The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
20VC: Why Google Will Win the AI Arms Race & OpenAI Will Not | NVIDIA vs AMD: Who Wins and Why | The Future of Inference vs Training | The Economics of Compute & Why To Win You Must Have Product, Data & Compute with Steeve Morin @ ZML
Mon, 24 Feb 2025
Steeve Morin is the Founder & CEO @ ZML, a next-generation inference engine enabling peak performance on a wide range of chips. Prior to founding ZML, Steeve was the VP Engineering at Zenly for 7 years leading eng to millions of users and an acquisition by Snap. In Today’s Episode We Discuss: 04:17 How Will Inference Change and Evolve Over the Next 5 Years 09:17 Challenges and Innovations in AI Hardware 15:38 The Economics of AI Compute 18:01 Training vs. Inference: Infrastructure Needs 25:08 The Future of AI Chips and Market Dynamics 34:43 Nvidia's Market Position and Competitors 38:18 Challenges of Incremental Gains in the Market 39:12 The Zero Buy-In Strategy 39:34 Switching Between Compute Providers 40:40 The Importance of a Top-Down Strategy for Microsoft and Google 41:42 Microsoft's Strategy with AMD 45:50 Data Center Investments and Training 46:40 How to Succeed in AI: The Triangle of Products, Data, and Compute 48:25 Scaling Laws and Model Efficiency 49:52 Future of AI Models and Architectures 57:08 Retrieval Augmented Generation (RAG) 01:00:52 Why OpenAI’s Position is Not as Strong as People Think 01:06:47 Challenges in AI Hardware Supply
Chapter 1: Who is Steeve Morin and what does ZML do?
Steve is the founder of ZML, a next generation inference engine, enabling peak performance on a wide range of chips. Literally the perfect speaker for this topic. And this was a super nerdy show. It was probably the most information dense episode we've done in a long time. So do slow it down, pause it, get a notebook out. But wow, there is so much gold in this one.
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Chapter 2: How does NVIDIA's strategy differ from AMD's in AI?
Probably the number one, you know, I would say obvious thing would be that if you ask a model to generate an image, then it will, you know, switch to a diffusion model, right? Not an LLM. And there's many, many more tricks. The turbo models and OpenAI do that. There's a lot of tricks.
So definitely models in the sense of getting, you know, weights and running them is something that is ultimately going away because, you know, in favor of like full-blown backends, right? You feel like you're talking to a model, but ultimately you're talking to an API. The thing is, that API will be running locally in your own cloud instances and so on.
So we will have a world where we're switching between models, and there's kind of this trickery around them. OK, perfect. So we've got that at the top, then we've got ZML in the middle, and then you said, and then on any hardware. So will we be using multiple hardware providers at the same time, or will we be more rigid in our hardware usage?
No, absolutely. You can get probably an order of magnitude more efficiency depending on the hardware you run on. That is substantial. Not a lot of people have that problem at the moment. Things are getting built as we speak. But a simple example is if you switch from NVIDIA to AMD on a 7 TB model, you can get four times better efficiency in terms of spend. So that is substantial.
That is very much substantial. Now, the problem is getting some AMD GPUs, right? I'm really sorry.
If there is such a cost efficiency four times, why does everyone not do that?
So there's a few reasons. Probably the most important one is the PyTorch CUDA, I would say, Duo. And that's very, very hard to break. These two are very much intertwined. Can you just explain to us what PyTorch and CUDA are? Oh, yes, absolutely. Yeah, yeah. PyTorch is the ML framework that people use to build actually trained models, right?
You can do inference with it, but by far the most successful framework for training is PyTorch. And PyTorch was very much built on top of CUDA, which is NVIDIA software, right? Let's just say the strengths of PyTorch make it ultimately very, very bound to CUDA. So of course it runs on, you know, it runs on AMD, it runs on, you know, even Apple and so on.
