Pod Insights No Priors Competition makes for better chip design with AMD CTO Mark Papermaster About this episode Compute is the fuel for the AI revolution, and customers want more chip vendors. AMD CTO Mark Papermaster joins Sarah and Elad on No Priors to discuss AMD’s strategy, their newest GPUs, where inference workloads will live, the chip software stack, how they are thinking about supply chain issues, and what we can expect from AMD in 2024.
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Show Notes:
(0:00) Introduction and Mark’s background
(2:35) AMD background and current markets
(4:40) AMD shifting to AI space
(8:54) AI applications coming out of AMD
(10:57) Software investment
(15:15) The benefits of open-source stacks
(16:58) Evolving GPU market
(20:21) Constraints on GPU production
(24:11) Innovations in chip technology
(27:57) Chip supply chain
(30:18) Future of innovative hardware products
(35:42) What’s next for AMD
Transcript Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:05 Sarah Guo? Hi listeners. For potential AI founders, my early-stage AI fund, Conviction, is accepting applications for its Embed accelerator for two more days. Embed offers $150,000 in an uncapped safe, more than half a million of free compute and API credits, a hand-selected set of peers, and access to leading founder and research mentors. Apply at embed. conviction.com by March 1st. Hi listeners and welcome to another episode of No Priors. Today, we're excited to be talking to the CTO of AMD, Mark Papermaster. Mark has had a storied career in chips and hardware with previous leadership positions at IBM, Apple, and Cisco. We're excited to have Mark on to get into GPUs and the competition that's been driving this industry. Welcome, Mark.
0:50 Speaker 01 Thanks, Sarah. Glad to be here with you and Elad.
0:52 Sarah Guo? Can you start by telling us a bit about your background? You've worked on all sorts of interesting things from the iPhone and the iPad to the latest generation of AMD supercomputing chips.
1:01 Speaker 01 Oh, sure. I've been around a while. So what's really fun is my timing was pretty good getting into the industry. I was an electrical and computer engineering grad, University of Texas, and got really interested in chip design. And so it was back at a time when chip design was radically changing. The kind of technology everyone uses today, CMOS, was just coming into production usage. And so I got on IBM's very first CMOS projects and created some of the first designs. So I got to get my hands dirty and do just about every facet of chip design and had a number of years at IBM and took on different roles, took on driving the
1:44 Speaker 01 microprocessor development at IBM across, first, their power PCs. And that meant working with Apple and Motorola, as well as the big iron, the big computing chips that we had in the mainframe and the big RISC servers. So really got all facets of technology there and included working on some of their server development, but then shifted over to Apple. Steve Jobs hired me in to run the iPhone and iPod. And so I was there for a couple of years. But it was A time of a great transition in the industry.
2:26 Speaker 01 And for me, it was a great opportunity because I ended up in 2011, fall of 2011, taking the role here at AMD of being both CTO and really running the technology and engineering. And right at a point where Moore's law is starting to slow down. And so, you know, tremendous innovation was needed.
2:45 Sarah Guo? Yeah, I want to get into that and sort of what we can expect in terms of computing innovation if we're not just ramming more transistors on chips or unable to do that. Every one of our listeners, I think, has heard of AMD, but can you give like a very brief overview of the major markets you serve there?
3:04 Speaker 01 Sure. So AMD is a storied company. It's been around well over 50 years. And it started out really being, you know, a second source company, really bringing, you know, second source on key components and x86 microprocessors. But you fast forward to where we are today, and it's a very, very broad portfolio. When Lisa and Sue, our CEO, and I were brought into the company just over 10 years ago, it was with a mandate to get AMD back into very, very strong competitiveness. And so we started with the CPU line, brought the CPU very, very competitive, and then
3:49 Speaker 01 really across the portfolio. And just in February 2022, acquired Xilinx, so that expanded the portfolio further. So AMD creates the world's largest supercomputers. It's got a massive install base now in the cloud, so many of your cloud operations that you're running are running on AMD Epic x86 CPUs. Gaming, we're huge, we're underneath all the Xbox, all the PlayStation, as well as many gaming devices that you buy when you buy your add-in boards. And then across embedded devices with all of that rich Xilinx portfolio, as well as embedded x86.
