"Secondary market data shows moderately-used 2 to 3 year old GPUs trading at 50% to 70% of new pricing under normal conditions."
That's for good NVidia H100 units.[1] There's a shortage of those. That seems to be the price after removal, cleaning, testing and refurbishing. Raw units removed from a shutdown will not be as valuable.
H100 units are available on eBay, but multiple sellers are using the same picture of a new unit in its original packaging, a bad sign.[2] Some even have pictures with the logos of a competitor.
[1] https://introl.com/blog/secondary-gpu-markets-buying-selling...
[2] https://www.ebay.com/shop/nvidia-h100-gpu?_nkw=nvidia+h100+g...
Who is buying these at 70% of new pricing given the sky high likelihood of them being shot? Maybe it's safe to buy from small labs that went under quickly, but I can't imagine a cluster that has been operating near its thermal limits for a couple years fetching that kind of resale.
It was true of crypto GPUs too, although mostly people picking them up for gaming. Always seems high to me too but if you can get any guarantee of them not being on fire when they were pulled the bathtub curve keeps you pretty safe, thermal limits are limits for a reason.
crypto gpus didnt run at 100% power draw so the strain wasn't that massive
Why weren't crypto gpus running at 100%?
Most crypto mining on GPUs would use 100% of memory bandwidth, but only a fraction of the compute available. This is a consequence of ASIC resistance of their mining algorithms -- custom silicon can only offer a modest benefit over GPUs if the hard part is memory bandwidth.
I bought a RTX 3070 off a miner when Eth went to proof of stake.
It was clean, cheap, and is still going strong for daily gaming.
In its working life it was undervolted and probably cooled better than in my rig.
I half expect Nvidia to have buyback contacts like Ferrari with the larger customers to prevent a price crash when they all upgrade and to keep them scarce.
I hope not though, perhaps I can pick up a H100 in a few years if they get sold on the open market.
I worked at an org that had a substantial on-prem GPU datacenter. We transitioned to <Big Cloud Provider> with a substantial negotiated discount rate, with part of the contract being we would sell them all of our hardware and not purchase any more.
How does this work? Is the OEM giving the cloud provider a big discount or is the cloud provider giving you a teaser rate to lock up your business.
The list price of cloud is 10x higher than the price of hardware itself so that leaves room for some discounting.
> and not purchase any more.
why would anyone sign such a contract?
If you're planning to run in clouds, committing to not buy hardware (during the contract term, presumably) isn't a big imposition. Maybe you switch to a different cloud, and you wouldn't buy hardware for that.
If you want to switch back to on prem, there's probably a way to structure acquiring hardware so it doesn't break the contract. Maybe you lease it, maybe the purchase happens through a related company, maybe there was no way for the contracted cloud to find out...
I don't know if this was IT shrugging me off or if it was something real but some IT person at this big ISP I worked at told me that they cannot just buy an SSD — my windows box at work was running off of a hard disk in 2019 — and that there was some contract that said any computer hardware we bought had to be through HP or something like that and it takes many months it something like that.
The usual very short term corporate thinking that maximizes quarter profits while bankrupting the company in the long term. IMO a very shortsighted decision, if not downright stupid.
There's going to be a golden age of GPGPU compute in the next few years once A100/H100 are fully obsolete for running frontier models efficiently and the price plummets
It will be perfect for stuff like GPU-accelerated query engines, "classical ML" and every other CPU-based workload that could conceivably be offloaded to GPU
GPGPU? General Purpose GPU? If embarrassingly parallel CPU algorithms weren't offloaded to the GPU previously, why would the A100/H100 price drop make a difference? We had cheap GPU in the past and we still left plenty of performance on the table with CPU programs because they were easier to build.
Is the idea that previously maintaining GPU programs was expensive whereas now AI makes it cheap? If so, I could buy that line of reasoning.
