from around the globe it's the cube with digital coverage of Red Hat summit 2020 brought to you by Red Hat welcome back to the cubes coverage of Red Hat summit 2020 of course this year it's rather than all coming to San Francisco we are talking to red hat executives their partners and their customers where they are around the globe happy to welcome back one of our cube alumni Lisa Spellman who's a corporate vice president and general manager of the Intel Xeon and memory group Lisa thanks so much for joining us and where are you joining us from well thank you for having me and I'm a little further north than where the conference was gonna be held so I'm in Portland Oregon right now excellent yeah we've had you know customers from around the globe as part of the cube coverage here and of course you're near the mothership of Intel so Lisa you know but let's start of course you know the Red Hat partnership you know I've been the Intel executives on the keynote stage for for many years so talk about to start us off the Intel Red Hat partnership as it stands today in 2020 yeah you know on the keynote stage for many years and then actually again this year so despite the virtual nature of the event that we're having we're trying to still show up together and demonstrate together to our customers and our developer community really give them a sense for all the work that we're doing across the important transformations that are happening in the industry so we view this partnership in this event as important ways for us to connect and make sure that we have a chance to really share where we're going next and gather feedback on where our customers and that developer community need us to go together because it is a you know rich long history of partnership of the combination of our Hardware work and the open-source software work that we do with Red Hat and we see that every year increasing in value as we expand to more workloads and more market segments that we can help with our technology yeah well Lisa you know we've seen on the cube for for many years Intel strong partnerships across the industry from the data centers from the cloud I think we're gonna talk a little bit about edge for this discussion too though edge and 5g III think about all the hard work that Intel does especially with its partnership you know you talked about and I think that the early days of Red Hat you know the operating system things that were done as virtualization rolled out there's accelerations that gone through so when it comes to edge in 5g obviously big mega waves that we spend a lot of talking about what's what's Intel's piece obviously we know Intel chips go everywhere but when it comes to kind of the engineering work that gets done what are some of the pieces that Intel spork yeah and that's a great example actually of what I what we are seeing is this expansion of areas of workloads and investment and opportunity that we face so as we move forward into 5g becoming not the theoretical next thing but actually the thing that is starting to be deployed and transformed you can see a bunch of underlying work that Intel and Red Hat have done together in order to make that a reality so you look at they move from a very proprietary ASIC based type of workload with a single function running on it and what we've done is drive to have the virtualization capabilities that took over and provided so much value in the cloud data center also apply to the 5g network so the move to network function virtualization and software-defined networking and a lot of value being derived from the opportunity to run that on open source standard and have that open source community really come together to make it easier and faster to deploy those technologies and also to get good SLA s and quality of service while you're driving down your overall total cost of ownership so we've spent years working on that together in the 5g space and network space in general and now it's really starting to take off then that is very well connected to the edge so if you think about the edge as this point of content creation of where the actions happening and you start to think through how much of the compute or the value can I get out at the edge without everything having to go all the way back to the data center you start to again see how those open standards in very complex environments and help people manage their total cost of ownership and the complexity all right Lisa so when you're talking about edge solutions when I've been talking to Red Hat where their first deployments have really been talking to the service providers really I've seen it as an extension of what you were talking about network functions virtualization you know everybody talks about edges there's a lot of different edges out there the service providers being the first place we see things but you know all the way out even to the consumer edge and the device edge where Intel may or may not have you know some some devices there so help us understand you know where where you're sitting and where should we be looking as these technologies work you know it's a it's a great point we see the edge being developed by multiple types of organizations so yes the service providers are obviously there in so much as they already even own the location points out there if you think of all the myriad of poles with the the base stations and everything that's out there that's a tremendous asset to capitalize on you also see our cloud service provider customers moving towards the edge as well as they think of new developer services and capabilities and of course you see the enterprise edge coming in if you think of factory type of utilization methodologies or in manufacturing all of those are very enterprise based and are really focused on not that consumer edge but on the b2b edge or the you know the infrastructure edge is what you might think of it as but they're working through how do they add efficiency capability automation all into their existing work but making it better so at Intel the way that we look at that is it's all opportunities to provide the right foundation for that so when we look at the silicon products that we develop we gather requirements from that entire landscape and then we work through our silicon portfolio you know we have our portfolio really focused on the movement the storage and the processing of data and we try to look at that in a very holistic way and decide where the capability will best serve that workload so you do have a choice at times whether some new feature or capability goes into the CPU or the Zeon engine or you could think about whether that would be better served by being added into a smart egg type of capability and so those are just small examples of how we look at the entirety of the data flow in the edge and at what the use case is and then we utilize that to inform how we improve the silicon and where we add feature well Lisa as you were going through this it makes me also think about one of the other big mega waves out there artificial intelligence so lots of discussion as you were saying what goes where how we think about it cloud edge devices so how does AI intersect with this whole discussion of edge that we were just having yeah and you're probably gonna have to cut me off because I could go on for a long time on on this one but AI is such an exciting at capability that is coming through everywhere literally from the edge through the core network into the cloud and you see it infiltrating every single workload across the enterprise across cloud service providers across the network service providers so it is truly on its way to being completely pervasive and so again that presents the same opportunity for us so if you look at your silicon portfolio you need to be able to address artificial intelligence all the way from the edge to the cloud and that can mean adding silicon capabilities that can handle milliwatts like ruggedized super low power super long life you don't literally out at the edge and then all the way back to the data center where you're going for a much higher power at a higher capability for training of the models so we have built out a portfolio that addresses all of that and one of the interesting things about the edges people always think of it as a low compute area so they think of it as data collection but more and more of that data collection is also having a great benefit from being able to do an amount of compute and inference out at the edge so we see a tremendous amount of actual Zeon product being deployed out at the edge because of the need to actually deliver quite high-powered compute right there and that's improving customer experiences and it's changing use cases through again healthcare manufacturing automotive you see it in all the major fast mover edge industries yeah now we're really good points they make their Lisa we all got used to you know limitless compute in the cloud and therefore you know let's put everything there but of course we understand there's this little thing called the speed of light that makes it that much of the information that is collected at the edge can't go beyond it you know I saw a great presentation actually last year talking about the geosynchronous satellites they collect so much information and you know you can't just beam it back and forth so I better have some compute there so you know we've known for a long time that the challenge of you know of our day has been distributed architectures and edge just you know changes that you know the landscape and the surface area that we need the touch so much more when I think about all those areas obviously security is an area that comes up so how does Intel and its partners make sure that no matter where my data is and you talk about the various memory that you know security is still considered at each aspect of the environment oh it's a huge focus because if you think of people and phrases they used to say like oh we got to have the fat pipe or the dumb pipe to get you know data back and or there is no such thing as a dumb pipe anymore everything is smart the entire way through the lifecycle and so with that smartness you need to have security embedded from the get-go into that work flow and what people need to understand is they undergo their edge deployments and start that work is that your obligation for the security of that data begins the you collect that data it doesn't start when it's back