4:30 Speaker 01 And we acquired Pentsando, so it extends that portfolio right into a networking interconnect that we need as we scale out these workloads. So very, very broad portfolio.
4:42 Speaker 02 Yeah, AMD has had a pretty amazing run over the last decade plus since you joined. One of the things that you folks have really emphasized over the last couple of years as well as AI. And there's been a big shift, both in terms of the adoption of AI over the last decade or so, in terms of the traditional CNN, RNN and other types of neural network architectures, but also in terms of this shift to transformers and diffusion models and everything else. Can you tell us a little bit more about what initially caught your attention in the AI landscape? And then how AMD started to focus more and more on that over time? And what sort of solutions you've come up with?
5:15 Speaker 01 You bet. Well, we all know the AI journey has been going since really the race began when the application space for AI opened up and GPUs were obviously pivotal there. When you look at the key work that, you know, Hinton had done in terms of showing how GPUs could drastically improve the accuracy of image recognition, natural language processing. And so that's been known for some time. And so what we did at AMD is we, right away, saw the opportunity.
5:57 Speaker 01 The question was plotting our course to be that strong player in AI. So it was a very thoughtful and deliberate strategy because AMD, we had to turn around the company. So if you look at where AMD was in 2012, you know, through, you know, really 2017, it was largely, all of the revenue was based on PCs and then gaming. And so it was about making sure that the portfolio, the building blocks, were competitive. Those building blocks had to be leadership. They had to attract people to get on that AMD platform for high-performance applications. And so first, we actually had to rebuild the CPU roadmap.
6:41 Speaker 01 And that was the Zen microprocessors that we released in 2017 in both PCs with our Ryzen line as well as EPYC, our x86. Server line. So that started the revenue ramp for the company and and started extending our portfolio. And so right about that time, in parallel, as we saw where heterogeneous computing was going, we had we had called the ball on heterogeneous computing before myself, before Lisa ever joined the company, AMD had made a great acquisition of ATI that brought GPU into the portfolio. It's one of the big reasons I was attracted to AMD
7:24 Speaker 01 in the role is that, wow, it was one of the, it was the, really the only company that had a very strong CPU portfolio and a very strong GPU portfolio. And to me, it was clear that the industry needed that powerful combination of the serial, the scalar competing of these traditional CPU workloads, and the massive parallelization that you get from a GPU. And so we started with that heterogeneous compute. It's created an architecture around that. So we've been shipping CPUs and GPUs combined for PC applications longer than anyone, started shipping those in 2011 with what we call APUs, accelerated processor units. And then
8:08 Speaker 01 for big data applications, we started with HPC, the kind of high-performance compute technology that's in national labs, it's in oil exploration companies. And so we focused first with, you know, big government bids that ended up leading to supercomputer wins. We now have AMD CPU and AMD GPUs under the world's largest supercomputers. But that work started years ago, and it was equally a hardware and a software effort. And so we've been building that hardware and software capability, and it really culminated in December 6 of 2023 of last year, when we announced our flagship, the MI300, which just is a
8:52 Speaker 01 beast for both high-performance compute with one variant we have, and takes high-performance AI for both training and inference head-on with with a variant which is optimized for those AI applications. So it's been a long journey, and we're really pleased to be where we are, where our our sales are taking off.
9:13 Speaker 02 No, it's fantastic. I mean, I guess when you launched the MI 300, you had public commitments from Meta and Microsoft, for example, to purchase that. And you just mentioned that there's a series of applications that you're pretty excited about there. Could you tell us more about which A applications and workloads you're most excited about or most bullish on today?
9:29 Speaker 01 Sure. So if you think about where the bulk of AI is today, you're still seeing just tremendous capital expenditures and building up the accuracy of capabilities for large language model training and inference. So it is the likes of ChatGPT, of Bard and the other LLMs that you can ask it anything because it's trying to ingest the vast of data that is out there and it can be trained upon. And it's with really, you know, an ultimate goal of artificial general intelligence, an AGI type of capability. And so that is
10:13 Speaker 01 where we focused the MI 300, is to start with that halo product that could take on the industry leader. And in fact, MI 300 has done that. It's competitive on training and it leads in inferencing. And it has over 2X. If you look at, you know, FP16 VLMs, which is a metric that generally everyone can run that. It's got a tremendous performance advantage. And we did that very purposely. We created very efficient engines for the math processing that you need for that training or inference processing. But we also brought the memory that you need to have more efficient computing. So that's more computing at less
10:56 Speaker 01 power, less rack space than you need with competition.