Maybe relatedly, I expect (hope) the hardware manufacturers will ramp up supply in the meanwhile which would also put downward pressure on GPUs. Right now though this hardware crunch is making me sad, not even because of GPUs but also because of general memory / disk.
GPGPU programming has become significantly easier now and the payoff is bigger (better hardware), due to the immense investment in this due to ML/AI.
That should be illegal. Sounds like a very fraudulent business tactic.
Fraudulent, I wouldn't say so. Anti-consumer or anti-competitive? Sounds like.
Would you call a trade-in a fraudulent tactic?
Trade-ins are voluntary.
so are buybacks. you choose to sign the contract. there's no way they didn't have an escape clause, although likely it meant not using the cloud provider anymore
> so are buybacks. you choose to sign the contract
Right, so they're not voluntary.
Literally what? Do you understand what is being proposed?
Yes. "You choose to sign the contract" is not a reasonable argument, for two reasons:
1. There is one supplier, so you have no choice. 2. Even if you had a choice to sign the contract, this still means that it's not the same as a trade-in, because trade-ins are always voluntary, but once you have signed the contract, a right of first refusal is not.
In general, the "you chose to sign the contract" argument is a poor justification for bad contracts. If the contract is bad, it is bad regardless of whether you chose to sign it.
The Grift Economy places all legalities on the marks and their inability to form legal fights.
Sounds like capitalism at its finest.
Good old "win-win-lose"
isn't Ferrari the brand that requires any purchaser to be an existing owner? I could see NVIDIA going for something like that.
The only reason a datacenter would ditch their H100 is if it becomes uneconomical to run them, with newer silicon providing much more power efficiency. When that happens they'll look like a used V100 looks today: horribly inefficient, lacking modern data types and engines, requiring screaming server fans with weird adapters to not melt, way beyond end of life in terms of cuda support. Almost completely damn useless unless you really have no other alternative.
Wouldn't that be crazy - a hobbyist market for H100s?
Maybe someone could start a business buying up and rehousing these.
They're pretty specific to the datacenter use case with no outputs and they need to be cooled externally, principally through the very loud high speed fans used in data centers. I suppose you could strap a fan to one and put it in a normal case or maybe make a dedicated after market cooler (like the water blocks made for water cooling cases).
I think there's a market for a home AI server that can run an LLM or video gen model behind a web frontend. Not literally a raspberry pi strapped to an H100, but something with lopsided enough specs that people joke it is.
And precisely because it's such a huge headache to do yourself, I think a small company could make a nice business wrapping up used datacenter cards in that sort of server.
> Very loud high speed fans
if you haven't heard a 5u server intended for a datacenter rack come to life it's quite the experience. Sounds like a plane taking off.
This is also true of the older P100 and hobbyists do this stuff now, today
Or you can secure yourself a DLC one and try to feed it with the correct regime (liquid composition, temperature and flow rate). I'd say good luck.
These things get hot and are fussy about their requirements.
H100s (and the PCIe converter card you need) are available on eBay.
The GPU alone has a TDP of 700W, together with everything else (CPU, RAM, storage, fans) you're looking at 1500W+. Depending on the country, that may be enough to saturate your home's electricity uplink...
That's actually less bad than I assumed. High-end gaming GPUs are already almost at 600W with peak usage above that. I assumed it was much worse than that; that seems absolutely feasible for running at home.
Never heard electrical uplink before but to those wondering Italy, India, and Japan all have requirements around 3kw. I was slightly surprised by this, but in the end if your running this in a tiny space with that low of power your already probably not buying used H100s.
Hell no. 1.5kw is half a socket capacity. A heat pump or many kitchen appliances are using much more. Our car charger uses more as well.
That's why they said "depending on country"
In North America we are on 120V, making a standard 15A outlet only 1500W max, and something like 1200W sustained. To use higher wattage appliances, we have to upgrade our outlets to 20A (2000/1600W) or up our voltage to 240V, but that carries a different set of plugs and outlets as well.
