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Reviews from AWS customer

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    Manush Madan

A rock-solid, plug-and-play platform enabling scalable, reliable multimodal AI inference and fine-tuning while saving significant time, with minor room for improvement.

  • January 10, 2026
  • Review from a verified AWS customer

What is our primary use case?

My main use case for NVIDIA RTX Virtual Workstation involves using it for AI inference and also training LoRAs or fine-tuning models, where I am using generative AI for images and video using inference tools like ComfyUI and other Windows-based tools, and I am also trying to fine-tune certain models.

NVIDIA RTX Virtual Workstation helps with my generative AI workflows by coming pre-loaded with the right drivers, and I use the CUDA toolkit and ComfyUI mostly for inference while utilizing open models.

For audio tools, I am fine-tuning the model that is also open source, which makes stability and predictability the best aspects for my use case.

I use all these generative AIs on my projects as well as my clients' projects, and I even fine-tune from the results and data provided by the client.

What is most valuable?

The best features NVIDIA RTX Virtual Workstation offers are that it is plug-and-play and ready to use, meaning I can just load my applications, my ComfyUI application, and other software without needing to worry about additional setups, making reliability and stability stand out for me.

The plug-and-play aspect has helped me significantly by firstly reducing time, and secondly, I need not hire additional specialists because it is easy for everyone to use.

The reliability is impressive as it works every time, and I can switch between instances, between G5 and G4 instances, depending upon my workloads, ensuring zero crashes and consistent performance.

NVIDIA RTX Virtual Workstation has positively impacted my organization by providing two main benefits: time-saving and improved quality, allowing me to complete tasks that would take days in just hours while using different open models that update regularly.

A specific example of a project where I saw a significant difference in time saved is during the prep work for a video game I am developing. The characters, the settings, and all workflows are done quickly, allowing me to present it to my client so they can develop a full storyline and environment, accomplishing in two weeks what typically takes months and with better quality than before.

What needs improvement?

Sometimes the CUDA toolkit had different versions that would not run, but updating it or running it in a virtual Python environment resolves the issue, which could be a Windows problem. These are minor issues, nothing major.

I would like to see support for more NICE DCV, specifically Amazon NICE DCV on the go, as I install remote desktop features beyond what is provided by Windows.

For how long have I used the solution?

I have been using NVIDIA RTX Virtual Workstation for more than two years now on different G5 and G6 instances.

What do I think about the stability of the solution?

NVIDIA RTX Virtual Workstation is very stable.

What do I think about the scalability of the solution?

NVIDIA RTX Virtual Workstation's scalability is excellent as I can switch between instances depending on workload, using smaller G4 instances or scaling up to maximum G6E instances, and it works flawlessly.

How are customer service and support?

Customer support is great.

How would you rate customer service and support?

Positive

Which solution did I use previously and why did I switch?

Previously, I used NICE DCV initially, then I switched to regular vanilla Windows instances before finally adopting NVIDIA RTX Virtual Workstation, which is the best among the options.

Before choosing NVIDIA RTX Virtual Workstation, I evaluated other options, including regular Windows servers where I had to install all the drivers myself, which was cumbersome, and NICE DCV instances because they came with Amazon DCV installed. However, I faced certain driver and CUDA issues before finally finding NVIDIA RTX Virtual Workstation, which runs out of the box and is plug-and-play.

How was the initial setup?

Deploying NVIDIA RTX Virtual Workstation in my environment is very easy. I just buy it from the AWS Marketplace, spin up the instance, and it is ready to go.

My experience with the configuration process is good as it is fairly straightforward and easy.

What was our ROI?

I have seen a return on investment in terms of time saved and better quality, with fewer employees needed to accomplish the same work, enabling me to handle more projects simultaneously.

What's my experience with pricing, setup cost, and licensing?

My experience with pricing, setup cost, and licensing is good as it is pretty straightforward and easy to understand.

What other advice do I have?

I purchased NVIDIA RTX Virtual Workstation through the AWS Marketplace.

I rate NVIDIA RTX Virtual Workstation a five on a scale of one to five.

I give it a five because it provides everything I need, including predictability, stability, and reliability, all while being plug-and-play.

NVIDIA RTX Virtual Workstation integrates flawlessly with other AWS services I use, allowing seamless switches between instances.

My experience with the procurement process is easy and straightforward as I just go to the marketplace, search for NVIDIA RTX Virtual Workstation, and purchase it without any hassle.

The metering and billing experience follows predictability and is straightforward, as I utilize it for other AWS services too, making it easy to navigate in the billing section.

I rate customer support a ten, as I have not used it much but score it highly whenever I have.

I would advise others looking into NVIDIA RTX Virtual Workstation to use the Win Server 2022 version rather than the 2025 version due to certain bugs and reliability issues present in 2025.

If anyone is using NVIDIA GPUs and CUDA for AI-related and graphic-intensive workloads, NVIDIA RTX Virtual Workstation is the best workstation instance.

I rate this product overall as a five out of five.


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