11:00 Sarah Guo? A big front of competition is, as you just pointed out, there's performance, like overall performance, there's efficiency, and then there's like the software platform, like CUDA, RockM, et cetera. How do you think about the investment in the optimized math libraries and like how you want developers to understand your approach versus competitors?
11:22 Speaker 01 Yeah, you're so right, Sarah. It's multifaceted to be able to compete in this arena. You see many startups going after the space, but the fact is, the bulk of inferencing done today is done on general-purpose CPUs, not the huge LLM inferencing, but you know, just general inferencing for AI applications. And then for large language model applications, it's almost all on GPUs because that is the software and developer ecosystems out there. And so we've been competitive on CPUs. We've been gaining share at a rapid clip because we've got, you know, a very strong CPU generation after generation that we've been releasing
12:05 Speaker 01 on schedules we've laid out for the industry. But for GPU, it did take us until now to develop really world-class hardware and world-class software. And what we've done is ensure that because we're GPU, it should be easy to deploy. And so really making sure that we leverage the fact that we have all the GPU semantics. So if you're a coder, it's just easy to code if you're using the lower-level semantics. But also, we support all of the key software libraries that are out there. When you think about the kind of frameworks, whether it be PyTorch, we're a founding member of a PyTorch foundation,
12:50 Speaker 01 whether it be Onyx, whether it be TensorFlow, we are out there very closely working with developers. And so what we've now gone to, now that we have, you know, competitive and leadership offering, is what you'll see is that when you're deploying with AMD, very facile if you're, let's say you're using Hugging Face, any of the, you know, thousands and thousands of LLMs, open-source LLMs out there on Hugging Face. Well, we've partnered with Clem and his team. They test, as they release any of those language models, they're testing on AMD with our Instinct GPUs equally as they're testing on Nvidia.
13:29 Speaker 01 So we've really done the same thing as well with PyTorch, where we're one of two qualified offerings on PyTorch. And so all of that testing is being done, you know, routinely with the regression testing that's run literally every night on any software release. The other thing that's key is to learn from deployments. And so we've had early engagements like Lam and I, who's running on AMD. And they've been, they've been offering, you know, services of getting on AMD and running your L L M's on their, on their cloud, on their, their rack configurations they have. And so they've already been working with customers.
14:11 Speaker 01 And now, as you saw other people on stage with us at our December event, you can see that we're in there with a key hyperscaler and we're also being sold through many OEM applications and we're directly working within customers. So there's nothing like that feedback from key customers that are running on your platform to speed us, you know, ensuring that we can just be easily deployed and, and make sure that's, that it's a seamless process.
14:44 Sarah Guo? Yeah, yeah. Lam and I is a portfolio company for me and Sharon and Greg are great. I think it's an indication of you guys having a big ecosystem of software developers and machine learning people that want to see competition and more heterogeneous compute out there for these AI applications.
15:02 Speaker 01 So you cannot underestimate that. It tells you that it was a very constrained environment or there was a lack of a competition. It was bad for everybody, by the way, if there's a, if there's not competition because you, you really end up with a stagnant industry. You can look at the CPU industry before we brought competitive and leadership. It was really getting stagnant. You're just getting incremental improvements. And so the industry knows that and we've had tremendous pull and partnership and we're very appreciative of that. And, and in return, we're going to, we're going to keep providing generation after generation of, of competitive product out.
15:39 Sarah Guo? For such a huge like software stack like Rockham to be open source, like talk about that philosophy.
