The Programmable Data Center — Evan Conrad, SF Compute

[00:00:00] Nvidia's obviously like the 800-pound gorilla in the room. Why don't they Why didn't they make a supercomputer? >> 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. >> Anyway, Evan, welcome back to A I space. Welcome back to A I E. Actually, first time at A I E? >> I think so, yeah. >> This is very special one because I feel like you have been working on computer trading computer markets for a while. Now it's like feeling of the moment. >> Yeah. >> Jump basically announced that they are getting into computer markets, which is why I'm excited to sort of cluster people. You met Oliver from Semi Analysis, who is basically initiating coverage on computer computer markets. This is This is my read on this. So people are going to watch your [00:01:00] >> talk. >> I think it's very good explanation of like both your origin story and why you need to exist and what the hard problems you work on. >> Yeah. >> Done in a very simple engaging manner. I'm very jealous of your writing skills cuz you showed the flyer for that you showed at NeurIPS. Yeah. It was like it's pretty straightforward and like simple and like it's it's good. >> Yeah. >> So anyway, congrats. >> Thank you. I appreciate that. And also congrats on A I E. It's It seems to Y'all are rolling. >> We're rolling. I just came back from the site tour for next year's A I E. And then for >> Give me like a hint. >> At Javits Center. It's the same place they do Comic-Con. >> Okay, wow. >> Which >> Wow. Wow. Damn. >> But I think the finance focus is really hitting for folks. >> Yeah. >> So we're going to try to split up 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 in future because people want to go deep on hedge fund stuff or banking or personal wealth or [00:02:00] >> Yeah, especially in in New York. >> Yeah, like Entropic was like, yeah, we're we're all about RIAs right now. RIAs like registered investment advisors. >> Yes. >> Like so different from you. >> [laughter] >> Yeah, no, no, I'm uh I'm aware of this. >> Yeah, yeah, yeah. Um okay, so for listeners who don't know, uh we did a podcast 2 years ago where you talked about your your uh you know, where where SF Compute was. >> Mhm. >> Um today you're you're much more in this sort of physical settlements. You're talk you talk about your your back fabric or what do you call it? >> Uh the backplane. >> Backplane, which uh obviously like like takes a lot of hardcore engineering stuff. Um can you give a status update on like where is SF Compute versus listeners who last heard you on a podcast 2 years ago? >> So when we started, we were this company called Junnar, and then we were just trying to sublease our cluster. 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?" And then you say, "Oh, I guess I probably also need the hotel [00:03:01] 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. 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 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 um the compute at a reliable level. Um we call that physical settlement. We're taking a term from >> the futures market futures markets, from other markets. Um where in, you know, oil or soybeans or something, physical settlement means you get delivered the [00:04:01] 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 and 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 the port and like I can't pay for this. >> Yes. >> And so I will pay you to take my oil. >> Yes. Um >> That 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 buyers 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 [00:05:01] get factored into the order book. Um this Jenner thing is the same thing that happens in computer markets. So we co-designed physical settlement and the order book at the 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 short uh and also because I I am I have been a derivatives trader so I can give people a bit more detail that you normally don't get into. >> Okay. >> Um so for example, well, how how mature or deep or liquid is the computer market today? >> Uh not very mature or deep or liquid. Um 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 [00:06:01] 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 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 $100 to buy that capacity, will 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 um 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, "Oh, I just want tons of makers or something." Um and you can measure it in like high-minded ways. We try to measure it by like, "Is this solving the problem um [00:07:02] 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. Um talking with like uh the CFTC or whatever, like what's the status on 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 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 go be different is a single operator can offer two different types of virtual machines. One type of virtual machine might have a 40% performance 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 burning that you've experienced or what? >> It will affect So, in this particular [00:08:03] case, I'm talking about GPU kernel launches and how long it takes for that to happen, which is a process that happens quite frequently when you're doing sort of either training or inference. >> Okay. >> Um the um uh that will basically the end result is that you get a worse MFU, but um like >> Is the intermediate level >> 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 exactly 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 >> equivalents of those things. >> Right, yes. And I'm wondering if they they have been established. >> 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 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 [00:09:04] is it allows us to generate liquidity without having to standardize before you 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, and