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mnky9800n 11 hours ago

I really don't understand the argument that nvidia GPUs only work for 1-3 years. I am currently using A100s and H100s every day. Those aren't exactly new anymore.

mbrumlow 9 hours ago | parent | next [-]

It’s not that they don’t work. It’s how businesses handle hardware.

I worked at a few data centers on and off in my career. I got lots of hardware for free or on the cheap simply because the hardware was considered “EOL” after about 3 years, often when support contracts with the vendor ends.

There are a few things to consider.

Hardware that ages produce more errors, and those errors cost, one way or another.

Rack space is limited. A perfectly fine machine that consumes 2x the power for half the output cost. It’s cheaper to upgrade a perfectly fine working system simply because it performs better per watt in the same space.

Lastly. There are tax implications in buying new hardware that can often favor replacement.

fooker 9 hours ago | parent | next [-]

I’ll be so happy to buy a EOL H100!

But no, there’s none to be found, it is a 4 year, two generations old machine at this point and you can’t buy one used at a rate cheaper than new.

pixl97 8 hours ago | parent | next [-]

Well demand is so high currently that it's likely this cycle doesn't exist yet for fast cards.

For servers I've seen where the slightly used equipment is sold in bulk to a bidder and they may have a single large client buy all of it.

Then around the time the second cycle comes around it's split up in lots and a bunch ends up at places like ebay

lancekey 6 hours ago | parent [-]

Yea looking at 60 day moving average on computeprices.com H100 have actually gone UP in cost recently, at least to rent.

A lot of demand out there for sure.

SequoiaHope 6 hours ago | parent | prev | next [-]

There’s plenty on eBay? But at the end of your comment you say “a rate cheaper than new” so maybe you mean you’d love to buy a discounted one. But they do seem to be available used.

fooker 3 hours ago | parent [-]

> so maybe you mean you’d love to buy a discounted one

Yes. I'd expect 4 year old hardware used constantly in a datacenter to cost less than when it was new!

(And just in case you did not look carefully, most of the ebay listings are scams. The actual product pictured in those are A100 workstation GPUs.)

aswegs8 9 hours ago | parent | prev [-]

Not sure why this "GPUs obsolete after 3 years" gets thrown around all the time. Sounds completely nonsensical.

belval 8 hours ago | parent | next [-]

Especially since AWS still have p4 instances that are 6 years old A100s. Clearly even for hyperscalers these have a useful life longer than 3 years.

tuckerman 6 hours ago | parent | prev | next [-]

I agree that there is hyperbole thrown around a lot here and its possible to still use some hardware for a long time or to sell it and recover some cost but my experience in planning compute at large companies is that spending money on hardware and upgrading can often result in saving money long term.

Even assuming your compute demands stay fixed, its possible that a future generation of accelerator will be sufficiently more power/cooling efficient for your workload that it is a positive return on investment to upgrade, more so when you take into account you can start depreciating them again.

If your compute demands aren't fixed you have to work around limited floor space/electricity/cooling capacity/network capacity/backup generators/etc and so moving to the next generation is required to meet demand without extremely expensive (and often slow) infrastructure projects.

zozbot234 5 hours ago | parent [-]

Sure, but I don't think most people here are objecting to the obvious "3 years is enough for enterprise GPUs to become totally obsolete for cutting-edge workloads" point. They're just objecting to the rather bizarre notion that the hardware itself might physically break in that timeframe. Now, it would be one thing if that notion was supported by actual reliability studies drawn from that same environment - like we see for the Backblaze HDD lifecycle analyses. But instead we're just getting these weird rumors.

bmurphy1976 8 hours ago | parent | prev [-]

It's because they run 24/7 in a challenging environment. They will start dying at some point and if you aren't replacing them you will have a big problem when they all die en masse at the same time.

These things are like cars, they don't last forever and break down with usage. Yes, they can last 7 years in your home computer when you run it 1% of the time. They won't last that long in a data center where they are running 90% of the time.

zozbot234 7 hours ago | parent | next [-]

A makeshift cryptomining rig is absolutely a "challenging environment" and most GPUs by far that went through that are just fine. The idea that the hardware might just die after 3 years' usage is bonkers.

Der_Einzige 7 hours ago | parent [-]

Crypto miners undervote for efficiency GPUs and in general crypto mining is extremely light weight on GPUs compared to AI training or inference at scale

Der_Einzige 7 hours ago | parent | prev [-]

With good enough cooling they can run indefinitely!!!!! The vast majority of failures are either at the beginning due to defects or at the end due to cooling! It’s like the idea that no moving parts (except the HVAC) is somehow unreliable is coming out of thin air!

