boundaries of what’s possible—allowing one GPU to serve multiple applications without requiring the expensive overhead of dedicated hardware for every instance. Although there are some performance trade-offs inherent in NVIDIA’s time-slicing strategy, the overall benefits for organizations facing spatial, financial, and administrative constraints are substantial.
Whether you’re managing a sprawling data center or tinkering in a homelab, this guide provides a solid foundation to take full advantage of your GPU’s capabilities. By embracing the power of vGPU, you ensure that your GPU truly gets the respect—and utilization—it deserves, rather than being relegated to a simple passthrough.
Stay tuned for further updates as Proxmox continues to refine its features, and feel free to explore related discussions on Windows 11 updates, Microsoft security patches, and cybersecurity advisories on WindowsForum.com to broaden your tech horizons.

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Source: StorageReview.com Proxmox vGPU Guide: Your GPU Deserves More Than Just Passthrough