NVIDIA AI Enterprise

We purchased NVIDIA H100 GPUs, and as a result of that, we also got an NVIDIA AI Enterprise license.

It was mainly used for NVIDIA NGC and to deploy the CLI on the server with the GPUs.

As I mentioned, we have the NVIDIA NGC website and you need to register. I was the main person who did all the registrations with NVIDIA AI Enterprise. I created the organization, added users, and gave access to multiple resources that NVIDIA AI Enterprise offers through its website, NGC.

NVIDIA AI Enterprise offers a great breadth of resources. To start with, NVIDIA offers its fine-tuned models, and one of the best features are the blueprints. For enterprises who have access to NVIDIA AI Enterprise, they can kickstart their AI journey by using the models as well as the blueprints.

As far as NVIDIA models are concerned, we did not use the NVIDIA models, but we used open source models that we downloaded and installed on the AI server. However, some of the resources that we widely used are the blueprints. For example, to start off with enterprise RAG applications, NVIDIA had everything in place that helped cut down our time to build the applications.

The resources that are offered include the models, secondly, the blueprints, and then the training resources that NVIDIA offers, which are invaluable. For an enterprise that is trying to have AI as part of their strategy, NVIDIA AI Enterprise license provides almost everything that enterprises need to get the team trained, use the models, and get the training that is required. It is almost a one-stop shop.

I believe the support can be better. For anything that you need to reach out to NVIDIA, it is through the ticket system. I did not have many problems, so I did not really utilize the ticket system. Maybe that is an area that can be improved. There is always scope for improvement in that particular space.

It is the unknown. There is always scope for improvement, and I will not put it at a perfect 10. I will not say that anything is perfect.

I have used NVIDIA AI Enterprise for the last two and a half years.

They have all mastered that and they have been doing it for so long and with so many customers that I believe what is currently present will really help customers.

They are scalable. It all depends on your infrastructure needs and basically, you can add more GPUs, more servers, and build the necessary production-grade resilience. So, they are pretty scalable.

As I said, I did not have much interaction with the customer support. It was mostly through email. I did not have any issues, so I did not find the need for customer support much.

We did not use a different solution. However, we were also using Microsoft Azure. The NVIDIA H100s was an internal deployment. So, we were using Azure AI Foundry, too.

I actually took a new role with my current employer, Mercy. However, the experience with NVIDIA is at Point32 Health, where I was the enterprise architect. We had signed a contract with NVIDIA. It is a three-year license to purchase the H100 GPUs. We did it internally. I was talking about sovereign AI, so we designed the server and we installed the GPUs with the help of HPE.

We had a contract with HPE. Through HPE, we negotiated the contract with them and got the GPUs and the server.

For organizations that want to have sovereign AI and instead of depending totally on cloud providers, the tokenomics is a huge factor. That is a huge savings that organizations can achieve by utilizing open source models, open-weight models, utilizing open source AI tooling, and the investments that go into this whole stack by doing it yourself. We are investing in NVIDIA resources that in the long run are going to pay off. So, the ROI on going for an NVIDIA AI Enterprise license is huge.

Definitely fewer employees are needed. As far as return on investment is concerned, there are studies which show that because we got the H100s two years ago, the value of the H100s actually rose because of the increase in pricing. In a way, instead of depreciating the value of the H100 GPUs, it actually went up.

We got a very great deal.

Absolutely, most enterprises depend on the CSP platforms, such as Azure or AWS or GCP. For regulated domains, such as healthcare, security, or other domains like financial, if they have a strategy of having sovereign AI, then NVIDIA AI Enterprise is the way to go because you will get the resources and you will get the training that is required. Of course, it is a matter of skills in order to understand the AI architecture and implement them. However, I believe that sovereign AI is the way to go for the regulated industries and having an NVIDIA AI Enterprise license is definitely a way to cut down the time for enterprises to learn things and help them get on their journey and implement the strategies.