15:46 Speaker 01 It's a great question. It's very near and dear to us because we are, as I mentioned, all about collaboration. That's just such a strong part of our culture. And what open source does is it opens up technology to the community. And so if you look at the history of AMD, it's been very focused on open source. Our compiler for our CPUs is L L V M. It's open source. The L L V M is underneath our compilers on our GPU. But more than just the compiler on the GPU, we've opened up the Rock'em stack. It is our enabling
16:25 Speaker 01 stack. It was a huge piece in our winning supercomputing with such large installations we have. Why is it our philosophy? And by the way, Xilinx had exactly the same philosophy. And so bringing Xilinx and AMD together in 2022 did nothing more than even deepen that commitment to open source. But Sarah, the point is, we're not about locking in someone with a proprietary, wall garden software stack. What we want is, we want to win with the best solution. And we're committed to open source, and we're committed to giving our customers choice. We expect to win having the best solution, but we're
17:10 Speaker 01 not going to lock our customers in. We're going to win on merit, generation in and generation out.
17:15 Speaker 02 I guess one of the areas that I think is evolving very rapidly right now is sort of the clouds for AI compute. And so there's obviously the hyperscalers, the Azure from Microsoft and AWS from Amazon and GCP from Google. But there's also other players that have been emerging, you know, Base10, Together, Modal, Replicate, et cetera, et cetera. And one could argue that they both are providing differentiated services in terms of different tooling, API endpoints, et cetera, that the hyperscalers don't currently have. But also that in part, they have access to GPU, and there's a GPU shortage, and so that's also driving part of the utilization. How do you think about that market as it evolves over
17:55 Speaker 02 the next three, four years? And perhaps, you know, GPU becomes a bit more accessible, and maybe shortages or constraints fall away?
18:04 Speaker 01 Well, that's definitely happening. I mean, the supply constraint will go away. We'll be a part of that. We're ramping up and shipping as we speak on our Instinct line, and it's going quite well. It's going according to plan. But moreover, to answer your question, I think the way to think about it is that it's just breathtaking how the market is expanding so rapidly. I said earlier that most of the applications today that started on, you know, the generative AI with these LLMs, that's been largely cloud-based, and not just cloud-based, but hyperscaler-based, because it's such a massive cluster that's required, not just for the training,
18:44 Speaker 01 but frankly, for quite a bit of that type of generative AI LLM inferencing, also is on these massive clusters. But what's happening now is we're getting application after application that is just taking off nonlinearly. It's, and what we're seeing is a proliferation, is people are understanding how they can tailor their models, how they can fine-tune it, how they can have smaller nodules that don't have to answer any question you have or any application you need to support, but it might be just for your business and your area of exploration. And so that allows a tremendous variety of the size of compute and how you need to configure that cluster.
19:28 Speaker 01 So a rapidly expanding market, application-specific configurations you need for your compute cluster, and it moving even further, not just from these massive hyperscalers to, you know, I'll call it, you know, kind of tier-two kind of data centers, but it just keeps on going, because when you think about applications which are really bespoke and they can be run on the edge, right on your factory floor, where, you know, very low latency, put the inferencing and, you know, right at the source of data creation, right to end-user devices. So we've added AI inference accelerators right onto our PCs. We have been shipping it throughout all of 2023, and actually, at CES this year, announced already.
20:14 Speaker 01 Our next generation of AI accelerated PCs. And then, of course, with our Xilinx portfolio across embedded devices, we're getting a lot of pull from industry that has bespoke inference application right in a plethora of embedded applications. So with that trend, we're going to see more of that, more tailored compute installations with, you know, an attempt to service this ballooning demand.
20:46 Speaker 02 Yeah, that makes a lot of sense. I mean, I guess a lot or a subset of inference is going to push to the edge. And obviously, we'll have things on device, both on laptops as well as phones in terms of, you know, where certain small models will be running. And then it seems like there may be some ongoing potential set of constraints for larger models or larger data centers, at least in the short run. What are the main drivers of the constraints on the GPU supply side? Is that, you know, I've heard things around packaging. I've heard things around TSMC capacity. I've heard sort of a mix of like potential drivers of constraints. Some people say the next constraint after that is do you have enough power into data centers to actually run these? I just don't know what's real in terms of all this stuff. And so I'm a little bit curious like how to think about, you know, what are the constraints and how do we think about when those, the supply demand things come a bit more into balance?
21:30 Speaker 01 Yeah, supply demand is frankly something that any chip manufacturer, you know, has to manage. You have to secure your supply. You look during the pandemic, we had actually a tremendous run on our devices that stretched our supply chain because the demand for PCs went way up. People were working from home. The demand for our XA6 servers went way up. And so we were in a scramble mode during the pandemic and we did very well. We worked, we had shortages of substrates and we secured more substrate manufacturing capability. We worked closely with our primary wafer foundry supplier, TSMC.
