On this episode of the Futurum Tech Webcast – Interview Series, I am joined by Greg White, Senior Director, Solutions Marketing at Nutanix and Pritish Nilangi, Product Marketing Manager, at AMD, for a conversation on how Nutanix’s software-defined storage and AMD processors are helping companies run modern applications in hybrid and multi clouds.
In our conversation, we discussed the following:
- How hybrid clouds have changed hyper-converged infrastructure (HCI) over the years, and how HCI pioneer Nutanix has adapted to that change
- What we mean by modern apps in the context of hybrid multi-clouds
- The role server technology plays in software-defined storage and security
- What the rise of interest in generative AI means to software and hardware vendors
For more information, visit www.nutanix.com/hpe.
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Transcript:
Dave Raffo: Hello, welcome to another episode of the Futurum Tech Webcast. I’m Dave Raffo, your host for this one, and I’m here with Greg White of Nutanix and Pritish Nilangi of AMD. So let’s start out by just talking a little bit about your roles, what you do for your respective companies. Give us a quick rundown on your specific job roles. So Greg, let’s start with you.
Greg White: Yeah, thanks so much. Excited to be here. I’m at Nutanix covering our solutions marketing. So we work across the various capabilities of our platform to bring to market solutions for things like end user computing, robo, business critical apps and databases. Really, you name it, we’re working to put together those solutions that customers are looking for.
Dave Raffo: Okay, Pritish?
Pritish Nilangi: Thanks everyone for having me here. My name is Pritish. I’m the product marketing manager here at AMD, part of the server business unit, and I manage, I work mostly on the virtualization infrastructure.
Dave Raffo: Okay. So Pritish, you work with several different AMD software partners?
Pritish Nilangi: Yes, yes. Thanks. Yes, I work with most of our ISB partners and mainly the Nutanix here.
Dave Raffo: Okay, great. So Greg, Nutanix made hyper-converged infrastructure, what we now call HCI, popular. It’s probably, what? About a dozen years ago. The IT landscape was different than. Public and hybrid and multi-cloud use, they weren’t nearly as prevalent. They’ve grown considerably, and now Nutanix positions itself as a hybrid multi-cloud company. So how has the rise of the hybrid and multi-cloud impacted HCI use cases? And how has Nutanix adapted to that?
Greg White: Yeah, thanks so much. We’re excited. It’s been an interesting journey, really starting out trying to make infrastructure invisible and take a lot of the pain, the manual pieces, out and automate that. And we were able to do that successfully with some initial key workloads, like VDI was a big one. And what we’ve seen as organizations have matured, they’ve started to bring the public cloud into what they’re doing. They’re looking for a way to bridge that so they don’t get back into that silos of infrastructure. And so what we’ve been able to do is use HCI, deploy that on a public cloud, deploy it on a service provider, just the same way they do on prem, and really bring all that together. And so it opens up, to really get to your question around the use cases, we’re seeing some of the initial ones being the ability to burst to cloud.
Like say you’ve got some VDI users for a particular time of year, being able to do DR to the cloud is a big one, and being able to really move workloads around. So being able to take advantage of the things that you can do in a public cloud and then move it back on prem if you want to, and having to do all that without refactoring. And so that’s some of the hybrid use cases we’re seeing, but we continue to see more and more workloads beyond VDI like databases and apps moving to HCI as well.
Dave Raffo: So we talk a lot and we hear a lot about modern apps these days with containers and cloud native and things like that. But from your standpoint, what do we mean exactly when we say modern apps? What apps are we talking about?
Greg White: Yeah, there are two big spectrums we look at. Clearly what you’re talking about, the containers, Kubernetes, the way people are designing apps in a new way so that they’re much easier to be portable, the things that we’ve talked about a little bit before. We’re also seeing more, when you say modern apps, kind of modern approaches for current apps. So we’re seeing a lot more databases and performance applications moving to a cloud platform. We just did a customer survey where… Over the last couple of years we’ve done a customer survey, and a couple of years ago it was about 69% of our customers were using databases on our platform, and now it’s over 90%. So those performance workloads are moving as a modernization of them occur, and then also the containers piece. And that’s an exciting place where I think a lot of this is going with the new kinds of apps that are being developed, AI and others. The container piece is becoming more and more important.
Dave Raffo: So how has Nutanix changed the cloud platform to meet the needs of these new apps?
Greg White: So a big area is really looking at what we did for virtualization and simplifying and automating a lot of the processes in the backend, doing that for the data services for containers. So we’re able to provide protection, automate a lot of that, make it easier for the developers to really create and know that they’ve got an enterprise-grade app while they’re not being slowed down by having to do everything manually or wait on a lot of work to be done in the background by other teams.
