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This was recorded on the sidelines of the new Compute Trading track at AIE NYC. Catch us next at AIE CODE, our last flagship for the top agentic engineers in the world. In the early days of cloud, former guest Martin Casado made something so disruptive that Cisco was threatened enough to pay $600m for it, and VMware outbid for $1.3B: That concept was software-defined networking, which virtualized the routing network by separating the control plane from the data plane. That revolution is now coming for GPUs - where previously running your own supercompute cluster required $50-100m upfront to get commitments and do the entire stack from hardware to firmware to kernels, SF Compute is now enabling you to not only buy or sell GPU hours now and in the future, but also to programmatically control everything from your network to memory. We last spoke with Evan about commoditizing compute. At AI Engineer New York, we went deeper: what does it take to physically deliver a GPU contract, and what has to be true before a market in compute can become liquid? From selling a room to running the hotel Evan describes SF Compute’s expansion with a hotel analogy. A customer wants a room, then asks for cleaning, room service, and security. Eventually you discover that delivering the room means operating the hotel. SF Compute began by trying to sublease a cluster; the company has since built much of the operating capacity of a cloud to deliver the compute traded on its market. That work is physical settlement: the buyer gets usable compute, rather than simply a cash payment against an index. It brings the physical constraints of a cluster into the market itself. Can a workload be interrupted? How quickly can it recover from storage? Which GPU generation, networking and software configuration does it need? Those details affect both the contract and the price. Making compute tradeable Rather than waiting for every GPU cluster to become interchangeable, SF Compute matches orders with different constraints. Evan argues that this makes trading possible while the technology changes, at the cost of spreading liquidity across more variants. We also discuss spot and forward pricing, batch versus live inference, and why an index can become disconnected from the cost of actually buying compute. Independence is central to Evan’s case for a market operator. He argues that companies with large hardware or inference businesses have incentives that make it harder to run a neutral exchange. We also get into the unglamorous engineering: visiting sites, designing clusters, running the backplane, and making infrastructure programmable. Near the end, he describes the ability to subdivide InfiniBand fabric on the fly as one example. Guest: Evan Conrad, CEO of SF Compute. Show notes - Evan’s AI Engineer NYC talk: How Compute Markets Work - Our previous SF Compute conversation - Why oil futures went negative in 2020 — EIA - How SF Compute customers resell unused GPU capacity - SF Compute’s $245M compute sale - Abel: SF Compute’s settlement ledger - CFTC: Barclays and LIBOR manipulation - Elastic InfiniBand partitions Timestamps - 0:00 — Why can’t NVIDIA build this? - 0:25 — Welcome back to Latent Space - 2:23 — From selling a room to running the hotel - 3:47 — Physical settlement: actually delivering compute - 6:00 — Liquidity and the GPU order book - 8:42 — Trading nonstandard compute - 10:00 — Interrupting and moving workloads - 12:20 — Why hardware cohorts matter - 14:32 — How far out do compute contracts go? - 16:19 — Batch versus live inference leases - 17:58 — The shape of compute prices - 20:33 — GPU generations, specifications and SKUs - 23:30 — Physical versus cash settlement - 27:00 — The emerging compute market ecosystem - 29:03 — What it takes to deliver a GPU cluster - 32:24 — Sovereign compute - 33:26 — Why NVIDIA can’t build this market - 34:48 — A programmable data center Transcript Automated transcript, lightly formatted; speaker labels have not been verified. 00:00 NVIDIA is obviously like the 800-pound gorilla in the room. Why didn’t they make SF Compute? So our impression is that NVIDIA can’t make it, but benefits tremendously from it existing. The reason why they can’t make it is because they would then not be independent enough for people to trade on it. 00:25 Anyway, Evan, welcome back to Latent Space. Thank you so Welcome back to AIE. Actually, first time at AIE. I think so, yeah. This is a very special one because I feel like you have been working on compute trading, compute markets for a while. Now it’s like feeling of the moment. Yeah. Jump basically announced that they are getting into compute markets, which is why I’m excited to sort of cluster people. You met Oliver from SemiAnalysis, who is basically initiating coverage on compute markets is my read on this. So people are going to watch your talk. 01:00 I think it’s a very good explanation of both your origin story and why you need to exist and all the hard problems you work on. Yeah. Done in a very simple, engaging manner. I’m very jealous of your writing skills because you showed the flyer that you showed at New Reps. Yeah. I was like, it’s pretty straightforward and simple