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Intel Gaudi Performance – Beats NVIDIA?

Intel Gaudi Performance - Beats NVIDIA?

The Six Five team discusses Intel Gaudi Performance – Beats NVIDIA?

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Transcript:

Daniel Newman: All right man. Listen, I tweeted something out the other night. I think this is probably where this topic came from, and I kind of said something along the lines of, “We don’t talk about Gaudi enough.” The last several months there’s been kind of this weird gap that’s been created. We talk about NVIDIA, H100 now the B Series and the Grace Blackwell, and then we talk about homegrown silicon being provided by the cloud providers and it’s NVIDIA AMD. It’s like NVIDIA has AMDs, they’re looking at them and that’s the competition. And then over here we’re looking at… But we do talk a lot about accelerators. You actually had a great tweet this week about ASICs and the need to create standards so that we can scale the development in that particular area, Pat. But one of the things that we haven’t talked a lot about is Intel and whether or not… I know we talk about 2025 and their potential GPU, but Pat, we’ve talked a lot on the show about how ASICs and even the XPU can be very competitive in certain cases to NVIDIA.

And this week Intel put out a newsroom post, this probably isn’t like a 20 minute discussion, but it’s a few minutes here. And they basically talked about the MLCommons putting new results industry standard MLPerf benchmark for inference. And it basically noted that the Gaudi2 and fifth gen Intel with their AMX, which is the accelerated extensions essentially can be a very good alternative to H100s for generative performance as it relates to inference, Pat. And I guess I just was thinking to myself when I’m looking at this is, “Gosh, why does nobody talk about Intel? Why is Intel being written off?

Now, of course, I can give you a quick argument of that because they haven’t talked about enough big cloud wins yet. And I think the fact that we’ve heard about Gaudi, we’re seeing its performance. By the way, this is a really strong performance with their Gaudi2 and guess what’s coming?

Patrick Moorhead: Oh gosh, Gaudi3.

Daniel Newman: Gaudi3.5. No, I’m kidding. That’s GPT. Gaudi3. So the point is, with their almost last generation, you know how we love to do the generations thing, Pat, we love to talk about, “Well gosh, NVIDIA’s chip that isn’t even shipping yet is kicking AMD’s butt. Well, hold on a second. H100s were outperformed in many ways by the new AMDs. And now yes, NVIDIA’s answered that with a product that’s going to ship in the future, but same thing here. So now we have an Intel product that’s coming that’s more performance in certain inference cases than the NVIDIA chip. Now, said that Pat, you and I think have to be very, very clear because we know a lot of people in the chip space listen to us, this is not a GPU, it does not have flexibility and programmability like a GPU, but in cases where inference in language is super important, this is a really efficient performance alternative with strong specs, strong metrics, and they talked about it on LLaMA, on Stable Diffusion, on Hugging Face text generation, so on a number of different workloads, this particular chip performed.

So the moral of my story is the world loves to write off intel, and I’m sure Pat Gelsinger loves what he calls the permabears. I just think between now the Gaudi3 and then ’25 when they start to deliver their GPUs, if there really is a 250 and upwards of potentially $400 billion TAM for GPUs over the next four years, five years, is what we’re hearing, I think there’s a real shot. Intel is going to get a piece of that business. And I know I’m a little too positive on Intel, I hear it sometimes from people. But people like to always tell me why they’re right and I like to mark it as this date, 3/29/2024 when I told them I think they’re wrong.

Patrick Moorhead: Wow, you left me a little oxygen. Let me take a little bit of a difference. So first off, the claim was not that it was better performance with Gaudi2, it was that it was best price performance and it’s 40% more. And when I stand back and say, “Hey, would I shift for 40%?” I probably wouldn’t if I needed three years of different types of models, but if it’s a steady state workload, 40% is a ton. The one thing that got a little bit buried in the lead was that Intel Xeon was the only processor tested or SOC tested with and like you said, AMX extensions and think of AMX as a little accelerator that sits on the Xeon SOC. And I think that’s a major accomplishment in that we didn’t see anything from AMD. Now, a MD does not have acceleration capability like AMX. It does have a massive FPU, and then a massive matrix engine that’s leveraged by SSE2, but that’s very different and less efficient for many workloads compared to AMX.

Dan, we have debated on this show that if only two people showed up for a gunfight, was there really a gunfight? And one thing I did appreciate from MLCommons, this is David Cantorc xxd, we’ve all been on briefing calls together and he said, “Submitting to MLPerf is quite challenging and a real accomplishment Due to the complex nature of ML workloads, each submitter must ensure that both their hardware and software stacks are capable, stable, performant for wanting these types of ML workloads.” And that message was directed at Dell, Fujitsu, NVIDIA and Qualcomm that submitted data center focused power numbers, but those power numbers had to be run while you’re running the ML inference out there.

So I think first of all, it’s good to acknowledge why others weren’t on there, but I still kind of question that if you only have two people show up for a certain benchmark, what’s the value of that? So, I mean we’ve already debated that I think on these MLCommons benchmarks, but I think it is a reflection of the difficulty of AI in totality. So Dan, let’s move to the next topic.

Daniel Newman: Can I say one thing?

Patrick Moorhead: Please.

Daniel Newman: I’m glad you called it out. I want to make sure I’m correct when I said it, I said on par, not equal, but I said that, and I believe it’s A100s, that it actually outperformed H100s that it was near par. So I should say, “Near par” not, “Outperform.” If I said outperform, I was wrong. I’m correcting myself.

Author Information

Daniel is the CEO of The Futurum Group. Living his life at the intersection of people and technology, Daniel works with the world’s largest technology brands exploring Digital Transformation and how it is influencing the enterprise.

From the leading edge of AI to global technology policy, Daniel makes the connections between business, people and tech that are required for companies to benefit most from their technology investments. Daniel is a top 5 globally ranked industry analyst and his ideas are regularly cited or shared in television appearances by CNBC, Bloomberg, Wall Street Journal and hundreds of other sites around the world.

A 7x Best-Selling Author including his most recent book “Human/Machine.” Daniel is also a Forbes and MarketWatch (Dow Jones) contributor.

An MBA and Former Graduate Adjunct Faculty, Daniel is an Austin Texas transplant after 40 years in Chicago. His speaking takes him around the world each year as he shares his vision of the role technology will play in our future.

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