NVIDIA Getting into DC AI ASIC Market?

By Patrick Moorhead - February 20, 2024

The Six Five team discusses NVIDIA getting into DC AI ASIC market?

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Daniel Newman: I got like 20 questions. I’m going to make you just… I’m going to grill you on this one because you put out a post on social. It was so juicy and I felt like it didn’t get the pickup because I don’t think most people realize what you were saying. It’s too complicated. But this rumor, man, there’s a lot of implications.

Patrick Moorhead: Yeah, so let’s step back here. So NVIDIA is the data center GPU training and I think the generative AI inference king at this point, I think there was an article that said they had 95% data center market share. Probably makes sense. Maybe it’s closer to, I don’t know, 90, but it’s in the 90s. It’s big, big, big. And as we’ve talked about many times in the show, there are different ways to do training and different types of technologies and chips. And I like to look at it as kind of a barbell, right? Infinite flexibility. You have the CPU, you can run basically anything. The most efficient over here is an ASIC, which stands for application-specific integrated circuit. It does one thing and it does great. Now it’s just hard to program. And the GPU is kind of in the middle, maybe a little closer to the ASIC.

And just to confuse matters more, some people would call the GPU an ASIC, but let’s not go there. So the market right now for custom ASICs, you have Broadcom and Marvell, and then on the merchant ASIC, right, you’ve got stuff like Intel, Habana, you’ve got Groq, you’ve got Untether, companies like Untether AI, and we are seeing where it goes. Now, Dan, you and I have always talked about the AI ASIC being much more efficient and what’s going on here, but the rumor says, and this came out of Reuters, that NVIDIA is chasing the $30 billion custom ASIC market out there. And yeah, I got a little bit provocative, and Dan, you read between the lines of what I was saying, which is first of all, if this is true, totally justifies the market for data center AI ASICs, been all GPUs so far. NVIDIA already ships tiny ASIC blocks on its data center GPUs, and it’s called Tensor Cores.

And by the way, they’re Jetson platform. They have some specific, I think ASICs for convolutional network site, I forget what it’s called, DLA, maybe. If this is true, you would totally expect NVIDIA to play the CUDA compatibility software card, because right now, people are all in on GPUs, because they can get at least three generations of AI goodness out of them, if we look back historically. And naturally, if they were going to do that at, they would make this compatible with the CUDA tool set, the CUDA frameworks, and the CUDA models when it comes to generative AI. And the one thing that just popped into my head yesterday was where’s AMD in all this? AMD is a custom ASIC provider, a custom SOC provider to companies like Microsoft and Sony. It seems like they would be phenomenal in something like this, but hey, we’ll have to see what happens. I give it a 90% chance that NVIDIA gets into this custom market.

Daniel Newman: Hey, Pat, so let me interview you real quickly here, because you haven’t done a CNBC, so I’ll be CNBC, you’re going to be there. Just how hard, Pat, is it to enter the custom market?

Patrick Moorhead: Well, it’s a huge, huge commitment, and typically in any custom ASIC market, there is hundreds and hundreds of millions of R&D that you need to spend upfront before you get anything out there. Timeframe is key, too. I mean, the quickest reasonable ASIC that I’ve seen the pop out of the oven is maybe four years after inception, maybe three on a good day. Some companies will take what’s called NRE, non-recurring engineering, payments upfront. We’ve seen that for AMD. And when AMD did some of the first Microsoft and Sony ASICs or custom SOCs, they got big R&D payments that, by the way, made their gross margin… But they had to take the cost in their gross margin, which made the gross margin look low. But net margin business, it’s really good. So net-net, it’s hard, and it’s a pretty big commitment, because you’re also saying you’re going to support the software for a very long time.

Daniel Newman: Okay, two more quickies. So for companies like Marvell and Broadcom, Pat, is this an attack or is this complimentary?

Patrick Moorhead: So I think the market is so big that it would be complimentary. I don’t think this makes sense for the big hyper-scalers, just to be brutally honest. They already have a way to get custom done. I don’t know. I don’t see yet the incremental value that NVIDIA could bring over something custom, and it’s hard for me to imagine how would it be custom if NVIDIA is layering it in? It’s hard for me to kind of wrap my brain around that.

Daniel Newman: All right, well, listen, I’ve had fun interviewing you, Mr. Moorhead, thanks so much for joining CNBC.

Patrick Moorhead: Please have me on again.

Daniel Newman: Listen, there’s a couple of things too that occurred to me, but first and foremost is what you just said is look, the AI ASIC by the hyper-scalers, they’re going to go down their own route. What there is a little bit of an implication here that’s super interesting, though, is we’ve heard DGX Cloud, NVIDIA partnerships with companies like CoreWeave and others, as if you’re building competition, there is a benefit to potentially having this kind of IP and building. I also just, like I said, wonder, what’s it, about a $30 billion business, if it’s worth chasing, and how much incremental they’re going to get out of it, and where the incrementals can come from. It’s not going to be so much from the enterprise. So I have to imagine there’s some net revenue expansion coming out of this new AI data center concept, and being able to do it at scale.

But it’s going to be really interesting, Pat, because there’s a lot of speculation as to what’s going to happen next. I saw something, and this maybe transitions us really nicely to topic two, which is Intel’s IFS event that’s coming up.

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Patrick founded the firm based on his real-world world technology experiences with the understanding of what he wasn’t getting from analysts and consultants. Ten years later, Patrick is ranked #1 among technology industry analysts in terms of “power” (ARInsights)  in “press citations” (Apollo Research). Moorhead is a contributor at Forbes and frequently appears on CNBC. He is a broad-based analyst covering a wide variety of topics including the cloud, enterprise SaaS, collaboration, client computing, and semiconductors. He has 30 years of experience including 15 years of executive experience at high tech companies (NCR, AT&T, Compaq, now HP, and AMD) leading strategy, product management, product marketing, and corporate marketing, including three industry board appointments.