JMiao 8 hours ago | parent | prev [-]

Do you know how support contract lengths are determined? Seems like a path to force hardware refreshes with boilerplate failure data carried over from who knows when.

linkregister 11 hours ago | parent | prev | next [-]

The common factoid raised in financial reports is GPUs used in model training will lose thermal insulation due to their high utilization. The GPUs ostensibly fail. I have heard anecdotal reports of GPUs used for cryptocurrency mining having similar wear patterns.

I have not seen hard data, so this could be an oft-repeated, but false fact.

Melatonic 11 hours ago | parent | next [-]

It's the opposite actually - most GPU used for mining are run at a consistent temp and load which is good for long term wear. Peaky loads where the GPU goes from cold to hot and back leads to more degradation because of changes in thermal expansion. This has been known for some time now.

Yizahi 10 hours ago | parent | next [-]

That is commonly repeated idea, but it doesn't take into account countless token farms which are smaller than a datacenter. Basically anything from a single MB with 8 cards to a small shed with rigs, all of which tend to disregard common engineering practices and run hardware into a ground to maximize output until next police raid or difficulty bump. Plenty of photos in the internet of crappy rigs like that, and no one guarantees which GPU comes whom where.

Another commonly forgotten issue is that many electrical components are rated by hours of operation. And cheaper boards tend to have components with smaller tolerances. And that rated time is actually a graph, where hour decrease with higher temperature. There were instances of batches of cards failing due to failing MOSFETs for example.

Melatonic 7 hours ago | parent | next [-]

While I'm sure there are small amateur setups done poorly that push cards to their limits this seems like a more rare and inefficient use. GPUS (even used) are expensive and running them at maximum would require large costs and time to be replacing them regularly. Not to mention the increased cost of cooling and power.

Not sure I understand the police raid mentality - why are the police raiding amateur crypto mining setups ?

I can totally see cards used by casual amateurs being very worn / used though - especially your example of single mobo miners who were likely also using the card for gaming and other tasks.

I would imagine that anyone purposely running hardware into the ground would be running cheaper / more efficient ASICS vs expensive Nvidia GPUs since they are much easier and cheaper to replace. I would still be surprised however if most were not proritising temps and cooling

coryrc 9 hours ago | parent | prev | next [-]

Specifically, we expect a halving of lifetime per 10K increase in temperature.

whaleofatw2022 9 hours ago | parent | prev | next [-]

Let's also not forget the set of miners that either overclock or dont really care about long term in how they set up thermals

belval 8 hours ago | parent [-]

Miners usually don't overclock though. If anything underclocking is the best way to improve your ROI because it significantly reduces the power consumption while retaining most of the hashrate.

Melatonic 7 hours ago | parent | next [-]

Exactly - more specifically undervolting. You want the minimum volts going to the card with it still performing decently.

Even in amateur setups the amount of power used is a huge factor (because of the huge draw from the cards themselves and AC units to cool the room) so minimising heat is key.

From what I remember most cards (even CPUs as well) hit peak efficiency when undervolted and hitting somewhere around 70-80% max load (this also depends on cooling setup). First thing to wear out would probably be the fan / cooler itself (repasting occasionally would of course help with this as thermal paste dries out with both time and heat)

bluGill 5 hours ago | parent [-]

The only amatures I know doing this are trying to heat their garrage for free. so long as the heat gain is paid for they can afford to heat an otherwise unheated building.

zozbot234 7 hours ago | parent | prev [-]

Wouldn't the exact same considerations apply to AI training/inference shops, seeing as gigawatts are usually the key constraint?

WalterBright 5 hours ago | parent | prev [-]

Why would police raid a shed housing a compute center?

mbesto 9 hours ago | parent | prev [-]

Source?

zozbot234 11 hours ago | parent | prev | next [-]

> I have heard anecdotal reports of GPUs used for cryptocurrency mining having similar wear patterns.

If this was anywhere close to a common failure mode, I'm pretty sure we'd know that already given how crypto mining GPUs were usually ran to the max in makeshift settings with woefully inadequate cooling and environmental control. The overwhelming anecdotal evidence from people who have bought them is that even a "worn" crypto GPU is absolutely fine.

munk-a 11 hours ago | parent | prev [-]

I can't confirm that fact - but it's important to acknowledge that consumer usage is very different from the high continuous utilization in mining and training. It is credulous that the wear on cards under such extreme usage is as high as reported considering that consumers may use their cards at peak 5% of waking hours and the wear drop off is only about 3x if it is used near 100% - that is a believable scale for endurance loss.

denimnerd42 10 hours ago | parent | prev | next [-]

1-3 is too short but they aren’t making new A100s, theres 8 in a server and when one goes bad what do you do? you wont be able to renew a support contract. if you wanna diy you eventually you have to start consolidating pick and pulls. maybe the vendors will buy them back from people who want to upgrade and resell them. this is the issue we are seeing with A100s and we are trying to see what our vendor will offer for support.

iancmceachern 11 hours ago | parent | prev | next [-]

They're no longer energy competitive. I.e. the amount of power per compute exceeds what is available now.