Being able to bring different types of storage onto the platform, in the past we’ve had to have silos for our objects, for our files, for our blocks. Being able to have all those different types of data on our same platform really makes that app development and those more modern apps a lot more powerful now they can take advantage of file storage and object storage as a part of them building that app, and then really optimizing the platform so that we can adapt to different types of apps. So we can now do storage heavy nodes, compute only nodes. So if an application like a database that has licensing challenges around sockets, then you could have more of a compute heavy node and then have a storage node available there. And so there are ways to optimize our platform for different applications now too.
Dave Raffo: Okay, great. So we know Nutanix and HCI is software defined infrastructure and storage, but obviously you still need the underlying hardware. So Nutanix partners with all the major server vendors, close partnership with HPE, which is now outer with its ProLiant DX Gen 11 servers, and they use other servers on the market now. They use the latest AMD chip technology, the EPIC 9004, commonly known as Genoa, the code name anyway. So Pritish, maybe you could tell us a little bit about these chips and what new capabilities do they bring that helps hybrid and cloud native workloads?
Pritish Nilangi: Yes, definitely. So I mean here at AMD, we started into data center business about three, four years ago with Rome, our first generation of Epic CPU for enterprise solutions. And since then we have consistently delivered to our customers year by year, and hats off to our leadership and engineering team for that consistent execution. But when we come to general IT or hyperconverged infrastructure and virtualization, what we have seen from our customer is the performance and the total cost of ownership really matters to them. I mean, if you take one step back and look at the industry right now, every CIO is thinking, “Okay, fine, how do I keep my infrastructure updated so that not only it addresses to current applications, apps, modern apps, but also it handles the applications of the same workload from two years from now or three years from now.”
That’s every IT engineer’s… The question is like, okay, fine. Will my infrastructure handles that applications coming out one year or two years from now? I think that’s where AMD stands out with our fourth generation of Epic CPU, which was launched late last year, but we have leadership performance and security. If you look at per code based performance or per word based performance, we have great results there. But also what we offer to our customer is that you not only get all these great performance and TCO, but also you’ll get the most secure with underlying feature that is your CPU and your infrastructure is secured with our top of the notch Infinity Guard security features.
Dave Raffo: Yeah, I mean we can’t have a data center or cloud conversation today without addressing security. So you mentioned a few features. Anything else in those chips that help with security? How big of a role is the security?
Pritish Nilangi: Exactly. I want to code this revised code from Spiderman movie, right? With great infrastructures comes great security responsibilities or security threats. With that being said, yes here at AMD, with every Epic CPU, you get Infinity card, which is our enterprise security solutions. And then one of the main feature I want highlight here is SEV, our secure encrypted virtualization. What this means is that this is a hardware-based security provided by the CPU or silicon, whereas wherein here, I think, the CPU assigns an individual unique code for each of the virtual machines running on that infrastructure, which is invisible to the hypervisor running that it’s on or even the IT admin itself. So that allows your bad agent or rogue agent in your infrastructure, and then it’s impossible to get into those VMs. And then this is available across all the SKUs. All they have to do is just turn on this feature when they’re setting up their infrastructure.
Dave Raffo: I guess you can’t get more secure than Spiderman, right?
Pritish Nilangi: Yeah. We are the friendly neighborhood Spiderman.
Dave Raffo: So another topic that seems to have become mandatory in any IT discussion is AI and machine learning. So where does AI ML fit in with the latest Nutanix and AMD products and where do you see that technology going? We’ll start with Greg on this one.
Greg White: Yeah, yeah, sure. And it’s a really fun topic. AI ML’s been around for a while as far as we’re concerned. It’s been a key part of our product. We use it to help do forecasting and make sure we’ve got our best performance in the way that the platform runs. But we’ve also had a bunch of customers that have been using it for things more like your traditional video inferencing, whether that’s checkout or we have a construction company that uses it to see where workers are wearing their safety gear or not and if they’re in the wrong place. So it’s kind of those traditional models we’ve been around. But obviously the big exciting new thing is the generative piece, and we’re starting to really see that pick up with things where organizations are looking to keep ahold of their data and they want to query a database of regulations before they can make a decision on a loan or use it for chatbot for support within a particular region or state.
And so the new things are those new models, but kind of still a workload. And then the other piece is really seeing this in different places, so out at the edge training model, some maybe taking a model that’s been built in the cloud, training it on your data on prem, and then deploying it at a branch office, like I was talking about. So lots of exciting ways that people are looking at this now and in different locations. And so really being able to have the ability to move your workload around, have the data with it, still have that kind of enterprise grade approach wherever it’s going to be, is all something that we’re seeing and seeing how hyperconverged a cloud platform really can help with, as this new technology matures and as people really figure out how they’re going to use it.