and it’s good. Yeah. So anyway, congrats. Thank you. I appreciate that. And also congrats on AIE. It seems to, y’all are rolling. We’re rolling. I just came back from the site tour for next year’s AIE. then for You have like a hint? At Javits Center. It’s the same place they do Comic Con. Okay. Wow. 01:35 Wow. Wow. Damn. But I think the finance focus is really hitting for folks. Yeah. So we’re going to try to split out. Like this is, here we just lump everything in finance. Like your finance, their finance, like insurance is finance, but like they’re all super different. Yeah. So we’ll probably break out things more minutely in future because people want to go deep on hedge fund stuff or banking or personal wealth. Yeah. Especially in New York. Yeah. Anthropic was like, yeah, we’re all about RIAs right now. Yeah. RIAs like registered investment advisors. Yes. Like so different from you. 02:10 No, no, no. I’m aware of this. Yeah. Yeah. Okay. So for listeners who don’t know, we did a podcast two years ago where you talked about your, you know, where SF Compute was. Um, today you’re, you’re much more in a sort of physical settlement. You’re talking, you’re talking about your, your back fabric or what do call it? Uh, the backplane. Backplane. Yeah. Which obviously that takes a lot of hardcore engineering stuff. Yeah. Um, can you give a status update on like, where is SF Compute versus listeners who last heard you on a podcast two years ago? So, uh, when we started, we were this company called Junark and then we were just trying to sublease our cluster. 02:45 Um, and we started there in the technical stack. Um, and a way to think about it is it’s like you’re in a hotel and you’re trying to sell that hotel room. And then somebody asks you, Hey, like, are you going to clean my room? Um, and then you say, Oh, I guess I probably also need the hotel room as well. Or like I need the, um, the cleaning closet. And so you go get access to the cleaning closet and then they want room service. And so you get access to the kitchen and then, you know, they want, um, security. And so you get access to the security closet and you get this and you do that and you do this. And at a certain point you’re just running the whole hotel. Um, and the moment you’re running the whole hotel, you say, Oh, wait a second. 03:21 Um, instead my supplier on this market, um, is somebody that looks closer to a, fund, um, or a cloud that has access in our case, a cloud that has access to the data center. And we’re going to level up. And so what we’ve spent the last several years doing is building the entire operating capacity of a cloud, um, in order to offer, in order to actually deliver, uh, the compute at a reliable level. Um, we call that physical settlement. We’re taking a term from the futures market, from futures markets, from other markets, um, where in, you know, 03:57 oil or soybeans or something, physical settlement means you get delivered the oil and the soybeans. Yes. Um, but, um, in some markets, um, like in oil and also in power, um, there’s like a delivery network in oil. You have to have pipelines. If you would like the oil to be delivered to you, there has to be a way for it to get to you. Yes. Um, and finance, and for people interested in financial history during COVID oil went negative because, uh, so like I will pay you to take oil from me because it is sitting there in the port. Uh, and like, I can’t pay for this. 04:32 Yes. And so I will pay you to take my oil. Yes. That is like the weirdness of physical settlement. Yes. There’s a bunch of weirdnesses like this in other markets as well. So in the power markets and the grid, um, there’s parts of the orders that you fill out, the buy orders and sellers that, um, are there because the boiler takes a while to cool off. Um, and you can, you know, stop the boiler and then it will, you can start it back up again within some period of time. And so that has to get factored into the order book. Um, this genre thing is the same thing that happens in compute markets. So we co-designed physical settlement and the order book at the 05:10 same time. And that meant that we get to actually handle the real constraints of compute, which might include something like, oh, I’m okay with being interrupted, but only if there’s like storage on the thing that I can recover from really quickly. Um, very similar types of problems. Yeah. So, uh, people can refer to your talk and you’ve done a couple other podcasts already. Um, so I want to be like the sort of 201 level instead of the 101 level. Sure. Uh, and also because I, I am, I have been a derivative trader, so I can give people a bit more detail that you normally don’t get into. Um, so for example, well, how, how mature or deep or liquid is the compute 05:45 market today? Uh, not very mature or deep or liquid. Okay. so what we’ve been doing is first we had to, we had to go get a bunch of supply online in the first place, which just takes a while. And then we had to build the entire platform in order to do this. Um, and we just now got that working. And so now we move, um, capacity over onto the order book, um, which makes it liquid. Um, and so 5% of our total capacity is on the order book and we’re consistently