to the cloud or back in the data center so you own it and need to be on it from the beginning so we work across our Silicon portfolio and then our software ecosystem to think through it in terms of that entire pipeline of the data movement and making sure that there's not breakdowns in each of the handoff chain it's a really complex problem and it is not one that Intel is able to solve alone nor any individual silicon or software vendor along the way and I will say that some of the security work over the past couple years has led to a bringing together of the industry to address problems together whether they be on any other given day a friend or a foe when it comes to security I feel like I've seen just an amazing increase over the past two two and a half years on the collaboration to solve these problems together and ultimately I think that leads to a better experience for our users and for our customers so we are investing in it not just at the new features from the silicon perspective but in also understanding newer and more advanced threat or attack surfaces that can happen inside of the silicon or the software component all right so Lisa final question I have for you want to circle back to where we started it's Red Hat summit this week-long partnerships as I mentioned we see Intel it all the cloud shows you partner with all the hardware software providers and the like so big message from Red Hat is the open hybrid cloud to talk about how that fits in with everything that Intel is doing it's an area of really strong interconnection between us and Red Hat because we have a vision of that open hybrid cloud that is very well aligned and the part about it is that it is rooted not just in here's my feature here's my feature from either one of us it's rooted in what our customers need and what we see our enterprise customers driving towards that desire to utilize the cloud to in prove their capabilities and services but also maintain that capability inside their own house as well so that they have really viable work load transformation they have opportunities for their total cost of ownership and can fundamentally use technology to drive their business forward all right well Lisa Spellman thank you so much for all the update from Intel and definitely look forward to seeing the breakouts the keynotes and the like yes me too all right lots more coverage here from the cube redhead summit 2020 I'm Stu minimun and thanks as always for watching [Music]

(bright music) >> Narrator: Live from Silicon Valley. It's theCUBE, covering Google Cloud Next 17. >> Okay, welcome back, everyone. We're live in Palo Alto for theCUBE special two day coverage here in Palo Alto. We have reporters, we have analysts on the ground in San Francisco, analyzing what's going on with Google Next, we have all the great action. Of course, we also have reporters at Open Compute Summit, which is also happening in San Hose, and Intel's at both places, and we have Intel senior manager on the line here, on the phone, Lisa Spelman, vice president and general manager of the Xeon product line, product manager responsibility as well as marketing across the data center. Lisa, welcome to theCUBE, and thanks for calling in and dissecting Google Next, as well as teasing out maybe a little bit of OCP around the Xeon processor, thanks for calling. >> Lisa: Well, thank you for having me, and it's hard to be in many places at once, so it's a busy week and we're all over, so that's that. You know, we'll do this on the phone, and next time we'll do it in person. >> I'd love to. Well, more big news is obviously Intel has a big presence with the Google Next, and tomorrow there's going to be some activity with some of the big name executives at Google. Talking about your relationship with Google, aka Alphabet, what are some of the key things that you guys are doing with Google that people should know about, because this is a very turbulent time in the ecosystem of the tech business. You saw Mobile World Congress last week, we've seen the evolution of 5G, we have network transformation going on. Data centers are moving to a hybrid cloud, in some cases, cloud native's exploding. So all new kind of computing environment is taking shape. What is Intel doing here at Google Next that's a proof point to the trajectory of the business? >> Lisa: Yeah, you know, I'd like to think it's not too much of a surprise that we're there, arm in arm with Google, given all of the work that we've done together over the last several years in that tight engineering and technical partnership that we have. One of the big things that we've been working with Google on is, as they move from delivering cloud services for their own usage and for their own applications that they provide out to others, but now as they transition into being a cloud service provider for enterprises and other IT shops as well, so they've recently launched their Google Cloud platform, just in the last week or so. Did a nice announcement about the partnership that we have together, and how the Google Cloud platform is now available and running and open for business on our latest next generation Intel Xeon product, and that's codenamed Skylake, but that's something that we've been working on with them since the inception of the design of the product, so it's really nice to have it out there and in the market, and available for customers, and we very much value partnerships, like the one we have with Google, where we have that deep technical engagement to really get to the heart of the workload that they need to provide, and then can design product and solution around that. So you don't just look at it as a one off project or a one time investment, it's an ongoing continuation and evolution of new product, new features, new capabilities to continue to improve their total cost of ownership and their customer experience. >> Well, Lisa, this is your baby, the Xeon, codename Skylake, which I love that name. Intel always has great codenames, by the way, we love that, but it's real technology. Can you share some specific features of what's different around these new workloads because, you know, we've been teasing out over the past day and we're going to be talking tomorrow as well about these new use cases, because you're looking at a plethora of use cases, from IoT edge all the way down into cloud native applications. What specific things is Xeon doing that's next generation that you could highlight, that points to this new cloud operating system, the cloud service providers, whether it's managed services to full blown down and dirty cloud? >> Lisa: So it is my baby, I appreciate you saying that, and it's so exciting to see it out there and starting to get used and picked up and be unleashing it on the world. With this next generation of Xeon, it's always about the processor, but what we've done has gone so much beyond that, so we have a ton of what we call platform level innovation that is coming in, we really see this as one of our biggest kind of step function improvements in the last 10 years that we've offered. Some of the features that we've already talked about are things like AVX-512 instructions, which I know just sounds fun and rolls of the tongue, but really it's very specific workload acceleration for things like high performance computing workloads. And high performance computing is something that we see more and more getting used in access in cloud style infrastructure. So it's this perfect marrying of that workload specifically deriving benefit from the new platforms, and seeing really strong performance improvements. It also speaks to the way with Intel and Xeon families, 'cause remember, with Xeon, we have Xeon Phi, you've got standard Xeon, you've got Xeon D. You can use these instructions across the families and have workloads that can move to the most optimized hardware for whatever you're trying to drive. Some of the other things that we've talked about announced is we'll have our next generation of Intel Resource Director technology, which really helps you manage and provide quality of service within you application, which is very important to cloud service providers, giving them control over hardware and software assets so that they can deliver the best customer experience to their customers based on the service level agreement they've signed up for. And then the other one is Intel Omni-Path architecture, so again, fairly high performance computing focused product, Omni-Path is a fabric, and we're going to offer that in an integrated fashion with Skylake so that you can get even higher level of performance and capability. So we're looking forward to a lot more that we have to come, the whole of the product line will continue to roll out in the middle of this year, but we're excited to be able to offer an early version to the cloud service providers, get them started, get it out in the market and then do that full scale enterprise validation over the next several months. >> So I got to ask you the question, because this is something that's coming up, we're seeing a transition, also the digital transformation's been talked about for a while. Network transformation, IoTs all around the corner, we've got autonomous vehicles, smart cities, on and on. But I got to ask you though, the cloud service providers seems to be coming out of this show as a key storyline in Google Next as the multi cloud architectures become very clear. So it's become clear, not just this show but it's been building up to this, it's pretty clear that it's going to be a multi cloud world. As well as you're starting to see the providers talk about their SaaS offerings, Google talking about G Suite, Microsoft talks about Office 365, Oracle has their apps, IBM's got Watson, so you have this SaaSification. So this now creates a whole another category of what cloud is. If you include SaaS, you're really talking about Salesforce, Adobe, you know, on and on the list, everyone is potentially going to become a SaaS provider whether they're unique cloud or partnering with some other cloud. What does that mean for a cloud service provider, what do they need for applications support requirements to be successful? >> So when we look at the cloud service provider market inside of Intel, we are talking about infrastructure as a service, platform as a service and software as a service. So cutting across the three major categories, I give you like, up until now, infrastructure of the