so the prices are um going to have There's going to be more spreads between the the buy and the sell. >> Yeah. >> Um >> It 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 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 workload running on it right now or is it capacity in the future? So 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 [00:10:04] 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 will 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 Weka or Vast system under the hood? Um and do you offer that and like what's the size of that? Um uh this changes how you respond to um being preempted or removed off the cluster. Um 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 um like a a fast transit path between one cluster and another. Typically this is implemented with a WAN but [00:11:05] >> most of these clusters don't necessarily have >> um >> a wide area network. >> Yeah a wide area network. So um basically cables that connect the clusters together. A private internet is another way to think about it. Um uh lot of clusters that are being spun up today are like they're not attached to um traditional CPU clusters were um 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. 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 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 GB200s broke. The early H100s broke. Um, and [00:12:07] so, if you were buying H100s or you were buying GB200s, you might really want the ones that were deployed after a certain time because you're not getting the first GD1s off the rack. 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 know, 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 that sort of process is like, look, there's the defects everywhere. Like, it's it just happens, right? >> Totally. >> And so, like, you get it you get a new shipment off of from TSMC whatever and you just kind of run your tests and like, you you figure out which ones which ones are bad and you throw them out. And isn't that how it works? And I 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. [00:13:07] >> Yeah, so [laughter] um, I'll I'll give an example. Um, you're describing burn-in. Yes. Um, 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 RMAd. You basically go and replace them. Yeah. You return it and you get a new one and there's a lead time to that, but whatever, you'll get >> Yeah. >> 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 Supermicro has a firmware bug in the um in the box that you bought. Um and it applies to all of them. And it [laughter] 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. [00:14:07] >> Yeah. Or Cornelis had this for like the the B200s that that that we got. Um and then like you had to use DevCloud to go fix it. >> [laughter] >> Yes, correct. So like different types of problems like this can happen. >> Interesting. Okay. So I'm okay, 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 and you know we talked about spot instances and how how that the analogy is. And then there's also, you know, booking a month out. How far out do does do these markets go and is is real-time or sort of preemption more interesting to you versus uh far being further further out? >> Um to answer your first question, um 3 to 5 years. Um >> Wow. >> We're starting to see >> month by month or quarter by quarter or how's this? >> Are you asking what how the cash flow works? >> The the the the contracts. Like the few the futures contracts, right? Again like I'm a former contracts derivatives trader. >> In the in the market itself >> Yeah. >> um like is in the broader market. >> Yeah. >> Um clusters are financed 3 to 5 years. >> Okay. >> Um so the the >> The payments >> are over that period of time. [00:15:07] >> Yeah. >> Um there's also like blocks of capacity is in our order book um which can extend any arbitrary time. >> Yeah. >> Um there's some stuff that we have to do um for um the cash flow on that. Right now what we do is we just ask people to pay entirely up front 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. >> [laughter] >> Um, but, um, you had another question I was about to answer, I forgot what it >> Uh, 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 out 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 say you can buy an hour out, you can buy right now to the next hour. Or you can buy right now to the next 3 years and anywhere in between. >> That's wild. >> I know. Um, and so, the, um, you know, or finer technically. Um, [00:16:08] 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 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 2 months, maybe 3 months. Um, and then I can adapt to that um, 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 you would pay, um, in order to [00:17:08] 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, it's This is actually the market for breaking leases. >> These are the market for breaking leases, basically. >> 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. It's This is like Oh, the lease break. >> So, one way to think about it is in power markets, there's actually two markets. Um, there's like the day-ahead market, and 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. Um, backwardation or contango? >> [laughter] >> Backwardation or contango? >> Do uh, so do uh, is compute further out priced higher than spot, or lower than spot? >> Um, is compute further out priced Does [00:18:10] it Does it go like this, or does it go like that over, uh, you know, it over, like, 1-year 1-year out 2-year out? >> Do >> Oh, this is an ex- Both at different >> I I I It's I find it fascinating that you don't know. >> Yeah, I mean, both it depends on who you are. Um, so, uh, I'll make the case Um, wait, so you're asking is compute sold right now >> unit of compute today, uh, maybe not today, maybe a month out versus 1-year out versus 2-year out. Is that Is that curve going up or going down? >> We see a like a hump. >> Oh my god. >> Um, okay, so >> Explain. >> Um, like I think this will make intuitive sense. It's 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. >> So, it's it's the end of video of the lease 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. And so, it 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 [00:19:11] sold, and so therefore the price will drop. >> Mhm. >> Um and then it goes up um to where it wants to be. >> Mhm. >> 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 in the future. 