But there was always, you know, the tens of little details that not exactly run like, you know, you would expect and there's work involved, but then also there's supply. So probably that's the number one thing. The second thing is there's a lot of GPUs on the market. Pretty much all of them are NVIDIA.
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Chapter 3: What challenges exist in AI hardware supply?
I mean, they're not idiots, right? How should agents change NVIDIA's strategy? Hard to say, because NVIDIA has a very, very vertical approach. They do more of more, right? Like if you look at Blackwell, it's actually crazy what they did for Blackwell. They assembled two chips.
But the surface was so big that the chip started to bend a bit, which further perpetuated the problem because it then didn't make contact with the heat sink and so on. So they are very much, and you know, the power envelope, they push it to a thousand watts. It requires liquid cooling and so on. So they are very much in a very vertical foot to the pedal in terms of GPU scaling.
But the thing is, GPUs are a good trick for AI, but they're not built for AI. It's not a specialized chip. It is a specialization of a GPU, but it is not an AI chip.
Forgive me for continuously asking stupid questions. Why are GPUs not built for AI? And if not, what is better?
So the way it worked is that you can think of a screen as a matrix. And if you have to render pixels on a screen, there's a lot of pixels and everything has to happen in parallel, right? So that you don't waste time. Turns out, you know, matrices are a very important thing in AI.
So there was this cool trick in which we essentially tricked the GPU into, back, that was like probably 20 years ago, we would trick the GPU into believing it was doing graphics rendering, where actually we were making it do parallel work, right? It was called GPGPU at the time, right? So it was always a cool trick. But it was not dedicated for this.
The pioneers probably were, of course, Google with TPU, which are very much more advanced on the architectural level. But essentially, the way they work, it kind of works for AI. But for LLMs, that starts to crack because they're so big and there's a lot of memory transfers and so on.
Actually, that's why Grok achieves, not Grok, but Grok, Cerebras, and all these folks, they achieve very high performance single stream is because the data is right in the chip. They don't have to get it from memory, which is slow, which GPU has to do. So there's a lot of these things that ultimately make it a good trick but not a dedicated solution per se.
That said, though, the reason probably NVIDIA won, at least in the training space, is because of Mellanox, not because of the raw compute. Because you need to run lots of these GPUs in parallel. So the interconnect between them is ultimately what matters. How fast can they exchange data? Because remember,
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Chapter 4: Why might NVIDIA not dominate the inference market?
With increasing competitiveness within each of those layers, do we not see margin reduction?
Absolutely, yes. Here's the problem, though. Let's say you are on Google Cloud and you're on TPUs. Suddenly, you just removed that 90% chunk on the spend. The problem is that for multiple software reasons, which we are solving at DML, is that they're not really, I would say, a commercial success. They are very much successful inside of Google, but not much outside of Google.
Amazon, same, is pushing very, very hard for their, you know, Tranium chips. So the future I see is that you use whatever, you know, your provider has because you don't want to pay, you know, 90% outrageous margin and try to make, you know, a profit out of that. Okay.
So when we move to actually inference and training, everyone's focused so much on training. I'd love to understand what are the fundamental differences in infrastructure needs when we think about training versus inference?
So these two obey fundamentally different, I would say, tectonic forces. So in training, more is better. You want more of everything, essentially. And the recipe for success is the speed of iteration. You change stuff, you see how it works, and you do it again. Hopefully it converges. And it's like changing the wheel of a moving car, so to speak. So that is training.
On inference, this is a complete reverse. Less is better. You want less headaches. You don't want to be working up at night because inference is production. You could say that training is research and inference is production. And it's fundamentally different. In terms of infra, probably the number one thing that is the number one difference between these two is the need for interconnect.
So if you do production, if you can avoid to have interconnect between, let's say, a cluster of GPUs, of course you will avoid that if you can. And this is why models have the sizes they have. It's so that people can run them without the need to connect multiple machines together. It's very constraining in terms of the environment.
So that is probably the fundamental difference, the need for interconnect. And number two is, ultimately, do you really care about what your model is running on as long as it's outputting whatever you want it to output?
