NVIDIA AI Enterprise

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.

My main use case for NVIDIA AI Enterprise  involves deploying RAG models, LLMs, and NVIDIA Ingest. I also use audio models with NVIDIA Riva and Omniverse for digital twin applications. These use cases support retail floor assistants, research assistants, and multiple agents.

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 best features NVIDIA AI Enterprise offers are ease of use, deployment support, and scaling and performance optimization.

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.

To improve NVIDIA AI Enterprise, I feel the debug point for the digital twin should be more optimized. The rest of the product performs adequately.

I have been using NVIDIA AI Enterprise for more than four years.

The customer support of NVIDIA AI Enterprise can be improved because the response time is slightly slow.

I have seen a return on investment through NVIDIA AI Enterprise. A significant amount of money is saved by using local models, which avoids output token costs.

My experience with pricing, setup cost, and licensing for NVIDIA AI Enterprise is positive.

I did not evaluate other options before choosing NVIDIA AI Enterprise because I had a partnership with NVIDIA.

Regarding NVIDIA AI Enterprise's AI capabilities, I believe its governance and security are strong.

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.

I have been using NVIDIA AI Enterprise  for two to three years and was first introduced to this product a couple of years ago through an NVIDIA sales representative that I was working with at Dell Technologies, supporting numerous large-scale AI and high-performance computing products with NVIDIA AI Enterprise .

NVIDIA VGPU was one of the compute layers that has been one of the most common main use cases for NVIDIA AI Enterprise. It enables multiple GPUs to share different virtual machines and optimizes resource utilization while condensing hardware operating costs. Because NVIDIA AI Enterprise is typically sold on a per-GPU license, it is important that customers get the best bang for their buck, and NVIDIA VGPU for compute nodes has really been helpful. I have also used this in a number of large-scale RFPs and RFIs.

I primarily work with AI workloads that are on a hybrid cloud model because the public cloud lacks a secure posture that is required for organizations such as the Department of Defense and military organizations. The private cloud, while it is very secure, is also quite expensive. The hybrid approach is very helpful with primarily on-prem infrastructure for rack integration but also some remote connectivity options. Everything also connects via DHCP, which is a dynamic host control protocol that allows customers to use things such as PuTTY and other VS Code type platforms to essentially SSH or remote into a desktop server.

I have also been using a couple of other software development kit libraries including NVIDIA NeMo, which is one of our data curation tools that helps clean the data and allows for model training and fine-tuning, and NVIDIA AI Blueprints are very important, allowing for retrieval augmented generation or RAG. Model training and data curation are very important as well.

There is a large range of libraries offered by NVIDIA AI Enterprise. These catalogs give you all of the information necessary to securely run AI workloads. That has been a very important use case, such as NeMo for the data curation engine for retrieval augmented automated generation, and there are a couple of other use cases such as TensorRT, which is a built-in library for Jupyter notebooks, providing resources for developing the code and the programming. There are also other options available such as NVIDIA for Digital Twins that gets you interested in building a virtual layer to a physical data center, with various APIs available such as NVIDIA Base Command Manager , and many libraries available. The vast majority of these libraries are open source and can be found on tools such as GitHub  and GitLab .

NVIDIA AI Enterprise has impacted my organization positively for a number of reasons. There has been a lot of optimization when it comes to researching organizational information because we have consolidated sites such as SharePoint , and NVIDIA AI Enterprise helps us access resources much quicker without needing to search the web for article after article. That has been very helpful. Additionally, there has also been productivity gains in optimizing workloads with retrieval augmented generation and running demos on the AI workstation, the laptop, leading to a 200 percent increase in productivity.

The accuracy of NVIDIA AI Enterprise has been exceptional, particularly when using generative AI such as retrieval augmented generation. The platform is built on reinforcement learning and model training with extensive libraries, making accuracy and reliability standout features. I believe this to be one of the best advantages of NVIDIA AI Enterprise, and the training continues to reduce errors. While models are never perfect, as humans and data curation are not perfect, I do believe that increased customer support, such as a real-time support desk, would help provide customers with the right information to support this type of platform.

There should be more marketing presence for NVIDIA AI Enterprise. There are numerous training options available, but I feel that many people do not always know where to go because there are so many resources. I recommend creating a weekly or monthly newsletter depending on the subscription type, as there are different levels and layers of NVIDIA AI Enterprise software. The best approach is to make information widely accessible and provide relevant training and content not just for software engineers and developers but for a wide range of audiences.

To further emphasize the need for improvements, I think NVIDIA AI Enterprise should add more marketing, training, and collaborative material. It would also be very helpful to have people available for online chats to answer basic questions for newcomers. Investing in our youth as they are the future is also important; K through 12 schools and universities should have access to this type of information.