service has gotten a lot of the airtime or focus, but SaaS is actually the bigger business, and that's why you see, I think, people moving towards it, especially as enterprise IT becomes more comfortable with using SaaS application. You know, maybe first they started with offloading their expense report tool, but over time, they've moved into more sophisticated offerings that free up resources for them to do their most critical or business critical applications the they require to stay in more of a private cloud. I think that's evolution to a multi cloud, a hybrid cloud, has happened across the entire industry, whether you are an enterprise or whether you are a cloud service provider. And then the move to SaaS is logical, because people are demanding just more and more services. One of the things through all our years of partnering with the biggest to the smallest cloud service providers and working so closely on those technical requirements that we've continued to find is that total cost of ownership really is king, it's that performance per dollar, TCO, that they can provide and derive from their infrastructure, and we focused a lot of our engineering and our investment in our silicon design around providing that. We have multi generations that we've provided even just in the last five years to continue to drive those step function improvements and really optimize our hardware and the code that runs on top of it to make sure that it does continue to deliver on those demanding workloads. The other thing that we see the providers focusing on is what's their differentiation. So you'll see cloud service providers that will look through the various silicon features that we offer and choose, they'll pick and choose based on whatever their key workload is or whatever their key market is, and really kind of hone in and optimize for those silicon features so that they can have a differentiated offering into the market about what capabilities and services they'll provide. So it's an area where we continue to really focus our efforts, understand the workload, drive the TCO down, and then focus in on the design point of what's going to give that differentiation and acceleration. >> It's interesting, the definition's also where I would agree with you, the cloud service provider is a huge market when you even look at the SaaS. 'Cause whether you're talking about Uber or Netflix, for instance, examples people know about in real life, you can't ignore these new diverse use cases coming out. For instance, I was just talking with Stu Miniman, one of our analysts here, Wikibon, and Riot Games could be considered a cloud, right, I mean, 'cause it's a SaaS platform, it's gaming. You're starting to see these new apps coming out of the woodwork. There seems to be a requirement for being agile as a cloud provider. How do you enable that, what specifically can you share, if I'm a cloud service provider, to be ready to support anything that's coming down the pike? >> Lisa: You know, we do do a lot of workload and market analysis inside of Intel and the data center group, and then if you have even seen over the past five years, again, I'll just stick with the new term, how much we've expanded and broadened our product portfolio. So again, it will still be built upon that foundation of Xeon and what we have there, but we've gone to offer a lot of varieties. So again, I mentioned Xeon Phi. Xeon Phi at the 72 cores, bootable Xeon but specific workload acceleration targeted at high performance computing and other analytics workloads. And then you have things at the other end. You've got Xeon D, which is really focused at more frontend web services and storage and network workloads, or Atom, which is even lower power and more focused on cold and warm storage workloads, and again, that network function. So you could then say we're not just sticking with one product line and saying this is the answer for everything, we're saying here's the core of what we offer, and the features people need, and finding options, whether they range from low power to high power high performance, and kind of mixed across that whole kind of workload spectrum, and then we've broadened around the CPU into a lot of other silicon innovation. So I don't know if you guys have had a chance to talk about some of the work that we're doing with FPGAs, with our FPGA group and driving and delivering cloud and network acceleration through FPGAs. We've also introduced new products in the last year like Silicon Photonics, so dealing with network traffic crossing through-- >> Well, is FPGA, that's the Altera stuff, we did talk with them, they're doing the programmable chips. >> Lisa: Exactly, so it requires a level of sophistication and understanding what you need the workload to accelerate, but once you have it, it is a very impressive and powerful performance gain for you, so the cloud service providers are a perfect market for that, as are the cloud service providers because they have very sophisticated IT and very technically astute engineering teams that are able to really, again, go back to the workload, understand what they need and figure out the right software solution to pair with it. So that's been a big focus of our targeting. And then, like I said, we've added all these different things, different new products to the platform that start to, over time, just work better and better together, so when you have things like Intel SSD there together with Intel CPUs and Intel Ethernet and Intel FPGA and Intel Silicon Photonics, you can start to see how the whole package, when it's designed together under one house, can offer a tremendous amount of workload acceleration. >> I got to ask you a question, Lisa, 'cause this comes up, while you're talking, I'm just in my mind visualizing a new kind of virtual computer server, the cloud is one big server, so it's a design challenge. And what was teased out at Mobile World Congress that was very clear was this new end to end architecture, you know, re-imagined, but if you have these processors that have unique capabilities, that have use case specific capabilities, in a way, you guys are now providing a portfolio of solutions so that it almost can be customized for a variety of cloud service providers. Am I getting that right, is that how you guys see this happening where you guys can just say, "Hey, just mix and match what you want and you're good." >> Lisa: Well, and we try to provide a little bit more guidance than as you wish, I mean, of course, people have their options to choose, so like, with the cloud service providers, that's what we have, really tight engineering engagement, so that we can, you know, again, understand what they need, what their design point is, what they're honing in on. You might work with one cloud service provider that is very facilities limited, and you might work with another one that is, they're face limited, the other one's power limited, and another one has performance is king, so you can, we can cut some SKUs to help meet each of those needs. Another good example is in the artificial intelligence space where we did another acquisition last year, a company called Nervana that's working on optimized silicon for a neural network. And so now we have put together this AI portfolio, so instead of saying, "Oh, here's one answer "for artificial intelligence," it's, "Here's a multitude of answers where you've got Xeon," so if you have, I'm going to utilize capacity, and are starting down your artificial intelligence journey, just use your Xeon capacity with an optimized framework and you'll get great results and you can start your journey. If you are monetizing and running your business based on what AI can do for you and you are leading the pack out there, you've got the best data scientists and algorithm writers and peak running experts in the world, then you're going to want to use something like the silicon that we acquired from the Nervana team, and that codename is Lake Crest, speaking of some lakes there. And you'll want to use something like Xeon with Lake Crest to get that ultimate workload acceleration. So we have the whole portfolio that goes from Xeon to Xeon Phi to Xeon with FPGAs or Xeon with Lake Crest. Depending on what you're doing, and again, what your design point is, we have a solution for you. And of course, when we say solution, we don't just mean hardware, we mean the optimized software frameworks and the libraries and all of that, that actually give you something that can perform. >> On the competitive side, we've seen the processor landscape heat up on the server and the cloud space. Obviously, whether it's from a competitor or homegrown foundry, whatever fabs are out there, I mean, so Intel's always had a great partnership with cloud service providers. Vis-a-vis the competition and context to that, what are you guys doing specifically and how you'd approach the marketplace in light of competition? >> Lisa: So we do operate in a highly competitive market, and we always take all competitors seriously. So far we've seen the press heat up, which is different than seeing all of the deployments, so what we look for is to continue to offer the highest performance and lowest total cost of ownership for all our customers, and in this case, the cloud service providers, of course. And what do we do is we kind of stick with our game plan of putting the best silicon in the world into the market on a regular beat rate and cadence, and so there's always news, there's always an interesting story, but when you look at having had eight new products and new generations in market since the last major competitive x86 product, that's kind of what we do, just keep delivering so that our customers know that they can bet on us to always be there and not have these massive gaps. And then I also talked to you about portfolio expansion, we don't bet on just one horse, we give our customers the choice to optimize for their workloads, so you can go up to 72 cores with Xeon Phi if that's important, you can go as low as two cores with Atom, if that's what works for you. Just an example of how we try to kind of address all of our customer segments with the right product at the right time. >> And IoT certainly brings a challenge too, when you hear about network edge, that's a huge, huge growth area, I mean, you can't deny that that's going to be amazing, you look at the cars are data centers these days, right? >> Lisa: A data center on