22:16 Speaker 01 We're have such a deep partnership with them. We've had it for decades that if we get out ahead of it and we understand the Signals, we are generally able to meet the supply, or if there's a shortage, it's generally well-contained. And so, what's happening with AI is, yes, it is clear that we're seeing this, you know, this massive increase in the demand, and the fabs are responding, and you're having to not think of it just as a wafer fab, but you're absolutely right, it is the packaging. Our cells and our GPU competitor both use advanced packaging. I mean, I'll show you, I don't know if the camera will come across here, but that is our NI300.
23:01 Speaker 01 And what you see is a whole set of chiplets, so smaller chips with either, you know, a CPU function, I/O and memory controller, it can be the CPU for what the version we have that focuses on high-performance compute, we literally drop our CPU chiplets right in that same integration, and all the high bandwidth memory that you have around it to be able to feed those engines. And those are connected laterally, and on the NI300, we connect those devices vertically as well. So it's a complex supply chain, but it's one in which we are very, very good at. We're a fabulous company. We've been fabulous for, you know, coming on 18 years now.
23:44 Speaker 01 And so, we've got it down, hats off to the AMD supply chain team. And I think overall, as the industry, you'll hear that generally, we're going to move beyond those type of supply constraints. Now, you mentioned power. This is, I think, ultimately going to be certainly a key constraint, and you see, you know, all the major operators looking for sources of power. And for us, as a developer of the engines which are consuming that power, it brings tremendous focus for energy efficiency, and that we can drive into each generation of our design, and we are committed to that certainly, at very top priority.
24:28 Sarah Guo? One thing you said before, Mark, is that you were actually excited about the innovation of the end of Moore's Law and that being a reason that you actually wanted to go to AMD. Like, what directions of innovation should we expect investment in? I don't know if it's like too deep to ask you to give us a layman's understanding of like 3D stacking, but I think it is really interesting to think about it at a time when it's not obvious where to go.
24:53 Speaker 01 Well, no, Sarah, it's a great question. And the reason that I was so attracted to AMD is, one, it's had a storied history of being a disruptor in the industry. And I certainly felt very strongly that AMD could disrupt with very strong CPU and GPU, but more importantly, putting the pieces together. The idea of chiplets was just coming together. There was early exploration of that around that time. And the engineering team here at AMD, we were able to, you know, really get the team rallied and the key leadership rallied around it and drove that innovation.
25:35 Speaker 01 So, the reason it's so important is, when Moore's Law slows down, you know, the easy way to think about it is, it used to be that the chip technology itself, the foundry, going from one generation to the next, did most of the heavy lifting. So you could just bank on that new semiconductor technology node, shrinking your devices, giving you more performance, it'd have less power and it'd be at the same cost. So that was what Moore's Law was about. And with Moore's Law slowing, it means you still get those device improvements, but it costs more. Your power's not coming down as much as it used to, and you are still getting that integration.
26:20 Speaker 01 You're still certainly being able to pack more devices. But it demands more innovation. It demands what I call holistic design. So, you're gonna rely on those new transition devices, new foundry nodes, but how you use heterogeneous computing, meaning bringing the right compute engine for the right application, a CPU, a GPU, a dedicated engine like we have, super low power AI acceleration that we have in our in our PC devices and our embedded devices. So it's about getting, you know, tailored engines for the for the right application, leveraging chiplets that you combine them, put them on what is the best technology node you want each of those chiplets, each of those functions to be on.
27:04 Speaker 01 And then frankly, holistic design means you got to keep going right up through the packaging, how you package it together, how you interconnect it, and how you think about the software stack. And so it's literally got it, the the optimization has to be the full circle of transistor design, all the way up through the integration of your computing devices, and equally with a view of the software stack and applications. And what I'm thrilled about, along with all the engineers that I work with at AMD, is that we we have that opportunity, we have the building blocks, and we are built on collaboration. It's just such a part of our culture
27:46 Speaker 01 that we don't need to develop the entire system. We don't need to be the ones developing the application stack and the end applications. What we do is partner incredibly deeply and ensure that the solution is optimized end to end.