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 tried to run an inference on it, um you can make $40 an hour or so um selling that >> Any model? >> Any model, basically. Um >> [laughter] >> and so that's if you use it at 100%, 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 $5 to $6 an hour. One of these numbers has to change. >> Yeah. It's a huge overcharge. >> It's a huge overcharge. >> You mentioned this in your your talks a lot. >> 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 [00:20:11] 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 any more, 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, um 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 them among whatever chips you want? >> Uh we care about different generations of chips because >> You care. I think like I I only I guess do your customers care? >> Yes. >> Right. Oh, wait. When you say uh different generation chips, do you mean like >> So, like B300 generation. Yeah, 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 uh larger NVLink 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. [00:21:11] >> I think like you when you were saying oil, even oil isn't oil. Oil is actually just like a bunch of different types of oil. >> Yeah, like jet fuel or car fuel or whatever. >> I think it's the same thing. Um you're going to get a bunch of different types of >> It is, but you have oil refineries that you can effectively a black boxes you can put oil in and get it like whatever out. If compute it is so heterogeneous, I guess it's the it's the word is that you just have to expose the underlying complexity to your end customer, which is bad for you as a market maker. >> Maybe. [laughter] >> 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 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 you know it'll be somewhat inefficient. >> Yes. So we've been able to reduce the the the problems to a certain level. >> Yeah, it's like your job is like dimensional reduction or all that. >> We reduce a lot of the dimensions but nonetheless there are still dimensions. They can still going to exist. >> Do you do you know how many SKUs you have? [00:22:11] >> [laughter] >> So Um >> Like 5 * 100 * like 5,000 that equals I don't know. Like what's >> [laughter] >> We actually made SKUs like a a concept that and the goal is to make it so that somebody else can make their own SKU. So if I want to buy black wells, I can say I'm willing to buy black wells at $6 an hour or less. >> Uh-huh. >> And you can match me against GB300s or you can match me against B300s and I don't care which black well as long as it's less than $6 an hour. Um >> But you just said you care about memory and other stuff. >> Totally, but not every customer does. So some customers like just can work on black wells 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 if match that with whatever composition of sellers uh they might they might they might be able to fulfill. >> Yes. Um with the caveat that having a [00:23:11] 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. Have you have you Is 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're you guys are defining it. >> Um mainly just the requirement for physical settlement. Um we think that if you we we spent 3 years learning why you need physical settlement. >> [laughter] >> And um our entire conversation today is um that it is these things are not as fungible as you think. We're going to try and make them fungible. That's the industry's job to do that. Um but without that you run a lot of risks. Um if you try to build um too much financialization before you've the industry has had time to build out the um the physical settlement, then I think what you have is you have um [00:24:14] a market that's very easy to manipulate and a market that has a lot of basis risk where um the price you may be hedging against actually has nothing to do with the price that um the market >> Yeah. >> Yeah. Yeah. You can actually buy the capacity for. >> Yeah. So for for listeners uh we we talked about this briefly last time, but um people are often shocked to find out that let's say the market for credit default swaps is 12 times the the size of the bonds that they insure. Mhm. Uh something like that or like uh the the markets for mortgage um sort of reinsurance or uh all that it is is larger than the underlying physical settlement. So that's why physical versus cash settled. >> Yeah. And and like basically cash settlements are like exactly the kind of uh the side bet that you see in The Big Short >> Mhm. >> um, where, uh, they they may, uh, may or may not have anything to do with the underlying thing itself and it may may be used as a proxy for overall other stuff. That and you don't want it to that to go out of control. >> Yes. Yeah. So, to be clear, we think, um, cash settlement as a future concept is a great idea. Um, we just think it [00:25:15] should be derived from physical settlement. Um, because, as you said, it's a side bet, um, and it would be kind of like if you I mean be kind of like if you made a cash settlement future before you made physical settlement. >> Um it which I think people are kind of doing already. >> Not yet, totally. People are trying to make cash settlement before you make the physical settlement. >> do that for all sorts of things, including startup stock. >> Yes. >> Right? Like you