moving more and more. And then we’re bringing on more capacity. That’s all on the order book. Um, the nice part is that, um, maybe I should have said the, the volume on the actual order book is small, but the, 06:21 um, the liquidity on the order book is high. So for that small amount of capacity that you have, um, you have, um, like very concentrated liquidity. So, um, a sort of standard metric, I would say for someone who is using this is a customer who buys capacity from us and they spend a hundred dollars to buy that capacity. We’ll make back $25 on average from selling it back to somebody else. Um, and that’s the number that we, or this is one of the metrics that we try to track because, we want the liquidity to be useful to the commercial participant and you can measure liquidity in a bunch of different ways. You can say like, I just want tons of makers or something. 06:56 Um, and you can measure it in like high-minded ways. We try to measure it by like, is this solving the problem, um, for the customer? Yeah. Got it. And so, uh, last time we talked as well, there was a lot of contract, uh, conversation about standardizing the contracts. Yes. talking with like, uh, the CFTC or whatever, what’s the status of those things? Um, so I think standardizing the contracts in general is still pretty far away. Um, and the, what we were trying to do at the time was, um, or basically what we learned over the last several years was we just needed to 07:31 operate the whole thing. Otherwise you cannot standardize the thing. And so to get closer to something that can be standardized, you first need to make all the operations of the thing the same. Right. To give an example of what can be different is a single operator can offer two different types of virtual machines. One type of virtual machine, um, might have a 40% performance, um, degradation versus another types of virtual machine. And 40% is a lot. When you say performance degradation, are you talking like MFU type things or like amount of burn in that you’ve experienced or what? It will affect. So in this particular case, I’m talking about GPU kernel launches and how long it takes for that to happen. 08:07 Um, which is a process that happens quite frequently when you’re doing sort of either training or inference. Um, the, um, uh, that will basically, the end result is that you get a worse MFU, but, um, like is the intermediate level. the intermediate level basically. Yeah. Yeah. So, so what I’m trying to, so for anyone who’s traded commodities before, there’s different grades of commodity. Yes. Right. There’s like, it’s not oil. It’s light sweet crude versus like whatever the opposite of light sweet crude is, or it’s like, you know, uh, corn level a or corn B or whatever. Uh, and so like people are trying to, well, you have to establish the equivalents of those things. Correct. Yes. I’m wondering if they have been established. 08:42 So what we did instead, um, was we created an order book that had multiple different constraints and you can say, I want to have, um, you know, something that’s between this and that. Um, it runs from this time to this time. Um, it’s like, uh, X to Y, it’s a bunch of different properties that you can attach to your order. And then we match that against sell orders that have those properties, um, in a big matching engine. Um, and the reason why we did it this way is it allows us to generate liquidity without having to standardize before, know, what the technology can do. Um, and because the technology was shifting so quickly, um, we wanted to basically be able to do that. The trade off of this is that you have less concentrated liquidity. 09:18 And so the prices are going to have, there’s going to be more spreads between the, the buy and the sell. Yeah. Um, it sucks. Yeah. But on the other hand, it means there’s lots of arbitrage to make, um, if you can do that correctly. So if you have a product that can use lots of different types of capacity, um, then you might really like SF compute because you will be able to use lots of different types of capacity and you’ll make more money. Um, you can take the spread. Okay. What dimensions matter in this grid of things? Um, so a bunch of different things. Um, one is dimensions categories that I will say is like, is there a 09:53 workload running on it right now or is it capacity in the future? Sure. is it capacity right now? Um, capacity right now in order for you to sell that, um, you have to interrupt your current workload. So you have to think, do I care if my current workload is interrupted or not? Capacity that’s sold that’s starting a month from now, um, you can buy and sell that all the time. And so you get, um, liquidity on that really easily. Um, there is some concern like, oh no, well I have capacity next month, but it’s very different than if you’re interrupting a workload right now. Um, the way that the interruption happens matters. So an example is if you’re on a cluster and the cluster doesn’t have as amazing storage as you want it to have. Um, so an example is like, do you have a Weco or Vast system under the hood? 10:31 Um, and do you offer that? And like, what’s the size of that? Um, this changes how you respond to, um, being