wheels. >> Data center on wheels. >> Lisa: That's one of the fun things about my role, even in the last year, is that growing partnership, even inside of Intel with our IoT team, and just really going through all of the products that we have in development, and how many of them can be reused and driven towards IoT solution. The other thing is, if you look into the data center space, I genuinely believe we have the world's best ecosystem, you can't find an ISV that we haven't worked with to optimize their solution to run best on Intel architecture and get that workload acceleration. And now we have the chance to put that same playbook into play in the IoT space, so it's a growing, somewhat nascent but growing market with a ton of opportunity and a ton of standards to still be built, and a lot of full solution kits to be put together. And that's kind of what Intel does, you know, we don't just throw something out to the market and say, "Good luck," we actually put the ecosystem together around it so that it performs. But I think that's kind of what you see with, I don't know if you guys saw our Intel GO announcement, but it's really like the software development kit and the whole product offering for what you need for truly delivering automated vehicles. >> Well, Lisa, I got to say, so you guys have a great formula, why fix what's not broken, stay with Moore's law, keep that cadence going, but what's interesting is you are listening and adapting to the architectural shifts, which is smart, so congratulations and I think, as the cloud service provider world changes, and certainly in the data center, it's going to be a turbulent time, but a lot of opportunity, and so good to have that reliability and, if you can make the software go faster then they can write more software faster, so-- >> Lisa: Yup, and that's what we've seen every time we deliver a step function improvement in performance, we see a step function improvement in demand, and so the world is still hungry for more and more compute, and we see this across all of our customer bases. And every time you make that compute more affordable, they come up with new, innovative, different ways to do things, to get things done and new services to offer, and that fundamentally is what drives us, is that desire to continue to be the backbone of that industry innovation. >> If you could sum up in a bumper sticker what that step function is, what is that new step function? >> Lisa: Oh, when we say step functions of improvements, I mean, we're always looking at targeting over 20% performance improvement per generation, and then on top of that, we've added a bunch of other capabilities beyond it. So it might show up as, say, a security feature as well, so you're getting the massive performance improvement gen to gen, and then you're also getting new capabilities like security features added on top. So you'll see more and more of those types of announcements from us as well where we kind of highlight the, not just the performance but that and what else comes with it, so that you can continue to address, you know, again, the growing needs that are out there, so all we're trying to say is, day a step ahead. >> All right, Lisa Spelman, VP of the GM, the Xeon product family as well as marketing and data center. Thank you for spending the time and sharing your insights on Google Next, and giving us a peak at the portfolio of the Xeon next generation, really appreciate it, and again, keep on bringing that power, Moore's law, more flexibility. Thank you so much for sharing. We're going to have more live coverage here in Palo Alto after this short break. (bright music)

>> Hello, everyone. Welcome to theCUBE's coverage of Compute Engineered For Your Hybrid World, sponsored by HPE and Intel. I'm John Furrier, host of theCUBE. I'm pleased to be joined by Krista Satterthwaite, SVP and general manager for HPE Mainstream Compute, and Lisa Spelman, corporate vice president, and general manager of Intel Xeon Products, here to discuss the major announcement. Thanks for joining us today. Thanks for coming on theCUBE. >> Thanks for having us. >> Great to be here. >> Great to see you guys. And exciting announcement. Krista, Compute continues to evolve to meet the challenges of businesses. We're seeing more and more high performance, more Compute, I mean, it's getting more Compute every day. You guys officially announced this next generation of ProLiant Gen11s in November. Can you share and talk about what this means? >> Yeah, so first of all, thanks so much for having me. I'm really excited about this announcement. And yeah, in November we announced our HPE ProLiant NextGen, and it really was about one thing. It's about engineering Compute for customers' hybrid world. And we have three different design principles when we designed this generation. First is intuitive cloud operating experience, and that's with our HPE GreenLake for Compute Ops Management. And that's all about management that is simple, unified, and automated. So it's all about seeing everything from one council. So you have a customer that's using this, and they were so surprised at how much they could see, and they were excited because they had servers in multiple locations. This was a hotel, so they had servers everywhere, and they can now see all their different firmware levels. And with that type of visibility, they thought their planning was going to be much, much easier. And then when it comes to updates, they're much quicker and much easier, so it's an exciting thing, whether you have servers just in the data center, or you have them distributed, you could see and do more than you ever could before with HPE GreenLake for Compute Ops Management. So that's number one. Number two is trusted security by design. Now, when we launched our HPE ProLiant Gen10 servers years ago, we launched groundbreaking innovative security features, and we haven't stopped, we've continued to enhance that every since then. And this generation's no exception. So we have new innovations around security. Security is a huge focus area for us, and so we're excited about delivering those. And then lastly, performance for every workload. We have a huge increase in performance with HPE ProLiant Gen11, and we have customers that are clamoring for this additional performance right now. And what's great about this is that, it doesn't matter where the bottleneck is, whether it's CPU, memory or IO, we have advancements across the board that are going to make real differences in what customers are going to be able to get out of their workloads. And then we have customers that are trying to build headroom in. So even if they don't need a today, what they put in their environment today, they know needs to last and need to be built for the future. >> That's awesome. Thanks for the recap. And that's great news for folks looking to power those workloads, more and more optimizations needed. I got to ask though, how is what you guys are announcing today, meeting these customer needs for the future, and what are your customers looking for and what are HPE and Intel announcing today? >> Yeah, so customers are doing more than ever before with their servers. So they're really pushing things to the max. I'll give you an example. There's a retail customer that is waiting to get their hands on our ProLiant Gen11 servers, because they want to do video streaming in every one of their retail stores and what they're building, when they're building what they need, we started talking to 'em about what their needs were today, and they were like, "Forget about what my needs are today. We're buying for headroom. We don't want to touch these servers for a while." So they're maxing things out, because they know the needs are coming. And so what you'll see with this generation is that we've built all of that in so that customers can deploy with confidence and know they have the headroom for all the things they want to do. The applications that we see and what people are trying to do with their servers is light years different than the last big announcement we had, which was our ProLiant Gen10 servers. People are trying to do more than ever before and they're trying to do that at the Edge as well as as the data center. So I'll tell you a little bit about the servers we have. So in partnership with Intel, we're really excited to announce a new batch of servers. And these servers feature the 4th Gen Intel Xeon scalable processors, bringing a lot more performance and efficiency. And I'll talk about the servers, one, the first one is a HPE ProLiant DL320 Gen11. Now, I told you about that retail customer that's trying to do video streaming in their stores. This is the server they were looking at. This server is a new server, we didn't have a Gen10 or a Gen10+ version of the server. This is a new server and it's optimized for Edge use cases. It's a rack-based server and it's very, very flexible. So different types of storage, different types of GPU configurations, really designed to take care of many, many use cases at the Edge and doing more at the Edge than ever before. So I mentioned video streaming, but also VDI and analytics at the Edge. The next two servers are some of our most popular servers, our HPE ProLiant DL360 Gen11, and that's our density-optimized server for enterprise. And that is getting an upgrade across the board as well, big, big improvements in terms of performance, and expansion. And for those customers that need even more expansion when it comes to, let's say, storage or accelerators then the DL 380 Gen11 is a server that's new as well. And that's really for folks that need more expandability than the DL360, which is a one use server. And then lastly, our ML350, which is a tower server. These tower servers are typically used at remote sites, branch offices and this particular server holds a world record for energy efficiency for tower servers. So those are some of the servers we have today that we're announcing. I also want to talk a little bit about our Cray portfolio. So we're announcing two new servers with our HPE Cray portfolio. And what's great about this is that these servers make super computing more accessible to more enterprise customers. These servers are going to be smaller, they're going to come in at lower price points, and deliver tremendous energy efficiency. So these are the Cray XD servers, and there's more servers to come, but these are the ones that we're announcing with this first