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Chapter 1: What is the main focus of Carmen Li's GPU market initiative?
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Hello and welcome to another episode of the Odd Thoughts Podcast. I'm Traci Alloway. And I'm Joe Wiesenthal. So, Joe, we're still continuing our series recorded from the live show in New York. We had a bunch of great conversations. A couple of them were building off of discussions that we had had previously.
And one of those discussions was in Chicago at another live show about six or seven months ago, back in October. We spoke with Don Wilson of DRW about the trading environment, but also about his new venture. Right. And so his new venture is one that actually there's quite a bit of competition in and quite of excitement in.
And it's essentially like, OK, GPUs, we know they're very important for the AI boom, et cetera. The question is, can GPU capacity, which is scarce. Can it become a tradable commodity such that I can buy futures to lock in my price of access to compute power? Could I resell those futures?
Will there be speculators speculating on the up or down price of like an H100, running an H100 NVIDIA chip for an hour? This is a big question. We know there's a lot of interest in the actual compute, but whether there's interest in compute futures as tradable instruments is very TBD. Yeah. And the analogy that everyone always uses is compute is the new oil.
So why can't it have a market structure that looks somewhat like the oil market? And there are challenges. Fungibility is a big one. Like one chip might not necessarily be equal to another chip. Or one chip, the same chip at one data center might not be equal to the same chip at a different data center. Exactly.
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Chapter 2: How does Carmen Li plan to standardize GPU pricing?
That's right. Literally, this is all the question I get. It's pretty fascinating. So when we look at volatility, we look at daily volatility movement, not the price up and down, right? The daily volatility for A100, H100 is around 20 to 30. There's a very healthy commodity volatility range. So I don't manage volatility. It just happened to be that volatility that can change.
It's all because we normalize it. If you look at each individual chip configuration at different geolocation, the volatility are different. There are some chips with 80% volatility, some chips with over 100. Because normalization of indices, you actually get very healthy 20 to 30 daily vol.
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I'm always fascinated by, like, you know, we look at the Bloomberg terminal, for example, and there's a price on the screen and it's just there. And we started taking for granted that like it had to come from somewhere. And maybe some commodities have like a, you know, there's an existing exchange and a public price. And then there's also a lot of commodities, just bilateral trades.
What is the actual process by which you collect the most recent data? So if you say, OK, an hour of H100 usage costs x, whatever it is right now, how did you assemble that number? How did you gather that information from, say, the inference providers?
So it can be lengthy. It depends on what data source is. The nature of GPU spot markets, so Compute Change is one of them, and then many, many new clouds, hyperscaler marketplaces, all have very different contract size, durations, specs, and their way to manage their data, right?
So it's a lot of licensing conversation, negotiation, and also context, like myself, I don't know, I was used for Bloomberg data, so I was in data business for a period of time, so everything is pretty intuitive to me. It's very important to get a variety of data sources, especially for computer.
But do you call them up? So it's like, OK, the price is different on a Friday versus what?
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Chapter 3: What challenges does the GPU market face regarding price volatility?
Once those things start trading, the market always gravitates with things that actually help them hatch, right? If you're easily manipulatable, if you are not data source, people actually use you hatch, what's the point aside from speculation, right? So, you know, I love to say, I mean, I also strongly believe we're the best, but again, I will let the market decide, which will happen very soon.
So, of course, like, yes, there's the economic rationale for the existence of a hedging instrument. And we can understand that someone who is an entity that from time that needs compute, they're short, implicitly short GPUs, they want to hedge, etc. But the liquid markets also really do need speculators and they need people betting on price.
What are you seeing right now in terms of traders or institutions, et cetera, who economically can take both sides of the trade? And how active is this getting where there's just a compute trading desk that is separate from their economic needs?
The conversation has been going on for a very long time with various banks, various market participants, speculators. They are all very excited. So some banks obviously have those both sides of the trade, right? So they can cross off some positions internally. That's great. Obviously some, they have to use leverage external products. So that's where we come in.