preempted or removed off the cluster. meanwhile, the location of that storage matters. So, um, if you’re able to move that data from one cluster to another, um, quickly, then maybe you might be able to spread across multiple different clusters. Um, and you can be interrupted over here and then pop back up over there. Um, to make that work, you also need, like a, a fast transit path between one cluster and another. Typically this is implemented with a WAN, but most of these clusters don’t 11:06 necessarily have, um, like a wide area network. Yeah. A wide area network. So, basically cables that connect the clusters together. A private internet is another way to think about it. uh, a lot of clusters that are being spun up today are like, they’re not attached to, um, traditional CPU clusters were, next to the customer for latency reasons. In GPUs, they’re next to the power, um, wherever the, like, you know, you put the data center next to the power rather than next to the customer. Um, this means that a lot of stuff is getting put in places that previously they weren’t expecting data centers there. 11:41 And so you may not have, um, as, um, excellent of a transit path, um, from one to the other. So this, this starts to matter. Um, other examples are, um, weird shit where we theoretically support, but don’t actually, um, uh, like this is a thing that we’re thinking about, but haven’t done basically. Um, the early GB 200s broke. The early H 100s broke. Um, and so if you were buying H 100s or you’re buying GB 200s, you might really want the ones that were deployed after a certain time because you’re not getting the first shitty ones off the rack. 12:16 And so if you’re a super sophisticated customer, you may be willing to pay more, um, because for that. Oh, so it’s, it’s about like what cohort they were, they were produced and they get better over time. So like, uh, the first ones off the rack maybe had some faults or some problem. Right. And so if you were the ones who got the first ones, um, or, you first ones from, um, the, the first shipment of them, um, then yeah, they would, they would be broken or, um, in some way. I assume the sort of process is like, look, there’s defects everywhere. Like it’s, it just happens. Right. Totally. And so like you get it, you get a new shipment offer from TSMC or whatever, and you just kind of run your tests and like you, you figure out 12:53 which ones, which ones are bad and you throw them out. And isn’t that how it works? And I, I didn’t know about this element of cohorting where like, let’s say Q4, 2026, uh, B300s are way worse than 2027 B300s. Yeah. So, um, I’ll give an example. Um, you’re describing burn-in. Yes. so when you get a cluster, um, a good cloud, what it does. Is it tries to kill all the GPUs, um, before they ever get in the hands of the customer. Um, and then, um, the GPUs that are broken or the nodes that are broken, um, you get RMA’d. You basically go and request it. Yeah. You return it and you get a new one and there’s a lead time to that, but 13:30 whatever, you’ll get, you’ll get some back. Um, and then the customer never has to experience this. Um, the, um, thing I’m referring to is that, um, like in early H100s, there were like firmware bugs that may not even be related to the specific, like they may not be NVIDIA. They might be like Super Micro has a firmware bug in the, um, in the box that you bought. Um, and it applies to all of them and it doesn’t matter. Like, um, the solution to that was you need to get them to update their firmware. Um, or they need to ship a new thing, um, in every new box. 14:07 Yeah. had this for like the, the B300s that, that we got. Um, and the IQ had to use Devin to go fix it. Yes. Great. So like different types of problems like this can happen. Interesting. Okay. So I’m like, okay, look, that seems small compared to the overall. Like, I just want to, like, I want people to understand that the shape of this market, um, you mentioned like preemption, you know, and, you we talked about spot instances and how, how that, uh, the analogy is. Yeah. And then there’s also, you know, booking a month out. How far out do this, do these markets go? And is, is real time or sort of preemption more interesting to you versus, 14:42 far being further, further out? Um, to answer your first question, three to five years. Um, wow. We’re starting to see. month by month or quarter by quarter or how’s this? Are you asking how the cashflow works? Like the, the contracts, like the, the futures contracts, right? Like again, like I’m a former contracts, uh, derivatives trader. In the, in the market itself, um, like is in the broader market. Yeah. clusters are financed three to five years. Okay. Um, so the, the, the payments are totally over that period of time. Yeah. there is also like blocks of capacity in our order book, um, which can extend any arbitrary time. Yeah. there’s some stuff that we have to do, um, for, um, the cashflow on that 15:18 right now. What we do is we just ask people to pay entirely upfront for a block. Um, we don’t ask them to do that for a lease for a longer term contract. This gets into something that I definitely need a whiteboard to explain. Um, but, um, you had another question I was