Can you just help me understand, sorry, why is training more is more and that's great and in inference less is more? Why do we have that difference?
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Chapter 5: What is latent space reasoning and its implications?
And actually, that market won't be won by NVIDIA.
Technically speaking, he is right. But realistically speaking, I'm not sure I agree. The thing is, these chips are on the market. They're here. I'll tab on Chrome and get one. That is something that I don't take lightly. Availability, that is, right?
I think Nvidia is used to stay, at least if not for the H100 bubble bust, because these chips are going to be on the market and people will buy them and do inference with them. Remains to see the OPEX and the electricity, etc., but... The thing is, the only chips that are really frontier on that sense are probably TPUs and then the upcoming chips.
But the thing is, they're great chips, but they're not on the market. Or like there are outrageous prices, like millions of dollars to run a model. So what chips are great and why aren't they on the market? Let's say for instance, Cerebras, incredible technology, incredibly expensive. So how will the market value the premium of having single stream, very high tokens per second?
There is a value into that, right? As we saw with Mistral and Perplexity, but I think there was another loss. I don't know, I don't have the details, But I think it was done at a loss that Cerebrus put it out. So today there's three actors on the market that can deliver this. I think this will be, I would say, the pushing force for change in the inference landscape, agents and reasoning.
So that is very high tokens per second only for you.
What is forcing the price of a Cerebrus to be so high? And then you heard Jonathan at Grok on the show say that they're 80% cheaper than NVIDIA.
So there's this trick. Because here's the thing, there's no magic. This little trick is called SRAM. SRAM is memory on the chip directly. So that is very, very fast memory. But here's the problem with SRAM. is that SRAM consumes surface on the chip, which makes it a bigger chip, which is very hard in terms of yield, right? Because the chances of problems are higher and so on.
So SRAM is, I would say, very, very, very fast memory, which gives you a lot of advantage when you do very, very high inference, but it's terribly expensive. And if you look at, for instance, Grok, they have on their generation, this generation, they have 230 megabytes of SRAM per chip. A 7TB model is 140 gigabytes. So you do the math, right?
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Chapter 6: How does memory technology impact AI chip performance?
Do you think NVIDIA owns both of those markets in five years' time?
Depends on the supply. I think that there's a shot that they don't. Because here's the thing, you know, even if we take, you know, same amount of, you know, let's imagine we have a new chip from Amazon, right? That is the same amount. Oh, wait, we do. It's called Tranium. You know, why would I pay 90% margin of NVIDIA if I can freely change to Tranium? My old production runs on AWS anyways, right?
If you run on the cloud and you're running on NVIDIA, you're getting squeezed out of your money, right? So if you're on production on dedicated chips, of course, so maybe through commoditization, but hey, I'm on AWS, I can just click and boom, it runs on AWS's chips. Who cares, right? I just run my model like I did two minutes ago.
With that realization, do you think we'll see NVIDIA move up stack and also move into the cloud and model?
They are. They have a protocol NIM, sort of does that. The thing with NVIDIA is that they spend a lot of energy making you care about stuff you shouldn't care about. And they were very successful. Like, who gives a shit about CUDA? I'm sorry, but I don't want to care about that, right? I want to do my stuff.
And NVIDIA got me into saying, hey, you should care about this because there's nothing else on the market. Well, that's not true. But ultimately, this is the GPU I have in my machine. So, you know, off I go. If tomorrow that changes, why would I pay 90% margin on my compute? That's insane. This is why I believe it ultimately goes through the software. This is my entry point to the ecosystem.
If the software abstracts away those idiosyncrasies, as they do on CPUs, then the providers will compete on specs and not on fake modes or circumstantial modes, right? So this is where I think, you know, the market is going. And of course, there's the availability problem. There is, you know, if you, you know, piss off Jensen, you might need to kiss the ring, you know, to get back in line, right?
But I mean, ultimately, I don't see this as being sustainable.