Usually the home service is 200+ amps these days which is 24 KW total across all circuits, though you're right that most individual circuits are only 10-15 amp.
> In North America we are on 120V, making a standard 15A outlet only 1500W max, and something like 1200W sustained.
It's 1800W for short periods and 1500W sustained.
In the US they have a 120V system so it might actually saturate a normal socket over there. My PC room has a 16A fuse and 230V though, so should be plenty :)
A typical hair dryer uses about 1500W. I guess GPUs are mechanically not much different from a hair dryer.
Install it in your bathroom, use it like a restroom hand dryer.
> The job of an operations team is to keep all of this in steady state. They know which racks run hot in summer, which cooling loops have been flaky since the last firmware update, which jobs to re-route when a node degrades but has not failed yet. None of that knowledge is written down. It lives in the team.
Hmmn. All of this information should live in the monitoring system, in which case any frontier model will be able to get to grips with it in short order. It feels like the author doesn't really fully understand the changes brought about by the systems they are writing about.
It's generated prose; the author's understanding doesn't factor in, because they didn't actually write it.
The relatively slow depreciation of GPU value is an artifact of supply constraints. If you run fp4 inference and could choose freely between Hopper and a Rubin, the performance per watt would make the Hopper unattractive even if you paid zero for the hardware and only for the power.
You can't get the Rubin, or even the Blackwell, so you will pay for the H100 but this won't last if fabs ramp up capacity.
Not to mention, physical limits to lithography are slowing down significantly... so tech will continue to evolve more slowly... it'll never be the jump from 1080-1990 again, for example, even though 1990-2000 was pretty close, 2000-2010 much slower and since 2010 slower still.
What's as or more weird is how much hardware is backordered, and how much live hardware is allocated, but waiting on facilities for operation. And how many facilities are years behind at this point already... all on various credit and dept swaps between all the involved companies... it's not just a balloon, it's a house of cards balanced on a balloon.
The process node size in William the Conqueror's time was really off the charts.
supposedly, the next step is into fiber & optics.
but I generally agree, people put a lot of faith in the exponential leaps vs the exponential space.
You tell them we're not living on mars any time soon and they'll bring up christopher columbus.
How would fiber and optics help if the bottleneck is computation efficiency and not data transfer?
Are fabs going to ramp up capacity? Wouldn't it make more business sense for them to just not ramp up capacity and enjoy the higher prices?
I wonder what shovels were worth after the Gold Rush faltered. Blacksmiths probably had all the scrap iron they could ever care for.
Nah, "selling shovels" is mostly a metaphor, the amount of iron that went into mining equipment was insignificant, beyond some local demand peaks. The majority of the business was consumables.
A key thing to understand about the gold rush is that it was not a major economic event, or at least nowhere near as big as the participants thought it would be, hence the tradegy.
The AI gold rush is different in that there actually is a mountain of "shovels" large enough to flood the global market quite severely.
One data point: 512GB Mac Studios are selling on ebay for double what they were selling for new at the beginning of the year.
https://news.ycombinator.com/item?id=49005798 "I have an M3 ultra mac studio with 512 GB of memory. I want to sell it, [...] Given the high cost of the Mac studio ($20,000 or more)"
Anything with RAM in it is an appreciating asset.
Very difficult to sense check that substack post without access to the TLB credit agreement.
There are likely management service agreements from xAI proper -> SPV to cover precisely what the author talks about. Clearly, xAI could play games but without seeing the docs (which are not public), it's very difficult.
This article's basic point is right though. On the other hand, the LTV of this deal was approx 50% debt-financed (not too high; very much depends on the "V"). At 12.5%, it's not as if its being priced as a high quality asset.
Overall, substack post was too bearish. The wider point is that there's a lot of froth tied to what has now become systemically opaque - namely the circular deal flow that every hyperscaler, nvidia, neoclouds and friends are now engaged in. When the proverbial hits the fan, that stuff will be difficult to price and find few willing buyers with the competence to underwrite.