about to answer. I forgot. do you want real time or do you want, like, do you want more, uh, what’s more interesting to you? Real time or long, longer out? So we care more about longer outs because we’re trying to build a forwards curve that has a lot of liquidity on it. Um, the, um, these are kind of the same thing and kind of not. Um, so what we do is we, since 15:54 a, you can buy an hour out, you can buy right now to the next hour or you can buy right now to the next three years and anywhere in between. That’s wild. I know. Um, and so the, um, you know, or if I, you’re technically, um, the, there’s a certain difference that happens that’s dependent for each customer. Basically like if I have a workload that is, um, uh, that’s batch inference basically where I can be interrupted at any point. I don’t really care. Um, then my ability to be 16:29 cut off, um, is probably like, I need an hour of time to know, um, that I’m going to be cut off and then I’m good. Or I can even be notified in, you know, a minute or two. Um, but if I have, um, live inference customers who are expecting me to be online, um, then you can interrupt me, but in a month, maybe two months, maybe three months. Um, and then I can adapt to that capacity over that period of time. Um, but you cannot interrupt me in a minute. Um, and so those two things are priced differently. And the reason why they’re priced differently is because what is the price 17:05 you would pay in order to interrupt your batch job? It’s very different than the price you would pay to interrupt your, um, live job. If you have a live job, you’re, you are probably paying an SLA when you break for your customer. Um, does that make sense? Yeah, I see. So this is actually the market for breaking leases. These are the market for breaking leases. Oh my God. And then there’s also the market for future leases or like for leases that are, um. Oh, I thought this was like a subletting situation. This is like a lease break. So one way to think about it, um, is in power markets, there’s actually two markets. Um, there’s like the day ahead market and 17:40 there’s the live market. Compute kind of has the same thing, um, where it has capacity that is coming online, um, or capacity that’s about to, um, arrive to you. Um, and then capacity that is currently in use. And these have different price dynamics. Okay. Interesting. backwardation or contango? Backwardation or contango? Yeah. so do, uh, is compute further out price higher than spot or lower than spot? Um, is compute further out price? Does it, does it go like this? Does it go like that over, uh, you know, over like one year, one year out, two year out? 18:16 Do. Oh, this is an, uh, both at different times. I find it fascinating that you don’t know. Yeah. I mean, both. It depends on who you are. Um, okay. So, uh, I’ll make the case. Um, wait, so you’re asking is compute sold right now? same unit of compute today. Yes. Uh, maybe not today, maybe a month out versus one year out versus two year out. Is that, is that curve going up or going down? We see, like a hump. Oh my God. okay. So explain, um, like, I think this will make intuitive sense is basically like if I have milk and it is about to expire, I will lower the price on it. Yes. Um, and so that’s basically what we see. 18:52 It’s like, it’s the Nvidia release cycle basically, right? Uh, well, we’re more saying like if there’s a block of compute and it’s approaching now, um, it can’t be sold in the past. And so it has to drop its price in order to clear at some price. Yeah. And so initially you see this like drop where, um, stuff that’s sold right now, anything that’s on the market right now, um, is something that hasn’t been sold. And so therefore the price will drop. Um, and then it goes up, um, to where it wants to be. Um, and then maybe compute in the future is worth less, um, because people don’t want, like people would like the compute now. And so they’re sort of paying for the shorter lead time. Um, and they pay less for the compute that’s off into the future. 19:28 This is what I think a normal healthy curve looks like. Yeah. The place where I’m like, oh man, no shit. Um, is that, um, right now if you took a B300 and you trying to run inference on it, um, you can make $40 an hour or so, um, selling that as inference. Any model basically. Um, and so that’s if you use it at a hundred percent and maybe if you only have like 50% utilization or something, so like $25 an hour. Yeah. So $25 an hour. Meanwhile, you can buy B300s at like five to $6 an hour. One of these numbers has to change. Yeah. It’s a huge arbitrage. It’s huge arbitrage. this in your talks a lot. 20:04 Yeah. So, um, a bunch of people are basically looking at this and thinking, um, if I think prices are going to go up, I might be willing to pay more than what I previously thought in the future. Um, because I might think, okay, I can buy a five right now, but if I can’t buy anymore, then maybe I will be willing to buy even more in the future just to lock it in if it’s going to come online later. So we see the other side as well. Um, kind of depends on who you are. Cool. So, is it okay. And, and I just, I just really want to make super clear on this. Of course. Do you