The question comes up on almost every GPU evaluation call we take at V2 Cloud, usually phrased the same way. Someone runs Revit, or Blender, or Premiere. They want to know whether one of our cloud desktops will feel like the machine sitting under their desk, or whether it will feel like a compromise they have to explain to their team every day for the next three years.
The answer depends on the workload, and the differences between workloads are bigger than most vendor pages admit.
A structural engineer opening a 400 MB Revit model has a completely different performance profile from a video editor scrubbing 4K timeline footage, and both are different again from someone fine-tuning a machine learning model overnight. Lumping all three under “GPU workloads" is how buyers end up disappointed.
Direct answer: Our GPU Workstations add-on handles professional GPU workloads, including AutoCAD, Revit, SolidWorks, Blender, Adobe Premiere Pro, Unreal Engine, and MATLAB. Interactive design work runs well on a shared GPU desktop that multiple users can access. Sustained compute, meaning final-frame rendering and machine learning training, needs a dedicated virtual machine sized for that job.
The useful split is not by application. It is by how the workload uses the graphics card over time.
Interactive workloads hit the GPU in bursts. You rotate a model, the card works for 200 milliseconds, then it idles while you think. Sustained workloads hold the card at high utilization for minutes or hours. That distinction drives how we size your deployment.
CAD and Building Information Modeling (BIM) are the clearest wins on our platform. AutoCAD, Revit, SolidWorks, Inventor, and Civil 3D spend most of their runtime waiting on the user rather than on the graphics card. Viewport navigation, model rotation, and section cuts are bursty by nature.
Photoshop, Illustrator, and InDesign behave the same way. So does most GIS work, including ArcGIS Pro.
This is where our multi-user model pays off. Because we price by virtual machine rather than per seat, a team whose GPU demand is intermittent can share a single desktop rather than paying for idle capacity per person.
What this means: If your users spend more time thinking than rendering, a shared GPU desktop delivers a local-feeling experience without a workstation on every desk.
Adobe Premiere Pro, DaVinci Resolve, After Effects, Blender modeling, and Unreal Engine editing all run on our GPU desktops. They need more care at the sizing stage.
Timeline scrubbing in 4K is continuous rather than bursty. So is real-time viewport rendering in Unreal. These workloads want a higher resource tier and fewer concurrent users on the same machine, and they benefit from proxy workflows during editing.
Data gravity is the other constraint. A 200 GB video project should live in the same region as the desktop editing it. We run data centers across multiple regions specifically so project storage and compute sit together.
Final-frame rendering in Blender Cycles, V-Ray, Arnold, or Redshift pins the card at full utilization for as long as the job runs. Machine learning training in PyTorch or TensorFlow does the same, often overnight.
Sharing a desktop across users breaks down here. One render job will starve every other session on that machine.
The pattern we recommend is separation. Interactive desktops for the design team, plus dedicated GPU virtual machines for render and training work. MATLAB with Parallel Computing Toolbox belongs in this second group depending on the toolbox in use.
Both models exist on our platform because both solve real problems. Picking wrong is expensive in different directions.
A shared GPU desktop lets several people work from one virtual machine. Since our pricing follows the machine and its workload tier rather than the number of named users, this is the efficient choice when GPU demand is intermittent.
A dedicated GPU VM assigns the machine to a single workload. It costs more per workload and removes the variable that causes most complaints: someone else’s job affecting your session.
| Decision factor | Shared GPU desktop | Dedicated GPU VM |
| Workload pattern | Bursty and interactive | Sustained high utilization |
| Typical applications | AutoCAD, Revit, SolidWorks, Photoshop, ArcGIS Pro | Blender Cycles, V-Ray, PyTorch, TensorFlow, 4K color grading |
| Performance consistency | Varies with concurrent user activity | Predictable and isolated |
| How we price it | Per virtual machine and workload tier, shared across users | Per virtual machine, reserved for one workload |
| Best for | Design teams doing daily model work | Render nodes, ML training, deadline-critical editing |
Most of our GPU customers run both. Designers work from shared desktops. A smaller number of dedicated machines handle overnight renders and anything with a hard deadline attached.
Sizing is not permanent. Start conservative, watch utilization for two weeks, then move the users who need more onto their own machine. That is considerably cheaper than buying twelve workstations and discovering four were overspecified.
Not sure which mix your team needs? We will size it against your actual applications before you commit to anything.
GPU model matters less than most buyers expect. Across the GPU workloads we deploy, these five variables explain nearly every performance complaint we investigate.
The quotable version: A correctly sized machine 20 milliseconds away outperforms a more powerful one 120 milliseconds away for every interactive workload.
Our GPU Workstations run standard Windows applications, so the question is usually about performance rather than whether the software installs.
Commonly deployed on our platform:
Teams standardizing on one application often deploy through a dedicated setup instead of a general-purpose image, such as AutoCAD cloud hosting or Revit cloud hosting.
Licensing is the part people forget. Network licenses, dongle-based licensing, and node-locked licenses each behave differently on a virtual machine. Confirm with your software vendor during the trial, not on deployment day.
The performance conversation gets most of the attention. The operational one usually matters more across three years.
A local workstation fleet means driver management on a dozen machines, refresh cycles, physical device security, and a machine that dies the week before a deadline. GPU driver updates in particular have a habit of breaking one application while fixing another.
We validate drivers once, on the image, before anyone touches it. A dead endpoint means the user logs in from a different device and keeps working, because nothing of value was stored locally.
Our desktops deploy in under 20 minutes, and our 99.95% uptime SLA works out to roughly 4.4 hours of potential downtime across a full year. Daily automated backups run without anyone configuring them, which for a firm holding client project files is usually the detail that closes the conversation with the partner signing off on it.
Access control lives in one console. Multi-factor authentication, single sign-on, user provisioning, and session policies are configured centrally rather than per machine. Our 24/7 technical support is included in every plan at no extra cost, which matters more on GPU deployments than on standard desktops because the failure modes are less common.
Want to see how your models actually perform before you commit? Our 7-day demo runs on your files, on your network, with your applications.
Vendor benchmarks run in ideal conditions. Yours will not. A structured week beats any spec sheet.
Day 1: Measure round-trip latency from your primary user locations to the data center region we assign you.
Days 2 and 3: Load your largest real project file. Not a sample. The 400 MB model with the linked files and the messy history.
Day 4: Test concurrency. Put three or four users on a shared desktop simultaneously and see whether anyone notices the others.
Day 5: Run your longest job. A full render, a large export, an overnight training run.
Document what breaks. Every deployment has one surprise in it, and finding yours during a trial costs nothing.
Yes, provided the machine is on an appropriate resource tier and network latency stays under roughly 60 milliseconds. AutoCAD is an interactive workload, so it does not require a dedicated machine for typical drafting and 2D work. Large 3D models benefit from a higher tier.
Sustained GPU workloads need a dedicated machine: final-frame rendering, machine learning training, and 4K color grading all hold the graphics card at high utilization for extended periods. Shared desktops suit interactive design work but degrade when one session monopolizes the machine.
We price by virtual machine and workload tier, never per named user. Several people can share one GPU desktop where the workload allows it. Current tiers and figures are listed on our pricing page.
Latency matters more than bandwidth. A stable connection under 60 milliseconds round trip to our data center will outperform a high-bandwidth connection sitting at 150 milliseconds. Bandwidth requirements rise with resolution and monitor count rather than with workload intensity.
Yes. Blender modeling and viewport work run well on a shared GPU desktop. Final-frame rendering in Cycles should run on a dedicated machine, because a render job holds the card at full utilization and will affect anyone else on that desktop.
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