When we chatted before, you said about AMD. And I said, hey, I bought NVIDIA and I bought AMD. And NVIDIA, thanks, Jensen, I made a ton of money. And AMD, I think I'm up 1% versus the 20% gain I've had on NVIDIA. You said that AMD basically sold everything to Microsoft and Meta and had a GTM problem. Can you just unpack that for me?
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Chapter 7: What role does compute in memory play in AI's future?
What is the right sustainable strategy then? You don't want to go so heavy that you can't ever get out and you have that switching cost.
Right.
But you also don't want to sprinkle it around and do, as you said, multiple.
Absolutely. The right approach to me is making the buy-in zero. If the buy-in is zero, you don't worry about this. You just buy whatever is best today. How do you do that by renting? Oh, because this is what we do. This is our promise. Our thesis is that if the buy-in is zero, you know, you completely unlock that value.
When you say the buy-in is zero, what does that actually mean?
It means that you can freely switch, you know, compute to compute, like freely, right? You just say, hey, now it's AMD, boom, it runs. You just say, oh, it's 10 store and boom, it runs, right? How do you do that then?
Do you have agreements with all the different providers?
Oh, yeah, yeah, yeah. Not agreements, but we work with them to support their chips. But the thing is, at least as a user myself of our tech, is that if it's free for me to switch or to choose whichever provider I want in terms of compute, AMD, Nvidia, whatever, then I can take whatever is best today, and I can take whatever is best tomorrow, and I can run both.
I can run three different platforms at the same time. I don't care. I only run what is good at the moment. And that unlocks, to me, a very cool thing, which is incremental improvement. If you are 30% better, I'll switch to you.
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Chapter 8: What are the economic dynamics of AI compute?
And mind you, TPUs do training. They are the only ones. We're training them now. But AMD can do training, but in terms of maturity, by far the most mature software and compute is TPUs, and then it's NVIDIA, right? So the buy-in is so high that people are like, no, fuck. We'll see, right? I'm not on Google Cloud. I have to sign up. Oh my God, right? So these are tremendous chips.
These are tremendous assets. Now, in terms of the risk, I think if you want to do it, you have to do it top to bottom. You have to start with whatever it is you're going to build and then permeate downwards into the infrastructure. Take, for example, Microsoft with OpenAI. They just bought all of AMD's supply and they run ChatGPT on it. That's it. And that puts them in the green.
That's actually what makes them profitable on inference. Or at least, let's say, not lose money.
I'm sorry, how does Microsoft buying all of AMD's supply make them not lose money on inference? Just help me understand that.
Because I can give you actual numbers. If you run eight H100, you can put two 70B models on them because of the RAM. That's number one. Number two is if you go from one GPU to two, you don't get twice the performance. Maybe you get 10% better performance. Yeah, that's the dirty secret nobody talks about. I'm talking inference, right?
So you go from, let's say, 100 to 110 by doubling the amount of GPUs. That is insane. So you'd rather have two by one than one by two, right? So with one machine of H100, you can run two 7 TBs model if you do four GPUs and four GPUs, right? That's number one. If you run on AMD, well, there's enough memory inside the GPU to run one model per card. So you get eight GPUs, eight times the throughput.
While on the other hand, you get eight GPUs, two, maybe two and a half times the throughput. So that is a Forex right there, just by virtue of this. So that is the compute part. But if you look at all of these things, there are tremendous amount of, we talked to companies who have chips upcoming with almost 300 gigabytes of memory. on it, right?
So that is, you know, a model like one chip per model. This is the best thing you want if you're on seven TBs, right? So, which is what I would say, not the state of the art, but this is the regular stuff people will use for serving.
So if you look, you know, top to bottom and you know what you're going to build with them, then it's a lot better to do the efficiency gains because four times is a big deal, right? And mind you, these chips are 30% cheaper than Nvidia's. It's like a no brainer. But if you go bottom up and say, I'm going to rent them out, people will not rent them. Simple.
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