care about the different generations of chips and, or do you just care about overall flops and you spread it among whatever chips you want? 20:40 Uh, we care about different generations of chips because. you care. I think like, I only, I guess do your customers care? Yes. Right. Oh, wait. When you say a different generation chips, do you mean like. So like B300s, B300s. exactly. Yeah. Or like even AMD or whatever. Right. Uh, yes. I think people totally care about the generation of chips. Um, uh, I think part of this is because of things like VRAM. Um, and part of this is because of like, um, larger NBLink domains. Yeah. And things like that. So there’s just no way to abstract all this to like some generalized unit of compute that you can sort of mush around and whatever you like. I think like when you were saying oil, even oil isn’t oil. Oil is actually just like a bunch of different types of oil. 21:16 Yeah. Like jet fuel or car fuel, whatever. I think it’s the same thing. Um, you’re going to get a bunch of different types. It is, but you have oil refineries. Yeah. can effectively have black boxes. You can put oil in and get like whatever out. Um, if compute, it is so heterogeneous, I guess is the word. It’s that you just have to expose the underlying complexity to your end customer, which is bad for you as a market maker. Maybe. Like you want to identify the one like choke point or like, I don’t know what the better word for choke point is like a shelling point where everyone agrees like, okay, like this is kind of the standard and we’ll, we’ll all agree to this standard and, uh, you know, it’ll 21:53 be somewhat inefficient. Yes. Uh, so we’ve been able to reduce the, um, the, the problems to a certain level, but yeah. Like your job is like dimensionality reduction. Yes. We reduce a lot of the dimensions, but nonetheless, there are still dimensions. Yeah. It’s still going to exist. Do you, do you know how many SKUs you have? So, um, like five times a hundred times like 5,000 equals, I don’t know. Like what’s, we actually made SKUs like a concept, um, that, and the goal is to make it so that somebody else can make their own SKU. So if I want to buy Blackwells, I can say I’m willing to 22:31 buy Blackwells at $6 an hour or less. And you can match me against GB300s or you can match me against B300s and I don’t care which Blackwell as long as it’s less than $6 an hour. but you just said you care about memory and other stuff. Totally. But not every customer does. So some customers like, um, just can work on Blackwells, whatever it is. Some customers are super hyper specific and they need this exact thing and they’re going to pay more for that because the market’s going to charge them more. Okay. Um, and that’s what we do. So, and so people use your APIs to programmatically define what they want and then you match that with whatever composition of sellers, uh, they might, they might, they might be able to fulfill. 23:08 Yes. Um, with a caveat that having a named query is a new thing we’re doing. Um, but the query system as a concept is how the order book works right now. Okay. Interesting. Um, all right. So, uh, you know, I, I think that’s like a lot of like, I guess, nitty gritty. Are there anything about like, I guess that you want to educate people on whether it’s financial people or engineers about the state of the compute trading market as, as you’ve developed it? Because effectively you guys are defining it. Um, mainly just the requirement for physical settlement. Yeah. Um, we think that if you, we, we spent three years learning why you need 23:44 physical settlement. Um, and, um, our entire conversation today is, um, that it is, these things are not as fungible a
This was recorded on the sidelines of the new Compute Trading track at AIE NYC. Catch us next at AIE CODE, our last flagship for the top agentic engineers in the world. In the early days of cloud, former guest Martin Casado made something so disruptive that Cisco was threatened enough to pay $600m for it, and VMware outbid for $1.3B: That concept was software-defined networking, which virtualized the routing network by separating the control plane from the data plane. That revolution is now coming for GPUs - where previously running your own supercompute cluster required $50-100m upfront to get commitments and do the entire stack from hardware to firmware to kernels, SF Compute is now enabling you to not only buy or sell GPU hours now and in the future, but also to programmatically control everything from your network to memory. We last spoke with Evan about commoditizing compute. At AI Engineer New York, we went deeper: what does it take to physically deliver a GPU contract, and what has to be true before a market in compute can become liquid? From selling a room to running the hotel Evan describes SF Compute’s expansion with a hotel analogy. A customer wants a room, then asks for cleaning, room service, and security. Eventually you discover that delivering the room means operating the hotel. SF Compute began by trying to sublease a cluster; the company has since built much of the operating capacity of a cloud to deliver the compute traded on its market. That work is physical settlement: the buyer gets