iteration. >> Great stuff. I can talk about servers all day long, I love server innovation. It's been following for many, many years, and you guys know. Lisa, we'll bring you in. Servers have been powered by Intel Xeon, we've been talking a lot about the scalable processors. This is your 4th Gen, they're in Gen11 and you're at 4th Gen. Krista mentioned this generation's about Security Edge, which is essentially becoming like a data center model now, the Edges are exploding. What are some of the design principles that went into the 4th Gen this time around the scalable processor? Can you share the Intel role here? >> Sure. I love what Krista said about headroom. If there's anything we've learned in these past few years, it's that you can plan for today, and you can even plan for tomorrow, but your tomorrow might look a lot different than what you thought it was going to. So to meet these business challenges, as we think about the underlying processor that powers all that amazing server lineup that Krista just went through, we are really looking at delivering that increased performance, the power efficient compute and then strong security. And of course, attention to the overall operating cost of the customer environment. Intel's focused on a very workload-first approach to solving our customers' real problems. So this is the applications that they're running every day to drive their digital transformation, and we really like to focus our innovation, and leadership for those highest value, and also the highest growth workloads. Some of those that we've uniquely focused on in 4th Gen Xeon, our artificial intelligence, high performance computing, network, storage, and as well as the deployments, like you were mentioning, ranging from the cloud all the way out to the Edge. And those are all satisfied by 4th Gen Xeon scalable. So our strategy for architecting is based off of all of that. And in addition to doing things like adding core count, improving the platform, updating the memory and the IO, all those standard things that you do, we've invested deeply in delivering the industry's CPU with the most built-in accelerators. And I'll just give an example, in artificial intelligence with built-in AMX acceleration, plus the framework optimizations, customers can see a 10X performance improvement gen over gen, that's on both training and inference. So it further cements Xeon as the world's foundation for inference, and it now delivers performance equivalent of a modern GPU, but all within your CPU. The flexibility that, that opens up for customers is tremendous and it's so many new ways to utilize their infrastructure. And like Krista said, I just want to say that, that best-in-class security, and security solutions are an absolute requirement. We believe that starts at the hardware level, and we continue to invest in our security features with that full ecosystem support so that our customers, like HPE, can deliver that full stacked solution to really deliver on that promise. >> I love that scalable processor messaging too around the silicon and all those advanced features, the accelerators. AI's certainly seeing a lot of that in demand now. Krista, similar question to you on your end. How do you guys look at these, your core design principles around the ProLiant Gen11, and how that helps solve the challenges for your customers that are living in this hybrid world today? >> Yeah, so we see how fast things are changing and we kept that in mind when we decided to design this generation. We talked all already about distributed environments. We see the intensity of the requirements that are at the Edge, and that's part of what we're trying to address with the new platform that I mentioned. It's also part of what we're trying to address with our management, making sure that people can manage no matter where a server is and get a great experience. The other thing we're realizing when it comes to what's happening is customers are looking at how they operate. Many want to buy as a service and with HPE GreenLake, we see that becoming more and more popular. With HPE GreenLake, we can offer that to customers, which is really helpful, especially when they're trying to get new technology like this. Sometimes they don't have it in the budget. With something like HP GreenLake, there's no upfront costs so they can enjoy this technology without having to come up with a big capital outlay for it. So that's great. Another one is around, I liked what Lisa said about security starting at the hardware. And that's exactly, the foundation has to be secure, or you're starting at the wrong place. So that's also something that we feel like we've advanced this time around. This secure root of trust that we started in Gen10, we've extended that to additional partners, so we're excited about that as well. >> That's great, Krista. We're seeing and hearing a lot about customers challenges at the Edge. Lisa, I want to bring you back in on this one. What are the needs that you see at the Edge from an Intel perspective? How is Intel addressing the Edge? >> Yeah, thanks, John. You know, one of the best things about Xeon is that it can span workloads and environments all the way from the Edge back to the core data center all within the same software environment. Customers really love that portability. For the Edge, we have seen an explosion of use cases coming from all industries and I think Krista would say the same. Where we're focused on delivering is that performant-enough compute that can fit into a constrained environment, and those constraints can be physical space, they can be the thermal environment. The Network Edge has been a big focus for us. Not only adding features and integrating acceleration, but investing deeply in that software environment so that more and more critical applications can be ported to Xeon and HPE industry standard servers versus requiring expensive, proprietary systems that were quite frankly not designed for this explosion of use cases that we're seeing. Across a variety of Edge to cloud use cases, we have identified ways to provide step function improvements in both performance and that power efficiency. For example, in this generation, we're delivering an up to 2.9X average improvement in performance per watt versus not using accelerators, and up to 70 watt power savings per CPU opportunity with some unique power management features, and improve total cost of ownership, and just overall power- >> What's the closing thoughts? What should people take away from this announcement around scalable processors, 4th Gen Intel, and then Gen11 ProLiant? What's the walkaway? What's the main super thought here? >> So I can go first. I think the main thought is that, obviously, we have partnered with Intel for many, many years. We continue to partner this generation with years in the making. In fact, we've been working on this for years, so we're both very excited that it's finally here. But we're laser focused on making sure that customers get the most out of their workloads, the most out of their infrastructure, and that they can meet those challenges that people are throwing at 'em. I think IT is under more pressure than ever before and the demands are there. They're critical to the business success with digital transformation and our job is to make sure they have everything they need, and they could do and meet the business needs as they come at 'em. >> Lisa, your thoughts on this reflection point we're in right now? >> Well, I agree with everything that Krista said. It's just a really exciting time right now. There's a ton of challenges in front of us, but the opportunity to bring technology solutions to our customers' digital transformation is tremendous right now. I think I would also like our customers to take away that between the work that Intel and HPE have done together for generations, they have a community that they can trust. We are committed to delivering customer-led solutions that do solve these business transformation challenges that we know are in front of everyone, and we're pretty excited for this launch. >> Yeah, I'm super enthusiastic right now. I think you guys are on the right track. This title Compute Engineered for Hybrid World really kind of highlights the word, "Engineered." You're starting to see this distributed computing architecture take shape with the Edge. Cloud on-premise computing is everywhere. This is real relevant to your customers, and it's a great announcement. Thanks for taking the time and joining us today. >> Thank you. >> Yeah, thank you. >> This is the first episode of theCUBE's coverage of Compute Engineered For Your Hybrid World. Please continue to check out thecube.net, our site, for the future episodes where we'll discuss how to build high performance AI applications, transforming compute management experiences, and accelerating VDI at the Edge. Also, to learn more about the new HPE ProLiant servers with the 4th Gen Intel Xeon processors, you can go to hpe.com. And check out the URL below, click on it. I'm John Furrier at theCUBE. You're watching theCUBE, the leader in high tech, enterprise coverage. (bright music)