The systemic issues are the bigger concern than one specific deal imo.
Used GPU hosting for pension funds.
You go to ebay search for a used GPU. You get a price.
Neither is used servers a new thing or used routers. There are established used server companies.
Isn't it worth exactly what you can sell it for. a few ways to do this.
slow and awkward, best market match: The auction. sell to highest bidder.
faster and more customer friendly but poor market match until a lot of units sold: The store. guess price, adjust up or down to reach sell frequency desired.
fast and good market match but takes a knowledgeable customer base: The reverse auction. Start with price too high lower it over time until it sells.
All indications are there will be a lot of repossessed GPUs appearing on the market before too long. Likely to be messy for a while but will open up a lot of possibilities when it’s easy to get your hands on some secondhand GPUs.
This sounds like weapons market after the crash of soviet republic. Never has been a better time to get a functioning tank or parts of nukes.
This is a good article. I've been curious about how this is going to play out. A couple of data points:
1. An enthusiast had a project to get a V100 working on his PC [1]. This was a ~$10k GPU 10 years ago. It's now sold for scrap;
2. The A100 came out in 2020 and cannot run a large model like DeepSeek v4 Pro. It can run Flash. You need a 16xH100 cluster to run Pro and that's a ~4 year old GPU and AFAICT 8xB100 or 4xB200;
3. We're about to roll out R100/R200s.
I'm surprised that NVidia is moving to a 1 year product cycle (per this article) because the big question I've had is what's that going to do to existing investments in GPUs. Why? Because if 4xR100 can do the work of 32xH100 then that's a massive advantage in performance-per-Watt, which I think is going to be the only metric that ends up mattering.
In addition to raw power, new capabilities are developed and come online. For example, certain smaller, more efficient quantization methods just didn't exist on older hardware.
Oh, another thought from this: a 9% annual failure rate just goes to show you how ridiculous the idea of orbital data centers really is. Orbital DCs were always just a pump-and-dump scheme for SpaceX's IPO.
Currently it gets expensive to run models larger than ~31B locally. You start to need some pretty expensive hardware. That's going to change. I don't expect we'll be running 1T+ models on a Macbook Pro within 5 years (at reasonable inference rates) but I think people today will be shocked at what's being run locally in 5 years and that'll easily be 100-200B+ models.
[1]: https://www.hackster.io/news/hacking-a-server-grade-nvidia-g...
Time to dig up the spreadsheet models of what dark fiber is worth.
I like Meg a lot a human, but Meg is all doom and gloom. Every single post she makes is about how GPUs fail [0] and now she's onto how financing is a big thing just waiting to crash and "nobody knows what a used GPU cluster is worth"...
Actually, we do, people offer them to me all the time. A used box of MI300x is $257k. "There is no GPU futures market"... actually there are a few of them that people have pitched to me.
This article is a lot of words from someone who isn't actually buying or deploying compute. My point is... take it all with a grain of salt.
Somehow at any given moment every possible topic to discuss on HN belongs to either the set of "it's amazing and nobody can say anything bad about it" or the set of "this thing sucks and nobody is allowed to say anything positive about it" and I never have ANY idea which one any given topic will be in on any given day.
latchkey is being restrained. He runs a data center filled with AMD GPUs. He's got a lot more insight to the business of it than the post does.
That's great, their comment should get more attention then.
I was complaining that it's obviously incorrect that nobody knows what used GPUs are worth, not about latchkey.
I have NO idea why anyone is upvoting a post titled "nobody knows what a used GPU cluster is worth", that is a WILD claim.
she makes a lot of wild claims for clicks and gets to the front page with them... sigh.
In 2008, was the opinion of a banker more "insightful" than they opinion of journalists, bloggers and normal people talking about the imminent subprime crash?