usable compute, rather than simply a cash payment against an index. It brings the physical constraints of a cluster into the market itself. Can a workload be interrupted? How quickly can it recover from storage? Which GPU generation, networking and software configuration does it need? Those details affect both the contract and the price. Making compute tradeable Rather than waiting for every GPU cluster to become interchangeable, SF Compute matches orders with different constraints. Evan argues that this makes trading possible while the technology changes, at the cost of spreading liquidity across more variants. We also discuss spot and forward pricing, batch versus live inference, and why an index can become disconnected from the cost of actually buying compute. Independence is central to Evan’s case for a market operator. He argues that companies with large hardware or inference businesses have incentives that make it harder to run a neutral exchange. We also get into the unglamorous engineering: visiting sites, designing clusters, running the backplane, and making infrastructure programmable. Near the end, he describes the ability to subdivide InfiniBand fabric on the fly as one example. Guest: Evan Conrad, CEO of SF Compute. Show notes - Evan’s AI Engineer NYC talk: How Compute Markets Work - Our previous SF Compute conversation - Why oil futures went negative in 2020 — EIA - How SF Compute customers resell unused GPU capacity - SF Compute’s $245M compute sale - Abel: SF Compute’s settlement ledger - CFTC: Barclays and LIBOR manipulation - Elastic InfiniBand partitions Timestamps - 0:00 — Why can’t NVIDIA build this? - 0:25 — Welcome back to Latent Space - 2:23 — From selling a room to running the hotel - 3:47 — Physical settlement: actually delivering compute - 6:00 — Liquidity and the GPU order book - 8:42 — Trading nonstandard compute - 10:00 — Interrupting and moving workloads - 12:20 — Why hardware cohorts matter - 14:32 — How far out do compute contracts go? - 16:19 — Batch versus live inference leases - 17:58 — The shape of compute prices - 20:33 — GPU generations, specifications and SKUs - 23:30 — Physical versus cash settlement - 27:00 — The emerging compute market ecosystem - 29:03 — What it takes to deliver a GPU cluster - 32:24 — Sovereign compute - 33:26 — Why NVIDIA can’t build this market - 34:48 — A programmable data center Transcript Automated transcript, lightly formatted; speaker labels have not been verified. 00:00 NVIDIA is obviously like the 800-pound gorilla in the room. Why didn’t they make SF Compute? So our impression is that NVIDIA can’t make it, but benefits tremendously from it existing. The reason why they can’t make it is because they would then not be independent enough for people to trade on it. 00:25 Anyway, Evan, welcome back to Latent Space. Thank you so Welcome back to AIE. Actually, first time at AIE. I think so, yeah. This is a very special one because I feel like you have been working on compute trading, compute markets for a while. Now it’s like feeling of the moment. Yeah. Jump basically announced that they are getting into compute markets, which is why I’m excited to sort of cluster people. You met Oliver from SemiAnalysis, who is basically initiating coverage on compute markets is my read on this. So people are going to watch your talk. 01:00 I think it’s a very good explanation of both your origin story and why you need to exist and all the hard problems you work on. Yeah. Done in a very simple, engaging manner. I’m very jealous of your writing skills because you showed the flyer that you showed at New Reps. Yeah. I was like, it’s pretty straightforward and simple and it’s good. Yeah. So anyway, congrats. Thank you. I appreciate that. And also congrats on AIE. It seems to, y’all are rolling. We’re rolling. I just came back from the site tour for next year’s AIE. then for You have like a hint? At Javits Center. It’s the same place they do Comic Con. Okay. Wow. 01:35 Wow. Wow. Damn. But I think the finance focus is really hitting for folks. Yeah. So we’re going to try to split out. Like this is, here we just lump everything in finance. Like your finance, their finance, like insurance is finance, but like they’re all super different. Yeah. So we’ll probably break out things more minutely in future because people want to go deep on hedge fund stuff or banking or personal wealth. Yeah. Especially in New York. Yeah. Anthropic was like, yeah, we’re all about RIAs right now. Yeah. RIAs like registered investment advisors. Yes. Like so different from you. 02:10 No, no, no. I’m aware of this. Yeah. Yeah. Okay. So for listeners who don’t know, we did a podcast two years ago where you talked about your, you know, where SF Compute was. Um, today you’re, you’re much more in a sort of physical settlement. You’re talking, you’re talking about your, your back fabric or what do call it? Uh, the backplane. Backplane. Yeah. Which obviously that takes a lot of hardcore engineering stuff. Yeah. Um, can you give a status update on like, where is SF Compute versus listeners who last heard you on a podcast two years ago? So, uh, when we started, we were this company called Junark and then we were just trying to sublease our cluster. 