I have not really delved into the governance aspect of NVIDIA, but security is pretty robust because everything that NVIDIA provides is very clear. If you are utilizing it for your enterprise applications, then they provide what are the guardrails and the best practices. If it is a POC, they say that it is okay to do this, but not for production. So, they have it all covered as far as security is concerned. Governance has definitely some blueprints for governance, but I have not used the governance aspect, so I cannot comment on that.

As far as reliability and accuracy are concerned, that is something that the enterprise which is deploying the models and building apps and agents is responsible for it. NVIDIA provides the models and the microservices, everything, but again, everything depends on how they are implemented by the enterprise. So it is the responsibility of the enterprise or the AI architects of the enterprise in order to understand and lay it out as per the specs that they have.

I would say definitely consider it. Look at the pros and cons and the needs of your organization. As far as my experience goes, it is a great solution. It also depends on what your infrastructure needs are, whether you are deploying it on-premises or you want to actually deploy it on the cloud yourself. So, it all depends.

My experience was very positive. As I mentioned, it helped me start from scratch and the GPUs and also the NGC website that NVIDIA provides with all the resources are phenomenal. It was a highly enriching experience that I had with NVIDIA AI Enterprise. My rating for this solution is 9 out of 10.

NVIDIA AI Enterprise is totally dependent on AI applications. The use cases include healthcare with medical analysis, video analytics, and generative AI with large language models. We are using Copilot-style applications for generative AI and LLMs to achieve faster customer service, improved productivity, and automation of repetitive tasks.

NVIDIA AI Enterprise offers several advantages for customers. It provides faster AI performance and quicker time-to-market. Higher GPU utilization helps to accelerate work for organizations. Organizations can develop AI solutions and deploy them across on-premises data centers or any cloud environments.

NVIDIA AI Enterprise platform reduces the complexity of deploying and managing AI applications. Integration with AI has a significant impact on project development. We can improve quality and productivity through AI integration in development, which accelerates software quality, reduces costs, and increases team productivity. This enables faster business innovations and provides a significant competitive advantage through quick time-to-market.

The first area for improvement is licensing cost, as it has high licensing costs due to being a subscription-based software platform. This is the main issue I have observed. The second area is infrastructure cost. These are the two areas I remembered.

I have been using NVIDIA AI Enterprise for around 3.5 years.

Overall stability receives a high rating of approximately nine because NVIDIA AI Enterprise has strong hardware stability and software stability. From a security and reliability perspective, it is good, with long-term support branches available. NVIDIA AI Enterprise includes built-in security features such as Silicon Root of Trust. I gave it a nine rating for these reasons. I deducted one point due to considering enterprise-grade hardware requirements.

Scalability receives a rating of 9.9 and above.

We receive substantial benefits from NVIDIA AI Enterprise support. It provides access to NVIDIA AI experts and faster resolution times. NVIDIA AI Enterprise includes monthly patches for vulnerabilities and bug fixes, which helps reduce security risk and downtime.

I am satisfied with NVIDIA AI Enterprise customer service because support is available 24/7. Whenever we raise a ticket, they respond immediately without prioritizing by severity or priority level.

Installation is easy if you have an experienced person who has installation expertise. For new users, there is a learning curve to understand how to open and manage multi-GPU servers, AI environments, Kubernetes containers, and related components.

Regarding return on investment, NVIDIA AI Enterprise provides financial benefits. It enables faster time-to-market, increased productivity, and reduced infrastructure costs.

Resource optimization helps minimize downtime by ensuring proper allocations, monitoring resource utilization, and balancing workloads. This reduces stress on servers and optimizes resource usage to maintain application performance and service levels during traffic spikes.

I recommend measuring a 30% faster performance improvement through these optimizations. One feature I would suggest is auto-scaling AI infrastructure, which I have documented in a white paper. This feature allows you to add or remove GPUs based on workload demands, improving resource utilizations and lowering costs. My overall review rating for NVIDIA AI Enterprise is 9.5.