Cherry picked your example there a bit
latchkey is being restrained. He runs a data center filled with AMD GPUs. He's got a lot more insight to the real, lived experience of such a business than the post seems to have.
Aren't the AI Datacenters for the Digital Control Grid, so govt contracts?
The GPU cluster's RAM is probably worth more than the flops these days.
Is the 30-50% of face value realistic for "Liquidation Value"? If one organization has to liquidate, sure. But if the bubble pops and many groups have to liquidate at once? Owners will be lucky if they can dodge the recycling fees.
Nothing, because those "GPU's" are special proprietary hardware and are not what most people are capable of plugging in into anything.
People are selling adapter boards to plug some data center GPUs into a regular pcie slot.
Essentially nothing, fractions of a penny on a dollar, because players who could afford paying real money won't risk the crusty old hardware - so you're limited to buyers who still need a massive cluster but don't have AI infinite money glitch enabled
I’ll take “people who sell used hardware” for 500 Alex.
Maybe this article has something useful to say but the painfully LLM-generated prose is too distracting to make it evident.
Did an AI make all the text and kerning so ugly? Why do web sites look like this now?
Isn't this just Substack?
How do you know? I feel that it's just poorly edited.
In my experience, AI is easier to read than this was.
Some sentences feel pretty AI like:
> These are not catastrophic events. They are the steady state.
> There is no GPU futures market, no standardized residual value curve, and no way to lock in a forward rental rate. The premium is is the price of underwriting in the dark.
The headings are also AI like, a lot of essays before usually did not have titled sections but now they do and they all feel like these.
In addition the diagrams themselves look pretty AI generated.
Soon we will have people asking if these comments are AI-generated.
so when will xAI default on its debt?
Never? They're currently renting out those GPUs at a profit.
Their debt expects a much (much!) bigger margin tho
Would it make economic sense to strip it down and sell for parts? That’s how it’s done now for older data centers, where the obsolete equipment is sent off to China, stripped down for parts, and sold on the secondary market. I’ve picked up several older but still useful RAID hardware cards off eBay this way.
I’m mainly interested in getting some DDR4/5 and RTX5090s on the cheap :).
so you're telling me you can't use 1 or 2% percent of 5 billions dollars to rebuild a team that runs GPU clusters for like a couple of years ?! and the guy that borrowed billions from these banks would want to mess up that relationship for what? I mean he is a stupid narcissist but not to that degree. This whole article makes no sense to me.
Given the price for a rack of bc-250 after the crypto hype cycle, the expected value of the hardware will be around 5% to 10% of the original retail price.
Without other market influences, that is a >90% expected discount when the over-provisioned market must inevitably self-correct.
If the Market follows what Samsung/SK Hynix did to the South Korean exchange this week, than the "AI" bubble will hit harder than the dot com crash.
I like the Shrek Movie correlation theory, as they always happen just before Debt-backed investors get hit hard... And the new film is due out in 2027. =3
> If the Market follows what Samsung/SK Hynix did to the South Korean exchange this week, than the "AI" bubble will hit harder than the dot com crash
Can you tell us more about this? Or some link
GPU clusters have, largely, no actual value.
If anything, you might have to pay to have them disposed of, they don't really have any meaningful used eBay market outside of the randos that want to do high end extreme local inference in their basement.
Also, as for RAMmageddon, the inference SBCs that all of the AI bros bought don't have DIMMs, they're not even the right chip: its all GDDR and LPDDR. The only DDR DIMMs being consumed are for regular non-inference machines that help run the business and service infrastructure behind the scenes.
> Silent data corruption (SDC) is the most expensive, where a faulty GPU produces wrong answers without crashing anything, which means a multi-day training run can complete normally and the resulting model weights are quietly poisoned.
Anyone know how these get caught ultimately?
Usually silent data corruption isn't caught. If a file fails to open you might realize it's been corrupted.
If nobody knows what a used GPU cluster is worth it means nobody is doing anything important with GPUs - time to short everything. Do you believe it?