It's like if your taxi company bought taxis that were more fuel efficient every year.

bob1029 11 hours ago | parent | next [-]

Margins are typically not so razor thin that you cannot operate with technology from one generation ago. 15 vs 17 mpg is going to add up over time, but for a taxi company it's probably not a lethal situation to be in.

SchemaLoad 6 hours ago | parent | next [-]

At least with crypto mining this was the case. Hardware from 6 months ago is useless ewaste because the new generation is more power efficient. All depends on how expensive the hardware is vs the cost of power.

10 hours ago | parent | prev | next [-]
[deleted]
iancmceachern 8 hours ago | parent | prev [-]

Tell that to the airline industry

hibikir 6 hours ago | parent | next [-]

And yet they aren't running planes and engines all from 2023 or beyond: See the MD-11 that crashed in Louisville: Nobody has made a new MD-11 in over 20 years. Planes move to less competitive routes, change carriers, and eventually might even stop carrying people and switch to cargo, but the plane itself doesn't get to have zero value when the new one comes out. An airline will want to replace their planes, but a new plane isn't fully amortized in a year or three: It still has value for quite a while

bob1029 8 hours ago | parent | prev [-]

I don't think the airline industry is a great example from an IT perspective, but I agree with regard to the aircraft.

mikkupikku 11 hours ago | parent | prev | next [-]

If a taxi company did that every year, they'd be losing a lot of money. Of course new cars and cards are cheaper to operate than old ones, but is that difference enough to offset buying a new one every one to three years?

gruez 11 hours ago | parent | next [-]

>If a taxi company did that every year, they'd be losing a lot of money. Of course new cars and cards are cheaper to operate than old ones, but is that difference enough to offset buying a new one every one to three years?

That's where the analogy breaks. There are massive efficiency gains from new process nodes, which new GPUs use. Efficiency improvements for cars are glacial, aside from "breakthroughs" like hybrid/EV cars.

dylan604 11 hours ago | parent | prev | next [-]

>offset buying a new one every one to three years?

Isn't that precisely how leasing works? Also, don't companies prefer not to own hardware for tax purposes? I've worked for several places where they leased compute equipment with upgrades coming at the end of each lease.

mikkupikku 9 hours ago | parent | next [-]

Who wants to buy GPUs that were redlined for three years in a data center? Maybe there's a market for those, but most people already seem wary of lightly used GPUs from other consumers, let alone GPUs that were burning in a crypto farm or AI data center for years.

dylan604 7 hours ago | parent | next [-]

> Who wants to buy

who cares? that's the beauty of the lease. once it's over, the old and busted gets replaced with new and shiny. what the leasing company does is up to them. it becomes one of those YP not an MP situations with deprecated equipment.

bluGill 5 hours ago | parent [-]

The leasing company cares - the lease terms depend on the answer. That is why I can lease a car for 3 years for the same payment as a 6 year loan (more or less) - the lease company expects someone will want it. If there is no market for it after they will still lease it but the cost goes up

coryrc 9 hours ago | parent | prev | next [-]

Depends on the price, of course. I'm wary of paying 50% of new for something run hard 3 years. Seems an NVIDIA H100 is going for $20k+ on EBay. I'm not taking that risk.

pixl97 8 hours ago | parent | prev [-]

Depending on the discount, a lot of people.

gowld 10 hours ago | parent | prev [-]

That works either because someone wants to buy old hardware for the manufacturer/lessor, or because the hardware is EOL in 3 years but it's easier to let the lessor deal with recyling / valuable parts recovery.

wordpad 11 hours ago | parent | prev | next [-]

If your competitor refreshes their cards and you dont, they will win on margin.

You kind of have to.

lazide 11 hours ago | parent [-]

Not necessarily if you count capital costs vs operating costs/margins.

Replacing cars every 3 years vs a couple % in efficiency is not an obvious trade off. Especially if you can do it in 5 years instead of 3.

iancmceachern 5 hours ago | parent | next [-]

You highlight the exact dilemma.

Company A has taxis that are 5 percent less efficient and for the reasons you stated doesn't want to upgrade.