>>from the Cube Studios >>in Palo Alto and >>Boston connecting with thought leaders all around the world. This is a cube conversation. >>Hey, welcome back here. Ready? Jeff Frick here with the Cube. We're doing a special presentation today really talking about AI and making ai really with two companies that are right in the heart of the Dell EMC as well as Intel. So we're excited to have a couple Cube alumni back on the program. Haven't seen him in a little while. First off from Intel. Lisa Spelman. She is the corporate VP and GM for the Xeon Group in Jersey on and Memory Group. Great to see you, Lisa. >>Good to see you again, too. >>And we've got Ravi Pinter. Conte. He is the SBP server product management, also from Dell Technologies. Ravi, great to see you as well. >>Good to see you on beast. Of course, >>yes. So let's jump into it. So, yesterday, Robbie, you guys announced a bunch of new kind of ai based solutions where if you can take us through that >>Absolutely so one of the things we did Jeff was we said it's not good enough for us to have a point product. But we talked about hope, the tour of products, more importantly, everything from our workstation side to the server to these storage elements and things that we're doing with VM Ware, for example. Beyond that, we're also obviously pleased with everything we're doing on bringing the right set off validated configurations and reference architectures and ready solutions so that the customer really doesn't have to go ahead and do the due diligence. Are figuring out how the various integration points are coming for us in making a solution possible. Obviously, all this is based on the great partnership we have with Intel on using not just their, you know, super cues, but FPG's as well. >>That's great. So, Lisa, I wonder, you know, I think a lot of people you know, obviously everybody knows Intel for your CPU is, but I don't think they recognize kind of all the other stuff that can wrap around the core CPU to add value around a particular solution. Set or problems. That's what If you could tell us a little bit more about Z on family and what you guys are doing in the data center with this kind of new interesting thing called AI and machine learning. >>Yeah. Um, so thanks, Jeff and Ravi. It's, um, amazing. The way to see that artificial intelligence applications are just growing in their pervasiveness. And you see it taking it out across all sorts of industries. And it's actually being built into just about every application that is coming down the pipe. And so if you think about meeting toe, have your hardware foundation able to support that. That's where we're seeing a lot of the customer interest come in. And not just a first Xeon, but, like Robbie said on the whole portfolio and how the system and solution configuration come together. So we're approaching it from a total view of being able to move all that data, store all of that data and cross us all of that data and providing options along that entire pipeline that move, um, and within that on Z on. Specifically, we've really set that as our cornerstone foundation for AI. If it's the most deployed solution and data center CPU around the world and every single application is going to have artificial intelligence in it, it makes sense that you would have artificial intelligence acceleration built into the actual hardware so that customers get a better experience right out of the box, regardless of which industry they're in or which specialized function they might be focusing on. >>It's really it's really wild, right? Cause in process, right, you always move through your next point of failure. So, you know, having all these kind of accelerants and the ways that you can carve off parts of the workload part of the intelligence that you can optimize betters is so important as you said Lisa and also Rocket and the solution side. Nobody wants General Ai just for ai sake. It's a nice word. Interesting science experiment. But it's really in the applied. A world is. We're starting to see the value in the application of this stuff, and I wonder you have a customer. You want to highlight Absalon, tell us a little bit about their journey and what you guys did with them. >>Great, sure. I mean, if you didn't start looking at Epsilon there in the market in the marketing business, and one of the crucial things for them is to ensure that they're able to provide the right data. Based on that analysis, there run on? What is it that the customer is looking for? And they can't wait for a period of time, but they need to be doing that in the near real time basis, and that's what excellent does. And what really blew my mind was the fact that they actually service are send out close to 100 billion messages. Again, it's 100 billion messages a year. And so you can imagine the amount of data that they're analyzing, which is in petabytes of data, and they need to do real time. And that's all possible because of the kind of analytics we have driven into the power It silver's, you know, using the latest of the Intel Intel Xeon processor couple with some of the technologies from the BGS side, which again I love them to go back in and analyze this data and service to the customers very rapidly. >>You know, it's funny. I think Mark Tech is kind of an under appreciated ah world of ai and, you know, in machine to machine execution, right, That's the amount of transactions go through when you load a webpage on your site that actually ideas who you are you know, puts puts a marketplace together, sells time on that or a spot on that ad and then lets people in is a really sophisticated, as you said in massive amounts of data going through the interesting stuff. If it's done right, it's magic. And if it's done, not right, then people get pissed off. You gotta have. You gotta have use our tools. >>You got it. I mean, this is where I talked about, you know, it can be garbage in garbage out if you don't really act on the right data. Right. So that is where I think it becomes important. But also, if you don't do it in a timely fashion, but you don't service up the right content at the right time. You miss the opportunity to go ahead and grab attention, >>right? Right. Lisa kind of back to you. Um, you know, there's all kinds of open source stuff that's happening also in the in the AI and machine learning world. So we hear things about tense or flow and and all these different libraries. How are you guys, you know, kind of embracing that world as you look at ai and kind of the development. We've been at it for a while. You guys are involved in everything from autonomous vehicles to the Mar Tech. Is we discussed? How are you making sure that these things were using all the available resources to optimize the solutions? >>Yeah, I think you and Robbie we're just hitting on some of those examples of how many ways people have figured out how to apply AI now. So maybe at first it was really driven by just image recognition and image tagging. But now you see so much work being driven in recommendation engines and an object detection for much more industrial use cases, not just consumer enjoyment and also those things you mentioned and hit on where the personalization is a really fine line you walk between. How do you make an experience feel good? Personalized versus creepy personalized is a real challenge and opportunity across so many industries. And so open source like you mentioned, is a great place for that foundation because it gives people the tools to build upon. And I think our strategy is really a stack strategy that starts first with delivering the best hardware for artificial intelligence and again the other is the foundation for that. But we also have, you know, Milat type processing for out of the Edge. And then we have all the way through to very custom specific accelerators into the data center, then on top about the optimized software, which is going into each of those frameworks and doing the work so that the framework recognizes the specific acceleration we built into the CPU. Whether that steel boost or recognizes the capabilities that sit in that accelerator silicon, and then once we've done that software layer and this is where we have the opportunity for a lot of partnership is the ecosystem and the solutions work that Robbie started off by talking about. So Ai isn't, um, it's not easy for everyone. It has a lot of value, but it takes work to extract that value. And so partnerships within the ecosystem to make sure that I see these are taking those optimization is building them in and fundamentally can deliver to customers. Reliable solution is the last leg of that of that strategy, but it really is one of the most important because without it you get a lot of really good benchmark results but not a lot of good, happy customer, >>right? I'm just curious, Lee says, because you kind of sit in the catbird seat. You guys at the core, you know, kind of under all the layers running data centers run these workloads. How >>do you see >>kind of the evolution of machine learning and ai from kind of the early days, where with science projects and and really smart people on mahogany row versus now people are talking about trying to get it to, like a citizen developer, but really a citizen data science and, you know, in exposing in the power of AI to business leaders or business executioners. Analysts, if you will, so they can apply it to their day to day world in their day to day life. How do you see that kind of evolving? Because you not only in it early, but you get to see some of the stuff coming down the road in design, find wins and reference architectures. How should people think about this evolution? >>It really is one of those things where if you step back from the fundamentals of AI, they've actually been around for 50 or more years. It's just that the changes in the amount of computing capability that's available, the network capacity that's available and the fundamental efficiency that I t and infrastructure managers and get out of their cloud architectures as allowed for this pervasiveness to evolve. And I think that's been the big tipping point that pushed people over this fear. Of course, I went through the same thing that cloud did where you had maybe every business leader or CEO saying Hey, get me a cloud and I'll figure out what for later give me some AI will get a week and make it work, But we're through those initial use pieces and starting to see a business value derived from from those deployments. And I think some of the most exciting areas are in the medical services field and just the amount, especially if you think of the environment we're in right now. The amount of efficiency and in some cases, reduction in human contact that you could require for diagnostics and just customer tracking and ability, ability to follow their entire patient History is really powerful and represents the next wave and care and how we scale our limited resource of doctors nurses technician. And the point we're making of what's coming next is where you start to see even more mass personalization and recommendations in that way that feel very not spooky to people but actually comforting. And they take value from them because it allows them to immediately act. Robbie reference to the speed at which you have to utilize the data. When people get immediately act more efficiently. They're generally happier with the service. So we see so much opportunity and we're continuing to address across, you know, again that hardware, software and solution stack so we can stay a step ahead of our customers, >>Right? That's great, Ravi. I want to give you the final word because you guys have to put the solutions together, it actually delivering to the customer. So not only, you know