can buy and sell a sh- a made-up share opening eye, but it's nothing to do with the actual share. >> Yes. >> [laughter] >> Um, and lots of those go very poorly. Um, like it would be kind of bad if you made a, um, it would be bad if you started selling, um, the the the derivative on a stock that doesn't exist yet. Um >> Yeah. Yeah. >> Well, so, I mean, one thing that we do, uh, so I used to be a currency trader, right? And and interest rates as well. And what one thing we do is you have a board that gets together and says like this is the London Interbank Offered Rate, LIBOR, and we all agree like this is the the the monthly fixing and we all trade to that fixing. >> Yes. >> Uh, [00:26:15] is that even meaningful at some point in 3 years from now? >> So, critically, LIBOR was manipulated. >> Yes. >> Um, our concern at the moment is >> 12 banks. Yeah. >> Yes. >> Um >> Well, our concern is there's a very similar problem, um, where you can manipulate an index. Um, our impression is why you can manipulate that index is because a lot of the indexes are based on a very small amount of data that has um uh, not a lot of correlation with the actual price of buying a new reserve contract. Um that's what we're concerned about. >> [laughter] >> Okay. Interesting. Um, all right. I I think I think that's the the the rough state of things. Uh-huh. Uh, um, I think the other part of just just today is you you you talked with Oliver, and you're doing an event with Jump, and you're friends with Silicon Data. What are you learning from all these other players? Like who are the major players that you would want to call out as like doing interesting things? >> Ooh. Um Uh so, we're big fans of Silicon Data and Computer Change. I think they were also very early to this, and I think [00:27:16] they are doing like a reasonable shot at the A path that we think that we'll likely end up being partners with. >> And can you distinguish like your your different paths? >> So, the general gist is they're doing cash settlement, we're doing physical settlement. We think both can exist and should exist, and that they're sort of mutually beneficial to each other. And so, totally fine. So, I think they are one of the earliest in the space, and I think they are very clearly took the bet well before anybody else did, and I think that's like very honorable to do. Um Um you mentioned John >> Seminalysis says Jump. Like are these other players emerging like as far like you were my first introduction to this, and then I and I see all these other guys coming in, and I'm like, "Okay, like it's something's happening here." I don't know the landscape as clearly as you do. >> Yeah, so the categories of folks that we've seen is there's folks that are trying to set better standards. So, Seminalysis I think is working to try and create better standards. There's folks that are trying to do residual value insurance and so on. There's a number of different companies [00:28:16] in the space. I don't know that any of them super well, but I know know this is a concept, and I think that's important. There are folks that are trying to do a deposit lending where like they'll front your credit risk for the cloud. There's an ecosystem of companies that are forming around how do we resolve the financial bottlenecks of compute, and it is cool to see. >> I think effectively what you want in this ecosystem is buyers and sellers, you want an analyst, you want we said insurance or what what's the >> Uh residual value insurance. >> Residual Um, and you we want this like healthy ecosystem out there. >> Mhm. >> Um, but you obviously you guys are owning the problem of physical settlement, which which is really interesting. Um, Okay, so I mean, we can go into like the the hard tech side because obviously you are also hiring >> Yeah. >> uh, for extremely cracked engineers. Uh, describe your stack. >> Um, we do all the things the cloud does without being the cloud. Um, as in we don't own the hardware. Um, the reason why we don't own the hardware is because if we did, we would be able to [00:29:17] manipulate the forwards market. So, we have to be Switzerland, we have to be independent, um, which creates a lot of sort of strange things about the company, um, but in order to do physical settlement, you have to do site visits. You have to visit the data center. Um, you have to um, you have to design the bill of materials and ensure that um, you have to basically do like supply chain consulting, um, where you have relationships with different OEMs that >> Um, >> ensure that things get delivered on time. Um, then you have to um, do cluster architecture, um, uh, so you're sort of designing the cluster itself. Um, then you're um, rolling out racks or you're working with partners to roll out racks and then supervising that. Um, then you're doing uh, backplane management, so you're taking over the um, PDUs and the switches and the DPUs and um, UFM and BMC and to your operating um, harness around the nodes that you're settling. >> you as an SF Compute employees? >> Yeah. >> Wow. >> All right. Um, so uh, [00:30:18] when you buy a node from a cloud, um, there's the bare metal, but there's all this around the bare metal. Um, and in order to make that bare metal node work, you have to operate everything else. Then we >> Because I just like traditionally the job of the data center is to extract some some of this away from you. >> Yeah. We're we're the ones that do that. >> Yeah, yeah. >> Okay, [laughter] all right. Um, but you're not you're not in the data center business. You're not the I sort of Equinixes of the world. >> We're we're not in the data center business, sort of. Um, what we do work with is >> So, we offer supercomputing as a service. Um, and what this means is folks come to us and say, you know, I've got X, where X is I've got, you know, X megawatts here, and maybe sometimes they mean, I've got a data center um that's like