Intel's AI Portfolio with Greg Lavender & Sandra Rivera - Six Five On the Road

Absolutely. Edge to cloud inside of AI, we do it all here because, well, like you said, silicon rules the world. So it's a great event. I'm happy to be here and happy to have had some really great conversations and heard some thoughtful, introspective, and visionary commentary from many of the executives, the partners, and of course the developers.

Good to be here, Greg.

Thank you, glad to be here.

Well, I want to just add to the idea that AI is layering on capability, that it is accretive in terms of the overall technology landscape, and that frankly, we're in the early days of AI. Sometimes I feel like everything that gets written is, 'Oh, it's done and dusted,' exactly, 'it's over.' But truly, we are very much in the early days. It is such a transformative technology. It will impact every single industry, every single part of our lives, how we work, how we live. And we are big believers that when you open up the market opportunity, lower the barriers to entry, you increase market participation and then you accelerate the rate of innovation. And clearly, this is an area where there's lots of innovation from the cloud to the edge and to the client, which is a lot of what you've heard here at Innovation. It's not just what's happening in the 10,000-node GPU clusters that are dedicated for many months and many megawatts of power and many tens of millions of dollars, but it's the day-to-day AI that happens in the enterprise, where enterprises of all sizes and shapes are looking for more productivity, more efficiency, but also in your client device and the ability to have more personalized experiences. So we have a unique position from cloud to edge to client, which is the ubiquity of our compute through our CPUs, and now adding in and layering our GPUs and our AI accelerators. It's an exciting time for us to meet the moment and to address our customer requirements.

It always wins in the long term. Just look. There are obviously proprietary islands of technology that get a favorable competitive advantage for a period of time, but ultimately the market wants choice, diversity, stability, and quality as well. That's one of the reasons we launched Intel Developer Cloud. We sort of dreamed that up, which I think is very innovative for Intel, and we had Pat support for it. It's essentially to give developers access to our latest and greatest technology, whether it's our GPU technology or Gaudi 2s. All that technology is widely deployed. We're scaling it as fast as we can build it out and wire it up in the data centers and colo facilities where we're running Intel Developer Cloud, because that becomes the playground where you can come in and play for free for a little while. You start getting value out of it, and you're going to hear about some of that value in the presentations we have. But basically, we can do this at scale, and this is new mental muscle for the company. We're building large-scale systems architectures: compute, networking, storage, software, operational stability, operational scale. This is not something Intel has historically done. We've talked about rack-scale computing that other people do. We're doing that ourselves, and we're doing it in partnership with our closest customers so that they can take advantage of it as well. So I think this is really the game changer for us.

Well, I would kind of dial the clock back to the strategic acquisition that we made with Habana Labs, which we're coming up on four years now, and just the execution machine that we have with that organization. The fact that we delivered Gaudi 1 on time, on budget, and Gaudi 2 similarly. We actually delivered that product last year, but it's had a resurgence in terms of the amount of interest given the MLPerf results from last November to this May to the ones we published last week. Clearly beating the market leader, the A100, which is the most pervasively deployed GPU today, handily in terms of raw performance, throughput, time to train, as well as cost per token, and beating on power efficiency. When we look at H100, particularly with the FP8 data format that we released the software for—A100 doesn't have it, H100 does have it—from a price-performance perspective, we still beat H100 because the price differential for the premium product in the market is quite significant. And then look at what we are putting into play in the Intel DevCloud, just creating that sandbox for customers to come in, try their models out, see the ease of portability, the power efficiency, the time to train, the cost effectiveness of a Gaudi solution. We're pretty excited about that. I will say that in addition to that, if you notice, the only company that submitted CPU MLPerf results, leadership results, was Intel. Very much noticed, yes, with our Sapphire Rapids, fourth-gen Xeon, which of course has integrated AI acceleration capability. So I want to go back to your comment: silicon rules the world. We're getting silicon to market faster, and just the amount of progress we've made over the past year is pretty phenomenal. And yes, we're getting a lot of incoming interest on, 'Hey, what is this thing Gaudi 2?' 'Hey, maybe fourth-gen Xeon is something that I could deploy for many of the classic machine learning workloads, certainly all the inference type of opportunity, which is the highest growth opportunity that we see in the market.' And the more traditional enterprise OEM go-to-market sales motions that we have, given that's still a very strong share position for Intel with Xeon.

Well, to your point, there is the data gravity that exists in the enterprise, and the data wants to be processed at that point of data creation and data consumption. That is typically on-prem or clearly a hybrid model. So what we're seeing is this very strong affinity to taking a foundational model, clearly training on that model, using some of the goodness of that model, but then contextualizing the data set that you have on-prem, which is a much smaller data set, grounding it, and then doing that fine-tuning. And clearly, the deployment that we're seeing is on smaller nodes, like eight nodes, but that's all within the purview of what we have with our CPU, clearly with our GPU, and certainly with the AI accelerators, and then of course being fortified by our own IPU capability, our FPGA capability. So I think what we're going to see is this very big growth in the enterprise use cases that will require trust and security, a lot of the things that Greg talked about in his keynote as well: securing the models, securing the data, whether that's running in the secure perimeter in the cloud for some of that foundational training or actually being deployed on-prem. We just see a huge opportunity. And probably the early days of AI, the workloads are broad and ranging in terms of their complexity, their size, the multi-modality, the latency requirements, real-time nature, batch nature, training versus inference. It is not a one-size-fits-all. We're going to have heterogeneous architectures, and our job is to address all that complexity through this homogenizing layer of software to protect the developers' investment in their application software by hiding all that complexity. Back to Greg's point, we are close to the metal. We do understand how all those bits work and all those transistors work and how to get the most out of it, but we try to present a very elegant and simple software interface to the developer.

Thank you so much. Always great talking to you guys. Let's do it again.