the hardware and the software, but any other kind of ecosystem components that you have to bring together. So I wonder if you can talk about that approach and how you know it's it's really the solution. At the end of the day, not specs, not speeds and feeds. That's not really what people care about. It's really a good solution. >>Yeah, three like Jeff, because end of the day I mean, it's like this. Most of us probably use the A team to retry money, but we really don't know what really sits behind 80 and my point being that you really care at that particular point in time to be able to put a radio do machine and get your dollar bills out, for example. Likewise, when you start looking at what the customer really needs to know, what Lisa hit upon is actually right. I mean what they're looking for. And you said this on the whole solution side house. To our our mantra to this is very simple. We want to make sure that we use the right basic building blocks, ensuring that we bring the right solutions using three things the right products which essentially means that we need to use the right partners to get the right processes in GPU Xen. But then >>we get >>to the next level by ensuring that we can actually do things we can either provide no ready solutions are validated reference architectures being that you have the sausage making process that you now don't need to have the customer go through, right? In a way. We have done the cooking and we provide a recipe book and you just go through the ingredient process of peering does and then off your off right to go get your solution done. And finally, the final stages there might be helped that customers still need in terms of services. That's something else Dell technology provides. And the whole idea is that customers want to go out and have them help deploying the solutions. We can also do that we're services. So that's probably the way we approach our data. The way we approach, you know, providing the building blocks are using the right technologies from our partners, then making sure that we have the right solutions that our customers can look at. And finally, they need deployment. Help weaken due their services. >>Well, Robbie, Lisa, thanks for taking a few minutes. That was a great tee up, Rob, because I think we're gonna go to a customer a couple of customer interviews enjoying that nice meal that you prepared with that combination of hardware, software, services and support. So thank you for your time and a great to catch up. All right, let's go and run the tape. Hi, Jeff. I wanted to talk about two examples of collaboration that we have with the partners that have yielded Ah, really examples of ah put through HPC and AI activities. So the first example that I wanted to cover is within your AHMAD team up in Canada with that team. We collaborated with Intel on a tuning of algorithm and code in order to accelerate the mapping of the human brain. So we have a cluster down here in Texas called Zenith based on Z on and obtain memory on. And we were able to that customer with the three of us are friends and Intel the norm, our team on the Dell HPC on data innovation, injuring team to go and accelerate the mapping of the human brain. So imagine patients playing video games or doing all sorts of activities that help understand how the brain sends the signal in order to trigger a response of the nervous system. And it's not only good, good way to map the human brain, but think about what you can get with that type of information in order to help cure Alzheimer's or dementia down the road. So this is really something I'm passionate about. Is using technology to help all of us on all of those that are suffering from those really tough diseases? Yeah, yeah, way >>boil. I'm a project manager for the project, and the idea is actually to scan six participants really intensively in both the memory scanner and the G scanner and see if we can use human brain data to get closer to something called Generalized Intelligence. What we have in the AI world, the systems that are mathematically computational, built often they do one task really, really well, but they struggle with other tasks. Really good example. This is video games. Artificial neural nets can often outperform humans and video games, but they don't really play in a natural way. Artificial neural net. Playing Mario Brothers The way that it beats the system is by actually kind of gliding its way through as quickly as possible. And it doesn't like collect pennies. For example, if you play Mary Brothers as a child, you know that collecting those coins is part of your game. And so the idea is to get artificial neural nets to behave more like humans. So like we have Transfer of knowledge is just something that humans do really, really well and very naturally. It doesn't take 50,000 examples for a child to know the difference between a dog and a hot dog when you eat when you play with. But an artificial neural net can often take massive computational power and many examples before it understands >>that video games are awesome, because when you do video game, you're doing a vision task instant. You're also doing a >>lot of planning and strategy thinking, but >>you're also taking decisions you several times a second, and we record that we try to see. Can we from brain activity predict >>what people were doing? We can break almost 90% accuracy with this type of architecture. >>Yeah, yeah, >>Use I was the lead posts. Talk on this collaboration with Dell and Intel. She's trying to work on a model called Graph Convolution Neural nets. >>We have being involved like two computing systems to compare it, like how the performance >>was voting for The lab relies on both servers that we have internally here, so I have a GPU server, but what we really rely on is compute Canada and Compute Canada is just not powerful enough to be able to run the models that he was trying to run so it would take her days. Weeks it would crash, would have to wait in line. Dell was visiting, and I was invited into the meeting very kindly, and they >>told us that they started working with a new >>type of hardware to train our neural nets. >>Dell's using traditional CPU use, pairing it with a new >>type off memory developed by Intel. Which thing? They also >>their new CPU architectures and really optimized to do deep learning. So all of that sounds great because we had this problem. We run out of memory, >>the innovation lab having access to experts to help answer questions immediately. That's not something to gate. >>We were able to train the attic snatch within 20 minutes. But before we do the same thing, all the GPU we need to wait almost three hours to each one simple way we >>were able to train the short original neural net. Dell has been really great cause anytime we need more memory, we send an email, Dell says. Yeah, sure, no problem. We'll extended how much memory do you need? It's been really simple from our end, and I think it's really great to be at the edge of science and technology. We're not just doing the same old. We're pushing the boundaries. Like often. We don't know where we're going to be in six months. In the big data world computing power makes a big difference. >>Yeah, yeah, yeah, yeah. The second example I'd like to cover is the one that will call the data accelerator. That's a publisher that we have with the University of Cambridge, England. There we partnered with Intel on Cambridge, and we built up at the time the number one Io 500 storage solution on. And it's pretty amazing because it was built on standard building blocks, power edge servers until Xeon processors some envy me drives from our partners and Intel. And what we did is we. Both of this system with a very, very smart and elaborate suffering code that gives an ultra fast performance for our customers, are looking for a front and fast scratch to their HPC storage solutions. We're also very mindful that this innovation is great for others to leverage, so the suffering Could will soon be available on Get Hub on. And, as I said, this was number one on the Iot 500 was initially released >>within Cambridge with always out of focus on opening up our technologies to UK industry, where we can encourage UK companies to take advantage of advanced research computing technologies way have many customers in the fields of automotive gas life sciences find our systems really help them accelerate their product development process. Manage Poor Khalidiya. I'm the director of research computing at Cambridge University. Yeah, we are a research computing cloud provider, but the emphasis is on the consulting on the processes around how to exploit that technology rather than the better results. Our value is in how we help businesses use advanced computing resources rather than the provision. Those results we see increasingly more and more data being produced across a wide range of verticals, life sciences, astronomy, manufacturing. So the data accelerators that was created as a component within our data center compute environment. Data processing is becoming more and more central element within research computing. We're getting very large data sets, traditional spinning disk file systems can't keep up and we find applications being slowed down due to a lack of data, So the data accelerator was born to take advantage of new solid state storage devices. I tried to work out how we can have a a staging mechanism for keeping your data on spinning disk when it's not required pre staging it on fast envy any stories? Devices so that can feed the applications at the rate quiet for maximum performance. So we have the highest AI capability available anywhere in the UK, where we match II compute performance Very high stories performance Because for AI, high performance storage is a key element to get the performance up. Currently, the data accelerated is the fastest HPC storage system in the world way are able to obtain 500 gigabytes a second read write with AI ops up in the 20 million range. We provide advanced computing technologies allow some of the brightest minds in the world really pushed scientific and medical research. We enable some of the greatest academics in the world to make tomorrow's discoveries. Yeah, yeah, yeah. >>Alright, Welcome back, Jeff Frick here and we're excited for this next segment. We're joined by Jeremy Raider. He is the GM digital transformation and scale solutions for Intel Corporation. Jeremy, great to see