Company B just bought new taxis, and they are undercutting company A by 5 percent while paying their drivers the same.

Company A is no longer competitive.

Dylan16807 4 hours ago | parent [-]

The debt company B took on to buy those new taxis means they're no longer competitive either if they undercut by 5%.

The scenario doesn't add up.

iancmceachern 3 hours ago | parent [-]

But Company A also took on debt for theirs, so that's a wash. You assume only one of them has debt to service?

Dylan16807 3 hours ago | parent [-]

Both companies bought a set of taxis in the past. Presumably at the same time if we want this comparison to be easy to understand.

If company A still has debt from that, company B has that much debt plus more debt from buying a new set of taxis.

Refreshing your equipment more often means that you're spending more per year on equipment. If you do it too often, then even if the new equipment is better you lose money overall.

If company B wants to undercut company A, their advantage from better equipment has to overcome the cost of switching.

iancmceachern an hour ago | parent [-]

You are assuming something again.

They both refresh their equipment at the same rate.

zozbot234 11 hours ago | parent | prev [-]

You can sell the old, less efficient GPUs to folks who will be running them with markedly lower duty cycles (so, less emphasis on direct operational costs), e.g. for on-prem inference or even just typical workstation/consumer use. It ends up being a win-win trade.

lazide 9 hours ago | parent [-]

Then you’re dealing with a lot of labor to do the switches (and arrange sales of used equipment), plus capital float costs while you do it.

It can make sense at a certain scale, but it’s a non trivial amount of cost and effort for potentially marginal returns.

pixl97 7 hours ago | parent [-]

Building a new data center and getting power takes years to double your capacity. Swapping out out a rack that is twice as fast takes very little time in comparison.

lazide 7 hours ago | parent [-]

Huh? What does your statements have to do with what I’m saying?

I’m just pointing out changing it out at 5 years is likely cheaper than at 3 years.

pixl97 6 hours ago | parent [-]

Depends at the rate of growth of the hardware. If your data center is full and fully booked, and hardware is doubling in speed every year it's cheaper to switch it out every couple of years.

philwelch 10 hours ago | parent | prev [-]

If there was a new taxi every other year that could handle twice as many fares, they might. That’s not how taxis work but that is how chips work.

echelon 11 hours ago | parent | prev [-]

Nvidia has plenty of time and money to adjust. They're already buying out upstart competitors to their throne.

It's not like the CUDA advantage is going anywhere overnight, either.

Also, if Nvidia invests in its users and in the infrastructure layouts, it gets to see upside no matter what happens.

mbesto 10 hours ago | parent | prev | next [-]

Not saying your wrong. A few things to consider:

(1) We simply don't know what the useful life is going to be because of how new the advancements of AI focused GPUs used for training and inference.

(2) Warranties and service. Most enterprise hardware has service contracts tied to purchases. I haven't seen anything publicly disclosed about what these contracts look like, but the speculation is that they are much more aggressive (3 years or less) than typical enterprise hardware contracts (Dell, HP, etc.). If it gets past those contracts the extended support contracts can typically get really pricey.

(3) Power efficiency. If new GPUs are more power efficient this could be huge savings on energy that could necessitate upgrades.

epolanski 9 hours ago | parent | next [-]

Nvidia is moving to a 1 year release life cycle for data center, and in Jensen's words once a new gen is released you lose money for being on the older hardware. It makes no longer financially sense to run it.

pixl97 7 hours ago | parent [-]

That will come back to bite them in the ass if money leaves the AI race.

pvab3 5 hours ago | parent | prev [-]

based on my napkin math, an H200 needs to run for 4 years straight at maximum power (10.2 kW) to consume its own price of $35k worth of energy (based on 10 cents per kWh)

swalsh 8 hours ago | parent | prev | next [-]

If power is the bottleneck, it may make business sense to rotate to a GPU that better utilizes the same power if the newer generation gives you a significant advantage.

legitster 10 hours ago | parent | prev | next [-]

From an accounting standpoint, it probably makes sense to have their depreciation be 3 years. But yeah, my understanding is that either they have long service lives, or the customers sell them back to the distributor so they can buy the latest and greatest. (The distributor would sell them as refurbished)

savorypiano 11 hours ago | parent | prev | next [-]

You aren't trying to support ad-based demand like OpenAI is.

linuxftw 11 hours ago | parent | prev [-]

I think the story is less about the GPUs themselves, and more about the interconnects for building massive GPU clusters. Nvidia just announced a massive switch for linking GPUs inside a rack. So the next couple of generations of GPU clusters will be capable of things that were previously impossible or impractical.

This doesn't mean much for inference, but for training, it is going to be huge.