The governance and security of NVIDIA AI Enterprise need improvement. Some security features such as zero trust architecture or ZTA are crucial because everyone needs a secure software solution. While NVIDIA AI Enterprise does implement secure hardening of endpoints, it lacks all federal compliance certifications such as FIPS, which governs cryptography and the installation of cryptographic keys onto hard drives. FIPS 140-2, FIPS 140-3, data at rest encryption, and other security measures are necessary additions to NVIDIA AI Enterprise software, especially for US federal government clients such as the Department of Defense, which would enhance governance, surveillance, and security.

Reinforcing the need for improvements, I see a requirement for more human contact to work on support tickets. It would be beneficial if NVIDIA AI Enterprise allows customers to quickly reach someone for support without delays. I have experienced situations with Dell customers where support can bounce back and forth, creating challenges that need to be reduced for better efficiency.

I have been using NVIDIA AI Enterprise for two to three years.

NVIDIA AI Enterprise is a stable platform, releasing quarterly updates that customers can access.

The scalability of NVIDIA AI Enterprise is absolutely incredible because it layers across numerous GPUs and racks. I have designed systems with up to 12 compute racks, four storage racks, and several networking cables and cards, which are crucial. I have observed NVIDIA AI Enterprise scaling up to at least 512 GPUs simultaneously.

Customer support varies based on the support level purchased, whether it is ProSupport Plus with a mission-critical four-hour response. While this level guarantees quick access, sometimes there are delays as support can bounce between Dell, NVIDIA, and other involved partners and vendors. I believe there is room for improvement regarding transparency and communication in customer support.

I would rate customer support a seven, as there are metrics assessing effectiveness, time to value, and return on investment for customers. However, there have been delays in communication and responsibilities between companies such as Dell and NVIDIA, creating confusion regarding who owns specific responsibilities. I would like better communication between both parties, which would require investing in highly skilled AI services departments and customer support, including the online chat I previously mentioned.

I was previously using a combination of Red Hat OS and other orchestration platforms on Linux Ubuntu , which the federal government primarily utilizes. While Red Hat is crucial and works across many servers, it is not always the latest or most advanced, and its licensing costs have become expensive. The same situation applies to VMware private cloud foundations, where costs also escalated.

The return on investment has shown significant money saved and time needed. There has not been a reduction in employees, and nobody wants their job to be replaced by AI in any capacity. However, with GPUs, especially through RunAI, the GPU orchestration platform facilitates increased effectiveness and efficiency. NVIDIA has invested in GPU orchestration by acquiring Slurm, a popular job scheduling tool for high-performance computing, providing roughly a 250 percent return on investment. Millions of dollars are being reinvested into hardware, and savings from GPU orchestration are now allocated for power and cooling operations, such as liquid-cooled and air-cooled data center GPUs.

I am not too involved in the pricing, setup cost, and licensing process as a solution architect. I am responsible for creating the bill of materials, detailing items needed for compute servers, storage nodes, and networking fabric. The account team, including the account executive, sales executive, and storage executive, translate technical components into list pricing and discounts. I am aware that NVIDIA has promotions, including bundles for Omniverse and RunAI for GPU orchestration targeted at specific types of GPUs, which typically show up quarterly. NVIDIA AI Enterprise is structured as a per-license GPU cost.

I evaluated other options before choosing NVIDIA AI Enterprise, as discussed previously.

My advice for others considering NVIDIA AI Enterprise is to conduct thorough research and discuss with their facility team. Understanding the rack layout, data center size, floor height, and humidity or CFM in the room is essential. You must determine whether you have the plumbing for AI data center needs, the capacity to support the weight of heavy racks (typically two to 3,000 pounds), and essential infrastructure components such as shock pallets, doors, heat exchangers, and chillers. Once these components are solidified, you can have conversations regarding the appropriate type of NVIDIA AI Enterprise support based on your GPUs.

NVIDIA AI Enterprise platform continues to evolve over time, and the more often customers are able to go online and teach themselves about these platforms the better. NVIDIA Omniverse Enterprise  is a collaborative environment for 3D workflows. When you are making a digital twin, you are basically creating a 3D layer that virtualizes a hardware infrastructure platform, bringing the ideas to life.

NVIDIA AI Enterprise is primarily deployed in my organization through a hybrid cloud, which I have discussed earlier. Hybrid cloud combines both private and public on-prem solutions, offering the best of both worlds. Data that needs to stay on-prem can live in a secure environment while allowing for archival or secondary storage in the cloud, which can reduce costs. Working with a company such as Equinix for colocation of data back and forth plays a crucial role in the deployment as it provides a scalable, flexible approach, with private cloud environments making the most sense for the customers I work with. I am providing this review with an overall rating of 9.