28:05 Sarah Guo? I think everybody is very suddenly interested in the chip industry from a strategic perspective as well. I think everybody's thinking more about the supply chain from the, you know, TSMC near monopoly to the idea of fab security in an increasingly complex geopolitical environment. How does AMD prep for this or think about these issues?
28:25 Speaker 01 Well, you know, you have to think about these things. We are very supportive of working with certainly the US governments and other governments across the world, which have exactly that question, how, you know, our country is running now on chip design that that powers such essential systems that it
28:47 Speaker 01 Becomes a matter of national security to make sure that there will be continuity of supply. And so we build that into our strategy. Uh, we build it in uh with our partners and so we've been supportive of of fab expansion. So you see uh TSMC uh building fabs in in Arizona and we're partnering with them. You see uh Samsung uh building fabs in in Texas. But it's not just in the US, they're actually expanding uh as well, just a a global uh of facilities in in Europe and and other parts of Asia. And so uh it goes beyond the foundry. It's the same thing with the packaging.
29:24 Speaker 01 So where do you, as you put those ships onto carriers and you need to interconnect it, you need that ecosystem uh to have geographic diversity as well. So the way we think about it is it it is a matter of importance for everybody to know that uh that there will be uh geographic diversity. And we are heavily engaged and actually I'm I'm quite pleased with the the progress that we're making. It takes, it doesn't happen overnight. That's the difference between uh chip design versus software. Someone can up, you know, for the software, you can come up with a new idea and get that product out very, very quickly. Get that uh, you know, MVP design, get it out there and and it can go viral. Uh, but it does take years of prep uh in expanding the supply chain.
30:09 Speaker 01 Uh, some, the whole semiconductor industry was built up as historically as well. This is a global industry and will create geographic pockets of expertise. So that's how we got to where we are today. Uh, but when you have uh, you know, more volatile, uh, you know, macro that uh that we're facing today uh with uh political tensions, with uh, you know, economic tensions. Uh, it's just imperative uh that we that we spread out uh that manufacturing capability and it's well underway.
30:41 Speaker 02 I guess one of the um other things that's been happening a lot recently uh is, and you know, you've been involved with, I think, some of the most interesting and exciting new consumer hardware platforms like iPhone and iPad and other things. And obviously um AMD now is powering many. Interesting types of devices and applications. What's your point of view on the new hardware things that people are building today? There's the Vision Pro, there's Rabbit, which is sort of an AI-first device, there's Humane, focused on the health side, there's Figure. It seems like there's suddenly an explosion of new sort of hardware devices. And I was just curious to get your perspective on what do you think tends to predict success for those types of products? What tends to predict failure? Like, how to think about this whole sort of suite of new things and devices that are coming our way?
31:25 Speaker 01 Well, that's a great question. I'll give you, you know, one point, I'll start just with sort of a technological point of view. I mean, I'm proud of the fact that chip design is part of the reason you're seeing all these different type of applications, because you're getting more and more compute capability that is shrunk down and draws such a low power that you can see more and more of these devices that have simply incredible computing and audiovisual capabilities that they can bring to you. I mean, you look at MetaQuest and Vision Pro and things like that, this isn't happening overnight.
32:05 Speaker 01 You look at the earlier versions, they were simply too heavy, too big, not enough computing oomph, because if the lag between, you know, seeing a photon on that screen on your head-mounted device and actually being able to process it, if that lags too high, you actually get physically ill wearing that and trying to watch a movie or play a game. So, one, I'm very proud of the technology advances that we've been able to make as an industry, and we're certainly very proud of our aspects that we drive from AMD. But the broader question that you've asked is, well, how do you know what's going to be successful?
32:48 Speaker 01 The technology is an enabler, but if there's one thing I learned at Apple... Uh, the devices are successful, really serve a need. I mean, they really give you a capability that you love. It's not just that, oh, it's incremental. I can do this a little better than something else I did before. It's gotta be something that you love and that creates a new category. Uh, so it it's enabled by technology, but it is the product itself that has to really excite you and give you new capabilities. I will mention one thing. I mentioned the AI enablement in PCs. Uh, that's gonna, I think it's almost gonna make PCs a new category.