My main use case for NVIDIA AI Enterprise is to generate videos and to improve graphics. I use NVIDIA AI Enterprise to generate videos for social media.

I find that NVIDIA AI Enterprise is very useful to improve the speed of video effects and makes me work faster. I love it.

The HP8 and HP16 are the best features for core acceleration. The HP8 and HP16 features are deep learning, so they can learn very fast and then be more efficient after the core acceleration. With a weak machine, we can achieve some really nice improvement.

I have noticed a lot of time saved. The results can be not the best, but after the human task and after the AI, it is acceptable. Mainly, we can gain time.

I gain approximately thirty percent of my time, which equals a lot of hours each month.

I think they need to make NVIDIA AI Enterprise with a lower cost because this is a big investment.

Maybe they need to make a better cooler or something to keep the material from getting too warm.

NVIDIA AI Enterprise is very accurate but can be improved.

I have been working here for three years and a half.

It is acceptable.

I choose an eight because it is a great product that makes me gain time. However, I have to work a bit after the generation.

I think they need to make NVIDIA AI Enterprise with a lower cost because this is a big investment.

I rate this product an eight out of ten.

NVIDIA AI Enterprise is essentially a GPU enhancement software that takes advantage of NVIDIA's full stack built on what is called NeMo architecture and NIMs, which are the micro inferencing servers. It allows you to put a GPU into a server, oftentimes with eight or up to eight NVIDIA GPUs in a server.

You will get additional performance that enhances whatever workloads you are running on the server, but you don't have all the right tools such as monitoring, management, and orchestration. NVIDIA AI Enterprise gives you the full software stack that gives you access to really maximize the value. The primary benefit is that you are really taking advantage of the hardware and the software working together.

Orchestration allows you to schedule jobs to run at certain times. NVIDIA AI Enterprise software also has regular updates, so every couple of weeks there are new pushes out there so you can become more proficient and get a much better hands-on experience for achieving the goals and making the most effective GPU investment possible.

The best features of NVIDIA AI Enterprise are the GPU orchestration and the visualization that can happen. When you run the Enterprise software, you will have access to other features including AI Workbench.

Many of these features you can actually view for free on build.nvidia.com, and I want to stress that because people think this is so complicated and so expensive that they will never have access to it. That is not true. NVIDIA has a number of resources and training modules that give you access to this information without necessarily needing to purchase everything because usually the companies that have AI Enterprise purchase it on a per GPU basis.

So it is one license per GPU, and that number does add up quickly. However, it is easy because it again gives you visualization of everything. Think of it as a dashboard you can run. You don't need to know how to program or how to read code. It is very visual and it gives you metrics on how effective everything is running and you can toggle different environments. If you want to dive deeper, you can run all kinds of simulations with that.

You kind of start high-level visual and then you can get more specific over time depending on what your job is.

The impact of NVIDIA AI Enterprise on the time for my AI applications is pretty remarkable because you don't have as much downtime with this type of software. The downtime that has been saved is pretty much as much as possible. There is usually about six nines of availability. Six nines means it is 99.9999% up. That comes out to basically experiencing about 11 seconds out of the year, which is just basically them toggling new servers.

What is interesting about this is that many of the GPUs are what are called hot-swappable, meaning that you can actually change them out while your data center rack is still powered on and running. They have it built in so you can pull it with a special tab, which is an orange tab. It is not going to shock you or anything. This gives you the ability to go into the facility or have the facilities team change that out if there is an issue and you need to change a GPU out or you need to change a license out, and they can make it happen without any interruption. There is really not any interruption noticed. It is still a fairly new product so there are not as many examples, but from what I have seen, there is not really any issue with interruption. NVIDIA AI Enterprise definitely keeps the GPUs maintained, and that is why orchestration is so important. It is an electric car in nature—it charges when it is not being worked. Because if you just drive a car all day and don't maintain it, the car is going to fall apart. It is the ultimate maintenance package.