Dave Raffo: As you say, it’s been around a while, but I imagine that you didn’t have a whole lot of customers specifically asking for it probably right until the last year or so.
Greg White: Yeah, on the generative piece for sure.
Dave Raffo: Generative side? Yeah.
Greg White: Yeah, it’s exploded. Yeah.
Dave Raffo: Yeah. And what about on the hardware side? Where does it fit with the servers and chips, Pritish? How does that impact AMD?
Pritish Nilangi: Yes. So as you mentioned earlier, yeah, no conversation is complete without AI and machine learning, and it’s a great time to be in here to see this huge development in a very short period of time. So here at AMD, we have a broad portfolio of training and inference computing engines and a deep ecosystem of AI partners and co-innovation.
So I just want to highlight, a couple of months ago we had an AI day at AMD and we had our partners on stage talking about the results and the performance they’re seeing with the AMD, like Hugging Face. And also, I just want to highlight this. When we talk about the AI inferencing and different models, yes, it’s great to run on GPU or you need that extra power, but we have also seen some of our partners, like NeuralMetrics or ThirdEye, who have run these models or AI inferences on the Epic CPU itself, and then they have seen great results.
So with that said, whoever’s watching this, if you guys have a wait time of 12 months or over that for your GPUs try it on AMD CPU. You’ll get the same and great results at the fraction of the cost. Yeah.
Dave Raffo: Yeah. I mean that’s a good point because a lot of technology products, when they talk about AI, we say, “Well, what does that mean?”
“Well, we support GPU,” but yeah, obviously there’s other ways of doing it and there’s a lot more to generative AI than just the supporting GPUs.
Pritish Nilangi: Yes. If I can add, especially in the AI and machine learning field, so we have the CDNA 3, which is the next generation AI accelerator architecture. It’s a dedicated accelerator engine for AI and any other workloads which requires high compute. And with our 3-D packaging or 3-D v-cache with the fourth generation AMD CDNA, right, you get the best optimized performance and power efficiency.
Dave Raffo: So Greg, Nutanix is a software defined architecture, software defined storage. You guys make that plain, that you’re not a hardware vendor. You’re software vendor, and even though the hardware is not yours, but how much difference does the underlying hardware make when you’re running the Nutanix Cloud platform?
Greg White: Yeah, it’s really, really important. And if you start, one of the things we talk about is really being able to have that flexibility and choice to deploy the way you need to. And so different form factors are important when it comes to types of clusters, scaling, like I talked about, storage or compute heavy, or in an edge site versus in a data center, so having an option. A variety of form factors is huge from the processor level, really being able to take advantage of those new developments that come, being able to use both the performance but also the efficiency, the density.
And we’re really focused on power consumption and we’ve shown HCI bringing a lot of value to organizations really trying to reduce their power and cooling and all that. And as a result, carbon is a part of that, and so their places. That feeds into the hardware piece, and then the security piece that Pritish talked about is important. And so we really want to be able to take advantage of all of that. But then the hardware piece, like an HPE, like an AMD, they bring key elements that really support what we do on the software side and they bring some good management and pieces along with that, too, that we can tie into. And so it really creates that full solution, the software with the optimal platform to run on it.
Dave Raffo: Okay, great. So thanks a lot guys. It has been very, very enlightening and you’ve been watching another episode of Futurum Tech Webcast, so if you like what you see, hit the subscribe button and we’ll see you on the next episode. So thank you very much for watching.
Other insights from The Futurum Group:
Nutanix .NEXT 2023: A Focus on Multi-Cloud
HPE Extends GreenLake Private Cloud Portfolio
The Power of IaaS: Simplifying Your Multicloud Management
Author Information
Dave’s focus within The Futurum Group is concentrated in the rapidly evolving integrated infrastructure and cloud storage markets. Before joining the Evaluator Group, Dave spent 25 years as a technology journalist and covered enterprise storage for more than 15 years. He most recently worked for 13 years at TechTarget as Editorial Director and Executive News Editor for storage, data protection and converged infrastructure. In 2020, Dave won an American Society of Business Professional Editors (ASBPE) national award for column writing.
His previous jobs covering technology include news editor at Byte and Switch, managing editor of EdTech Magazine, and features and new products editor at Windows Magazine. Before turning to technology, he was an editor and sports reporter for United Press International in New York for 12 years. A New Jersey native, Dave currently lives in northern Virginia.
Dave holds a Bachelor of Arts in Communication and Journalism from William Patterson University.