The way I encourage them to do is I selfishly, I want them to start trading that on compute. The more people trade, the better for me, right, selfishly. But at the same time, it's important for people to understand GPU trading, it's not like you can't just move someone's phone, trade all your electricity with no background context, drop into GPU, compute futures.
There's a lot of contexts where, number one, GPU, it is not a homogeneous product. Number two, you have to understand the use cases for A100, H100. Right now, they are not that correlated. Is that right? Maybe that's not right. I don't know. There are use cases which are pretty separated. But maybe there are use cases they can be transferred. And also there's a software layer to this, right?
So right now, you can argue certain use cases, some large models cannot be deployed. the legacy chips, but doesn't mean six months later, you cannot do so. As the software layer compression, model compression gets better, optimization gets better, things can change. So really understand not just the hardware configuration, this local supply demand curve for the
server itself, also the software layer. That's kind of critical, right? That's really changed the supply-demand curve and all the way to the user behavior. So it's going to take some time, so we have to engage with a lot of participants, make sure they have the right setup.
I have what is possibly a dumb question, but the compute futures, how are those actually settled? Cause I have like images in my mind of taking physical delivery of like maybe one of those big chips.
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Chapter 4: Who are the potential buyers in the GPU spot market?
As in like, let's say there's probably some very bright people in the room, now with an institution, when it's all listed and everything, is it need to go through like a futures broker? Is it like, could it be like a prediction market where you just go to the website? Like what is the actual, how does someone actually get in this?
Setting aside whether they're sophisticated enough of whether they know what they're doing. A lot of people trade who have no idea what they're doing. Yeah, setting all this aside, yes, you know, only trade what you know. But like, what is it through a prime broker? Like, how will people actually be able to participate in this market?
The beauty of CME is you can do the same thing you're doing now, trading CME products.
Okay.
The same process, same margin. That's why you get great margin optimization, right? Everything is BAU. It's no different. We don't have anything right now.
So any commodities broker that someone has, they will be able to, on that platform, they will have access to these instruments.
Exactly right.
Yeah, we make it easy for people. Would you be upset if a prediction market set up a GPU price contract of some sort with that into your business?
Not at all. So we actually work with Polymarket last year.
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Chapter 5: How are futures contracts structured in the GPU market?
Someone actually listed my product at Polymarket with my consent. It's always start like that. And then someone told me that. And then we try to Polymarket say, hey, do you want to do something, you know, more real. So we did February settled and April settled a few contracts on Polymarket just to test the water. Right. Obviously, we're exclusively with CME right now.
But yeah, so I think obviously you have to do it right. Right. Licensing nominal, right. All the right things. Yeah. I you know, I don't market can do whatever they want and then people will choose the best product for them to use.
Setting aside the the financial instruments for the moment. When people think about AI and they think about the use of GPUs, they mostly still probably in their mind think of like OpenAI, Anthropic, and Google, basically. And that's kind of it.
But obviously, as you've stated, like the world of entities that serve inference in some form or another is much greater than these three companies that we talk about. Talk to us a little bit more about what the actual world of inference provision looks like outside of the big household AI names.
the ones you mentioned they mostly are closed source models as we call it right but they do have some open source versions but they're famous for their closed source models so we actually track 300 open source open weights closed source models globally from pricing and consumption point of view it is really interesting if we have actually you know we haven't really formally launched lm token indices you can kind of look at bloomberg and it's on bloomberg what's interesting is
people are depends it's all based on your choices right now the price actually doubled final indices from now from december 1st last year it's like 2.21 dollar per million token it's a mixture of input open token prices average weighted by consumption by basket models it's not here this is gpu unfortunately
Since we have this specific chart up right now, what is the y-axis in this chart showing?
So you're looking at the dollar per GPU per hour rental rate on demand for three chips. The top one, the yellow line, is B200 Neo Cloud on demand per GPU per hour. Sorry, it's a mouthful. The line, the yellow line is interesting, right? So every new chip we came out, based on historical data, A100, H100, usually came out to be high.
Okay.
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Chapter 6: What is the significance of the GPU lottery concept?
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