My thoughts on the security protocols and their data protection is that this is one area that has actually needed to be improved. NVIDIA has done those things, but I was recently working with the federal government and many times they require what are called FIPS security compliance. It is a cryptography key that gets put onto the hard drives that work with the servers that have the GPUs.

NVIDIA has done some investment in that type of security. There is Zero Trust Architecture that you can use, and that is a theme all of NVIDIA software runs on, meaning all of the software is built to be encrypted between the front end and the back end. However, I feel the investment needed to make this software even more secure could be additionally improved if NVIDIA continues to invest in federal government agencies and things of that nature.

This will help give them the highest level of security and resiliency necessary to really protect everybody from malicious actors because there are so many scams going on with AI and chatbots and phishing attacks are growing because the more that technology grows and expands, the more attacks are possible. NVIDIA is obviously the leader in AI GPUs, so they have such a large surface they have to protect.

In my opinion, the areas that have room for improvement in NVIDIA AI Enterprise are that not a lot of people know that NVIDIA has this offering. The people who know are the people who work in the tech sales world who actually talk to customers. However, people who are trying to learn on their own and don't have access to millions of dollars as the corporations do on a regular basis should still have the resources available to learn this type of information. NVIDIA should continue to invest in marketing to say they have this offering available to them. I have been trying to get them to do this. They should be able to go to universities and students who are obviously interested in this space and may not work at a large tech company.

A lot of my learning has been self-taught. I have some experience, but I went on the website and did a lot of digging.

There are so many resources out there that it can be overwhelming to figure out which is the right one to start with.

Also, going back to the security piece, the solution is secure, but it doesn't meet the Department of Defense regulations from my understanding, and that is a whole other level that NVIDIA would need to achieve. It usually takes a couple of years of auditing and strict compliance before you can get what are called FIPS 140-2 and 140-3 certification. I would hope that NVIDIA can continue to invest in that area. They have started, but they haven't really done enough to get that level of security yet that is needed for the highest level of classified information. Those are the improvements that are possible for sure with the platform.

I have been using NVIDIA AI Enterprise for about two years.

Regarding stability, NVIDIA AI Enterprise is probably a 10 because they are the best, they are the most profitable company in the world, so I don't see how you get more stable than that.

In terms of technical support, I would rate it probably a nine or so.

The deployment of NVIDIA AI Enterprise is very easy because they handle all of it for you basically. You are just getting the software to install on the GPUs.

There is a pretty useful manual you get, and you get support. Pretty much everybody who has this doesn't do it alone. They have NVIDIA services or professional services that they would purchase as well, and it is all bundled.

There can be NVIDIA team members that can handle this on-site deployment installation or you can buy that remotely or you can get training credits. There is always another resource available to help out. You just figure it out, but it is pretty easy. My approach is to try to learn as much as I can beyond just as much as I am allowed to learn because there is never an end to that.

Regarding pricing, I find it pretty expensive, but as I said, if you bundle everything, you can pretty much handle it. It makes sense because it will pay for itself in the long run. It is a long-term investment. The cost of the product is very expensive, but you are able to save long-term with how the product works and by getting the best bang for your buck. NVIDIA AI Enterprise does pay for itself; it just requires some strategy and some education.

When comparing NVIDIA with other solutions or other vendors like AWS, Google, Cerebras, I find that they are pretty much the leader in everything possible because they have the best hardware. They are not really a software company actually, because they prefer to work with their channel resellers and channel partners.

NVIDIA is very profitable because they don't really have a huge sales team. They basically work with everybody and anybody other than AMD is obviously a competitor, but they are still partnering with every possible supplier out there to push the envelope as best they can with innovation. I would say they are vastly outperforming everybody.

If you just look at the stock market, it has been that way for so long and now that they are as big as they are, people are excited to see where it goes, but they are wondering how much more it can grow. I would say they just need to help teach people as much as they can about how this information and technology works. They have a pretty good training and certification program, but many people don't know that it exists unless you already work there or work with someone who works there.