I would like to turn into a GPU cluster. Like my options are I could sell the GPU cluster or I could sell the data center to somebody or I could become the cloud. And then we go to them and say, great, um we will turn you into the cloud, [00:31:18] we're the supercomputing part, you're the data center cloud. Let's partner together and then we will sell and all make money. Um sometimes it's people who have something that could turn into a data center, in which case we're going to basically project manage that up to the layer, um and then we will work with those far we'll work with those folks to turn their powered land um or whatever they have um into a data center by working with partners, um and then we take over at the supercomputing part. This requires us to have some sort of data center expertise in-house, but we don't self-perform builds. Um, so yeah. >> Yeah, so it's somewhat modular because you have different scenarios you need to deploy under. >> Yeah, someone once described us as like um uh like Palantir for compute um [laughter] because it's like these each time it's this new rodeo um uh but and we like we we deeply partner with folks on both sides. >> Yeah. Uh and primarily the US? [00:32:18] >> Primarily the US, but we operate around the world. >> Yeah. At some point does SF Compute become a misnomer? >> Uh so, when >> [laughter] >> so, we're deploying clusters all around the world, and when we do that, we're working with people who themselves are trying to be clouds all around the world. Um they look like sovereign compute players. Um and our pitch to sovereign compute is amazing. Let's help you set up a cloud in your own country and we will bring the San Francisco off-take. We'll bring the San Francisco expertise. We'll set up the clusters. That's our whole business. >> Ah. >> We're going to make it financeable. >> who are your main your biggest announced partners that you can name? >> Um Nobody I can name basically. We will have some announcements in a week or two that we expect to come on. >> Yeah. >> All things sort of uh >> Sure, sure. Good luck. Yeah. No, I mean I I think this is really interesting for every country to like who's who's like investing in sovereign AI to figure this out and it's interesting that like SF is is still sort of like that brand name and [00:33:19] that that is that is very marketable. >> So I think one of the interesting things is just thinking through this market. >> Mhm. >> Nvidia is obviously like the 800-lb gorilla in the room. Why don't they Why didn't they make SF Compute? And again like I I you know, the answer's going to be market manipulation but like let's just really get into the incentives of the multiple players. >> Yeah, 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. If you don't trade on it, then you don't have enough liquidity for lenders to finance against the forwards market. This is also why Crusoe and Coreweave and Lambda why the hyperscalers can't make it, why Base10 and Fireworks and Modal can't make it cuz they have too much demand on that side. Um this is the thing that benefits the entire industry because it makes the financing costs go down. But nobody can make it unless they give up the ability to do all the big inference money and all the big cloud deals. [00:34:19] You have to kill your business in order to make this. >> Mhm. That's why nobody does it. It's like structurally they're not set up to do that. And you're you're make you're basically reducing friction and that's all your revenue. You've announced some benchmark uh, some milestones. Actually, I forgot to s- say that up front. Uh, can you repeat what they are and uh, you know, just take a victory lap a little bit? >> Uh, we closed several hundred million dollars of volume. Um, we have a significant number of new clusters on the way. Um, and then the things that I think I'm most excited about um, is we've um, shipped a whole bunch of new features um, that make SF Compute very viable for platform as a service companies. Um, an example is um, we allow customers to subdivide their InfiniBand fabric um, without having to ask us. So, many clouds when you go to buy them, they will give you one InfiniBand fabric. Um, and then if you want to re redraw the lines of that InfiniBand [00:35:19] fabric, uh, you need to contact them and they will then do something for you. Um, we now can do that on the fly for customers um, which I think is really great um, because it interfaces with the market really well um, where you want to be able to redraw things in order to scale up and down your clusters. >> Um, would you describe this as like you're virtualizing the data center and making it programmable? >> Yeah, we're virtualizing data center and making it programmable. So, all the stuff that you would have gotten if you had owned the hardware, we're trying to virtualize that um, which I think is um, exciting to us. >> It's funny like the last time this happened was like the cloud. Like the >> [laughter] >> Yeah. Uh, which is which is really cool. Uh, and then uh, uh, finally I you know, I think uh, what is your call to action for for people um, you know, is it is it engineers? Is it like give me your supply? Like what what is it? >> Um, if you would like to own a GPU cluster and you want the residual value from it, you want to have it for your portfolio, um, we would love to work with you in order to build that GPU cluster. We will take over and we will uh, do the supercomputing portions of that and ensure the things go right. We [00:36:19] own the SLA. And then if it turns out that that was a bad idea, that you need out of that cluster, we can immediately sell that cluster to with else so it doesn't hit your balance sheet. >> Very simple pitch. Well, thank you so much for your time. You're You're doing a great service to the industry and I just am happy to be your friend. >> I appreciate it. >> All right. That's it. >> [music]