you. Hey, thanks for having me. I love I love the flowers in the backyard. I thought maybe you ran over to the Japanese, the Japanese garden or the Rose Garden, Right To very beautiful places to visit in Portland. >>Yeah. You know, you only get him for a couple. Ah, couple weeks here, so we get the timing just right. >>Excellent. All right, so let's jump into it. Really? And in this conversation really is all about making Ai Riel. Um, and you guys are working with Dell and you're working with not only Dell, right? There's the hardware and software, but a lot of these smaller a solution provider. So what is some of the key attributes that that needs to make ai riel for your customers out there? >>Yeah, so, you know, it's a it's a complex space. So when you can bring the best of the intel portfolio, which is which is expanding a lot, you know, it's not just the few anymore you're getting into Memory technologies, network technologies and kind of a little less known as how many resources we have focused on the software side of things optimizing frameworks and optimizing, and in these key ingredients and libraries that you can stitch into that portfolio to really get more performance in value, out of your machine learning and deep learning space. And so you know what we've really done here with Dell? It has started to bring a bunch of that portfolio together with Dell's capabilities, and then bring in that ai's V partner, that software vendor where we can really take and stitch and bring the most value out of that broad portfolio, ultimately using using the complexity of what it takes to deploy an AI capability. So a lot going on. They're bringing kind of the three legged stool of the software vendor hardware vendor dental into the mix, and you get a really strong outcome, >>right? So before we get to the solutions piece, let's stick a little bit into the Intel world. And I don't know if a lot of people are aware that obviously you guys make CPUs and you've been making great CPIs forever. But there's a whole lot more stuff that you've added, you know, kind of around the core CPU. If you will in terms of of actual libraries and ways to really optimize the seond processors to operate in an AI world. I wonder if you can kind of take us a little bit below the surface on how that works. What are some of the examples of things you can do to get more from your Gambira Intel processors for ai specific applications of workloads? >>Yeah, well, you know, there's a ton of software optimization that goes into this. You know that having the great CPU is definitely step one. But ultimately you want to get down into the libraries like tensor flow. We have data analytics, acceleration libraries. You know, that really allows you to get kind of again under the covers a little bit and look at it. How do we have to get the most out of the kinds of capabilities that are ultimately used in machine learning in deep learning capabilities, and then bring that forward and trying and enable that with our software vendors so that they can take advantage of those acceleration components and ultimately, you know, move from, you know, less training time or could be a the cost factor. But those are the kind of capabilities we want to expose to software vendors do these kinds of partnerships. >>Okay. Ah, and that's terrific. And I do think that's a big part of the story that a lot of people are probably not as aware of that. There are a lot of these optimization opportunities that you guys have been leveraging for a while. So shifting gears a little bit, right? AI and machine learning is all about the data. And in doing a little research for this, I found actually you on stage talking about some company that had, like, 350 of road off, 315 petabytes of data, 140,000 sources of those data. And I think probably not great quote of six months access time to get that's right and actually work with it. And the company you're referencing was intel. So you guys know a lot about debt data, managing data, everything from your manufacturing, and obviously supporting a global organization for I t and run and ah, a lot of complexity and secrets and good stuff. So you know what have you guys leveraged as intel in the way you work with data and getting a good data pipeline. That's enabling you to kind of put that into these other solutions that you're providing to the customers, >>right? Well, it is, You know, it's absolutely a journey, and it doesn't happen overnight, and that's what we've you know. We've seen it at Intel on We see it with many of our customers that are on the same journey that we've been on. And so you know, this idea of building that pipeline it really starts with what kind of problems that you're trying to solve. What are the big issues that are holding you back that company where you see that competitive advantage that you're trying to get to? And then ultimately, how do you build the structure to enable the right kind of pipeline of that data? Because that's that's what machine learning and deep learning is that data journey. So really a lot of focus around you know how we can understand those business challenges bring forward those kinds of capabilities along the way through to where we structure our entire company around those assets and then ultimately some of the partnerships that we're gonna be talking about these companies that are out there to help us really squeeze the most out of that data as quickly as possible because otherwise it goes stale real fast, sits on the shelf and you're not getting that value out of right. So, yeah, we've been on the journey. It's Ah, it's a long journey, but ultimately we could take a lot of those those kind of learnings and we can apply them to our silicon technology. The software optimization is that we're doing and ultimately, how we talk to our enterprise customers about how they can solve overcome some of the same challenges that we did. >>Well, let's talk about some of those challenges specifically because, you know, I think part of the the challenge is that kind of knocked big data, if you will in Hadoop, if you will kind of off the rails. Little bit was there's a whole lot that goes into it. Besides just doing the analysis, there's a lot of data practice data collection, data organization, a whole bunch of things that have to happen before. You can actually start to do the sexy stuff of AI. So you know, what are some of those challenges. How are you helping people get over kind of these baby steps before they can really get into the deep end of the pool? >>Yeah, well, you know, one is you have to have the resource is so you know, do you even have the resource is if you can acquire those Resource is can you keep them interested in the kind of work that you're doing? So that's a big challenge on and actually will talk about how that fits into some of the partnerships that we've been establishing in the ecosystem. It's also you get stuck in this poc do loop, right? You finally get those resource is and they start to get access to that data that we talked about. It start to play out some scenarios, a theorize a little bit. Maybe they show you some really interesting value, but it never seems to make its way into a full production mode. And I think that is a challenge that has faced so many enterprises that are stuck in that loop. And so that's where we look at who's out there in the ecosystem that can help more readily move through that whole process of the evaluation that proved the r a y, the POC and ultimately move that thing that capability into production mode as quickly as possible that you know that to me is one of those fundamental aspects of if you're stuck in the POC. Nothing's happening from this. This is not helping your company. We want to move things more quickly, >>right? Right. And let's just talk about some of these companies that you guys are working with that you've got some reference architectures is data robot a Grid dynamics H 20 just down the road in Antigua. So a lot of the companies we've worked with with Cube and I think you know another part that's interesting. It again we can learn from kind of old days of big data is kind of generalized. Ai versus solution specific. Ai and I think you know where there's a real opportunity is not AI for a sake, but really it's got to be applied to a specific solution, a specific problem so that you have, you know, better chatbots, better customer service experience, you know, better something. So when you were working with these folks and trying to design solutions or some of the opportunities that you saw to work with some of these folks to now have an applied a application slash solution versus just kind of AI for ai's sake. >>Yeah. I mean, that could be anything from fraud, detection and financial services, or even taking a step back and looking more horizontally like back to that data challenge. If if you're stuck at the AI built a fantastic Data lake, but I haven't been able to pull anything back out of it, who are some of the companies that are out there that can help overcome some of those big data challenges and ultimately get you to where you know, you don't have a data scientist spending 60% of their time on data acquisition pre processing? That's not where we want them, right? We want them on building out that next theory. We want them on looking at the next business challenge. We want them on selecting the right models, but ultimately they have to do that as quickly as possible so that they can move that that capability forward into the next phase. So, really, it's about that that connection of looking at those those problems or challenges in the whole pipeline. And these companies like data robot in H 20 quasi. Oh, they're all addressing specific challenges in the end to end. That's why they've kind of bubbled up as ones that we want to continue to collaborate with, because it can help enterprises overcome those issues more fast. You know more readily. >>Great. Well, Jeremy, thanks for taking a few minutes and giving us the Intel side of the story. Um, it's a great company has been around forever. I worked there many, many moons ago. That's Ah, that's a story for another time, but really appreciate it and I'll interview you will go there. Alright, so super. Thanks a lot. So he's Jeremy. I'm Jeff Frick. So now it's time to go ahead and jump into the crowd chat. It's crowdchat dot net slash make ai real. Um, we'll see you in the chat. And thanks for watching