People who are trying to get in the door have to think of it as a numbers game. I hope that NVIDIA would make these resources more publicly available and say, "You want to learn how to use AI Enterprise? Here are the resources." They have these classes, but unless you know somebody, you are not going to really know where to study. They have exams that are about $100, but sometimes people's companies can reimburse them for that type of thing. That would be something I would encourage NVIDIA to continue to invest in. Their solution is going to perform better than anybody out there by far. However, they are going to also be pretty expensive, so you have to compare and contrast that price with the performance.

My advice to others looking into NVIDIA AI Enterprise is to learn as much as they can and ask the right questions and be as open-minded as they can because this is a pretty new product, but it has a lot of upscale potential and it is going to create value for just about anybody. You have to be open-minded to that.

The integration of NVIDIA AI Enterprise with AI frameworks on my project is basically the most important piece of the AI framework because it takes the AI possibilities and actually brings them to reality. It gives you the full capabilities that you would not have access to if you were not running this software because the GPUs alone are just there to help run parallel processor workloads. They are there to basically be resources to handle very intense streams of information running on the server. They are not really built by themselves to be completely optimized and customized without software on top of it. You have to run NVIDIA software to get the full benefit of the APIs, which are the application programmable interfaces, and all the specific use cases that you want, whether it is modeling a digital twin, which is what Omniverse does, and Omniverse is also a bundle.

The thing about AI Enterprise is that it is an overarching term. There are several other AI softwares that NVIDIA has that they are actually running promotions with. When you buy AI Enterprise, you also get access. It changes on a somewhat regular basis, but there are promotions going on. Those promotions are additional packages such as SDKs or software development kits that have the ability to run more things. You might have heard of NVIDIA Omniverse or Run AI—those are two of the most common ones. They have had these deals where depending on what kind of GPU model you are getting, if you get the AI Enterprise software license, you are actually going to get the software, but you are also going to get additional software that is all tied together with AI Enterprise.

I have NVIDIA AI Enterprise deployed in a hybrid model because when I was working with the federal government, security is a top priority. If I did do cloud, it would be a hybrid cloud because it would require some on-site presence with a little bit of remote or cloud orchestration. Hybrid is definitely the approach, and also SaaS because that gives the customer the ability to use it as they go with a consumption model without needing to pay for large upfront costs. Cloud can be expensive because if you don't keep track of your cloud resources, you can spend a lot more money than you anticipate. People are moving to the cloud.

My direct team using NVIDIA AI Enterprise is between 10 to 15 people, but we were supporting an entire sales organization that is 10,000 or more people. The actual team that was the specific sales team was about 10 to 15 and pretty much everybody is using it as best they can.

I would rate this product a 9 out of 10.

A specific example of how I am using NVIDIA AI Enterprise is a RAG-based architecture where I use NVIDIA embed models and NeMoTron embed models from NVIDIA AI Enterprise. I deploy LLMs locally, including Gemma 26 or Llama models. I use agents through agent flow from NVIDIA AI Enterprise, and I project digital humans using NVIDIA AI Enterprise software.

I have noticed that most of my clients have unique use cases in medical fields. Sometimes for training models, I leverage NVIDIA AI Enterprise.

The deployment support from NVIDIA AI Enterprise helps my projects significantly. Timely support helps every team and gives us the opportunity to explore and implement solutions.

NVIDIA AI Enterprise optimizes performance through TensorRT models, which improve the speed and throughput of the models.

NVIDIA AI Enterprise has positively impacted my organization by improving productivity, response time, and overall GPU performance. It optimizes models and enhances their capabilities.

The specific outcomes and metrics I have seen include faster deployment times, reduced costs, and improved model accuracy.

The accuracy and reliability of output from NVIDIA AI Enterprise are excellent.

The scalability of NVIDIA AI Enterprise is impressive.

I would rate this review 8 out of 10.