Company Overview and AI Challenges we address with Nutanix
GenAI’s rapid advancement and impact present a significant challenge for enterprises seeking to leverage its potential. Nutanix helps businesses transition from GenAI possibilities to production with its Nutanix Enterprise AI (NAI) solution, a full-stack AI infrastructure designed specifically for IT needs. The NAI offering provides a standardized inferencing solution centered around a model repository, allowing for creating secure endpoints with APIs for GenAI applications, spanning from edge to public clouds.
Mike Barmonde, the Sr. Product Marketing Manager for Nutanix AI products, presented an overview of Nutanix and its approach to addressing AI challenges. The presentation focused on how Nutanix simplifies AI inferencing for IT, highlighting that many organizations struggle to scale their AI initiatives. Nutanix Enterprise AI provides a four-step process to deploy AI infrastructure, including Kubernetes selection, hardware choice (with options for public cloud or air-gapped environments), LLM deployment from various sources, and the creation of secure endpoints, all managed from a central location.
The presentation emphasized the comprehensive nature of Nutanix’s AI infrastructure approach, extending from LLMs down to the underlying hardware. Nutanix’s goal is to streamline the entire process, enabling seamless Day 2 operations. This allows IT professionals to centralize their AI infrastructure and provide a better experience for their developers and application owners.
Presented by Mike Barmonde, Sr. Product Marketing Manager, Nutanix. Recorded live in Santa Clara, California, on April 24, 2025, as part of AI Infrastructure Field Day. Watch the entire presentation at https://techfieldday.com/appearance/nutanix-presents-at-ai-infrastructure-field-day-2/ or https://techfieldday.com/event/aiifd2/ for more information.
Transcript
My name is Mike Far Andy. I'm product marketing for our AI products here at Nutanix. So I'm gonna take the next 89 minutes.
I'm just kidding, I'm not gonna take that long. You're like, wait a minute. Hold on.
No, I'm, I'm very happy to be with you. We're excited to be back. I know it's been a while.
And let's jump right in. Um, we have a quick agenda. We're gonna go through the company overview.
That will be very interesting. Just kidding. And then we're gonna really dive into the good stuff, um, between a look at Nutanix Enterprise ai, a demo, and then a wrap up.
Okay, great. Let's jump in. So we like to think that Nutanix, amongst the other things we do is AI inferencing made for it.
This is very specific, right? You can make AI for data scientists, for other ai, for other things. We make ai, especially for it.
It's what we do. It's what all the other stuff we do makes when we make it work. We're gonna jump into this a little more.
So, um, us at a glance, we're a public company. We have been for a while. We have about 27,000 customers.
Uh, we have a best in class, um, NPS score. We have a very strong Nutanix community. This is my favorite part about, uh, um, 147,000 members.
We have a big conference going on in a couple of weeks in Washington, DC where they're gonna come together and we're gonna talk about some great stuff. So, um, I don't think there's, there's much to talk about there, but that's kind of who we are at a glance. So let's jump into this.
How many of you, and this is gonna be rhetorical, do or have deployed an LLM at work, maybe AI infrastructure of some kind of work put in A GPU. Try to use your data for AI at work. Yeah, right, right.
I think these are dumb questions, right? I mean, they could be, right? I mean, this is, these are very general things, but I think these are a lot of the questions that we find our customers asking us and how we're trying to build things for them that fit.
So now think about how your answers, how you'd scale some of the things you've done to production at work. It's a very different conversation. Does it feel like this maybe right?
Minecraft seen the new Minecraft movie. You have Flint and steel, and then all of a sudden you get this brand new world and it's like there's CLI and there's GitHub repos and there's weird storage files you have to deal with. There's this new world of things that you have to deal with, and production doesn't actually work like that a lot of the time.
So in fact, we did a huge poll and we do this every year called the Enterprise Cloud Index. And 91% of over a thousand customers that we pulled said that infrastructure for AI is a huge thing that they need to improve in order to move forward with their AI initiatives. It's a really big deal for them, which means that, you know, it really requires inferencing.
It has to be centralized there, and we have to make it simple. It has to be productized. No BS just smiles.
That's a very hard thing to Do. The clouds promise that everyone promises that. I will say we're not promising that what we're trying to do is align it to make it easier and better.
So I'm gonna give you a quick overview and then we're gonna dive in to the technical people and how this works. And this will kind of be your primer, uh, as we jump off into the deep end. So our product, Nutanix Enterprise AI is a four step way to really deploy AI infrastructure.
Number one, you basically choose your Kubernetes. It could be any CN CF compliant Kubernetes, your choice of hardware, Dell, hp, Lenovo, take your pick. It could be a public clouds or it could be in an air gapped environment, meaning you can take this and run it in an air gapped way.
Next, once Nutanix Enterprise AI is deployed, you get this pretty interface where you get your choice to deploy the LLMs you choose. Now we partner with amazing LLM, uh, libraries like hugging face or even nvidia. But you can also choose and upload your own, which we think is critical.
Going back to the air gap piece, when you're talking about cutting edge things or things you need to do in a private and sovereign way, once you choose this, you now can create a secure endpoint. Now what this is, it's an LLM and an API that together make an endpoint. This endpoint is now then able to be leveraged with your Gen AI applications and it's open AI compliant, meaning a lot of the applications that you put out there, you'll be able to leverage with this to take advantage of the LLMs for their applications.
Centralized way last, you can test and validate this as an IT person and then send it over to your developers, application owners, those type of people to leverage this from a centralized way. So again, back to what Alistair said at the first, I think this is really important. I AI infrastructure is a very interesting term.
Does it mean hardware? Does it mean LLMs? What does it mean?
We think it means everything from the LLMs and below is what we think infrastructure is to make the application and data work right back to the meme. So, and with this, you can operate and manage day zero, day one or day two.