02:45 Um, and we started there in the technical stack. Um, and a way to think about it is it’s like you’re in a hotel and you’re trying to sell that hotel room. And then somebody asks you, Hey, like, are you going to clean my room? Um, and then you say, Oh, I guess I probably also need the hotel room as well. Or like I need the, um, the cleaning closet. And so you go get access to the cleaning closet and then they want room service. And so you get access to the kitchen and then, you know, they want, um, security. And so you get access to the security closet and you get this and you do that and you do this. And at a certain point you’re just running the whole hotel. Um, and the moment you’re running the whole hotel, you say, Oh, wait a second. 03:21 Um, instead my supplier on this market, um, is somebody that looks closer to a, fund, um, or a cloud that has access in our case, a cloud that has access to the data center. And we’re going to level up. And so what we’ve spent the last several years doing is building the entire operating capacity of a cloud, um, in order to offer, in order to actually deliver, uh, the compute at a reliable level. Um, we call that physical settlement. We’re taking a term from the futures market, from futures markets, from other markets, um, where in, you know, 03:57 oil or soybeans or something, physical settlement means you get delivered the oil and the soybeans. Yes. Um, but, um, in some markets, um, like in oil and also in power, um, there’s like a delivery network in oil. You have to have pipelines. If you would like the oil to be delivered to you, there has to be a way for it to get to you. Yes. Um, and finance, and for people interested in financial history during COVID oil went negative because, uh, so like I will pay you to take oil from me because it is sitting there in the port. Uh, and like, I can’t pay for this. 04:32 Yes. And so I will pay you to take my oil. Yes. That is like the weirdness of physical settlement. Yes. There’s a bunch of weirdnesses like this in other markets as well. So in the power markets and the grid, um, there’s parts of the orders that you fill out, the buy orders and sellers that, um, are there because the boiler takes a while to cool off. Um, and you can, you know, stop the boiler and then it will, you can start it back up again within some period of time. And so that has to get factored into the order book. Um, this genre thing is the same thing that happens in compute markets. So we co-designed physical settlement and the order book at the 05:10 same time. And that meant that we get to actually handle the real constraints of compute, which might include something like, oh, I’m okay with being interrupted, but only if there’s like storage on the thing that I can recover from really quickly. Um, very similar types of problems. Yeah. So, uh, people can refer to your talk and you’ve done a couple other podcasts already. Um, so I want to be like the sort of 201 level instead of the 101 level. Sure. Uh, and also because I, I am, I have been a derivative trader, so I can give people a bit more detail that you normally don’t get into. Um, so for example, well, how, how mature or deep or liquid is the compute 05:45 market today? Uh, not very mature or deep or liquid. Okay. so what we’ve been doing is first we had to, we had to go get a bunch of supply online in the first place, which just takes a while. And then we had to build the entire platform in order to do this. Um, and we just now got that working. And so now we move, um, capacity over onto the order book, um, which makes it liquid. Um, and so 5% of our total capacity is on the order book and we’re consistently moving more and more. And then we’re bringing on more capacity. That’s all on the order book. Um, the nice part is that, um, maybe I should have said the, the volume on the actual order book is small, but the, 06:21 um, the liquidity on the order book is high. So for that small amount of capacity that you have, um, you have, um, like very concentrated liquidity. So, um, a sort of standard metric, I would say for someone who is using this is a customer who buys capacity from us and they spend a hundred dollars to buy that capacity. We’ll make back $25 on average from selling it back to somebody else. Um, and that’s the number that we, or this is one of the metrics that we try to track because, we want the liquidity to be useful to the commercial participant and you can measure liquidity in a bunch of different ways. You can say like, I just want tons of makers or something. 06:56 Um, and you can measure it in like high-minded ways. We try to measure it by like, is this solving the problem, um, for the customer? Yeah. Got it. And so, uh, last time we talked as well, there was a lot of contract, uh, conversation about standardizing the contracts. Yes. talking with like, uh, the CFTC or whatever, what’s the status of those things? Um, so I think standardizing the contracts in general is still pretty far away. Um, and the, what we were trying to do at the time was, um, or basically what we learned over the last several years was we just needed to 07:31 operate the whole thing. Otherwise you cannot stand
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