33:29 Speaker 01 Because when you think of the kind of applications that you're going to be able to run, uh, with with super high performance, but yet low power inference you can run. Imagine right now, if I'm, I don't speak English at all and I'm watching this, uh, podcast. Let's say it was a lot, you know, if it's broadcast live and I click my live translation button, I I could just have it translated, uh, uh, to my spoken language with no perceptible delay. Uh, and that's just one of a myriad of new applications, uh, that that will be enabled.
34:03 Sarah Guo? Yeah, I I think it's a really interesting time because for many years, like increasingly and AM they benefited from some of this, right? You're also in, um, in the data center, but there is so much compute load moving to, uh, servers, right? Era of cloud, era of like all these like, you know, complex consumer social applications. I I think in like, in the new era of trying to create experiences and fighting, like all these like new application companies are fighting latency as a a primary consideration because you have, you have the network, the models are slow, you're trying to chain models and you have, you know, things you you want to do on device once again. Um, and I just think that hasn't been like a real design consideration for a while.
34:49 Speaker 01 Sarah, I I agree with you and I think it's it's one of the next set of challenges. Uh, and that is really tackling the idea of
34:59 Speaker 01 Not just enabling a high performance and AI applications on the cloud, on the edge, and these end-user devices, but thinking about how are they working together synergistically, writing applications that where you don't have that latency, that, you know, that dependency on a lag in computing, run it on the cloud. It's going to be the most, it's going to be the most efficient because you're optimizing this massive data center with the most efficient computing. But write the algorithms such that where you do have that need for super-low latency, you just need that instant response, have those aspects of the algorithms be at the edge, or in fact, on your end-user device. And often, when you need to react quickly,
35:44 Speaker 01 it just has to be the case. I mean, do you want to be in your vehicle that's being driven at a high degree of autonomous driving, suddenly get a loss of signal back to the cloud and you just stop, you know, because it says, I don't have a signal. You wouldn't stand for that.
36:03 Sarah Guo? So our audience is lots of engineers, founders, tech executives, consumers too. What do you want people to know about that AMD is focused on in 2024?
36:14 Speaker 01 Well, this, for us, is a huge year because we have spent so many years developing our hardware and software capabilities for AI. We've just completed AI-enabling our entire portfolio. So cloud, edge, you know, our PCs, our embedded devices, our gaming devices, we're enabling our gaming devices to upscale using AI. And 2024 is really a huge deployment year for us. So now, now's the bedrock's there, the capability's there. I talked to you about all the partners that we're working with. So 2024 is, for us, a
36:57 Speaker 01 huge deployment. I think we're often unknown in the AI space. Everyone knows our competitor, but we not only want to be known in the AI space, but Based on the results, based on the capabilities and the value we provide, we want to be known, you know, over the course of 2024, is the company that really enabled and brought AI across those breadth of applications. Yes, in the cloud and those, you know, massive LLM training and inference for generative AI, but equally across the entire compute space. And I think this is also the year that that expanded portfolio of applications comes to life.
37:42 Speaker 01 I look at what Microsoft is talking about and in terms of their enablement that they're doing of capabilities, cloud to client. And it's incredibly exciting and many, many ISVs that I've talked to are doing the same thing. And frankly, Sarah, they're addressing the very question you asked. How do I write my application such that I give you the best experience tapping both the cloud and the device that's in your hand or in, you know, in your laptop, you know, as you're, as you're running the application. So it will be a transformational year and we're so excited at AMD to be right in the middle of it.
38:23 Sarah Guo? Awesome. Looking forward to the year ahead and seeing great things. Thank you so much for joining us.
38:28 Speaker 01 Yeah, thanks for joining us. Well, thank you both. This is, like I said, you guys have just done a wonderful job here with NoPriors and I'm very happy and appreciative that you invited us on and loved the time with you. It's real pleasure.
38:45 Sarah Guo? Find us on Twitter at NoPriorsPod. Subscribe to our YouTube channel if you want to see our faces. Follow the show on Apple Podcasts, Spotify, or wherever you listen. That way you get a new episode every week. And sign up for emails or find transcripts for every episode at no-priors.com.
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