An interesting implication of this is that AI inference and training has a path to a ~3x hardware cost reduction (and maybe ~2x total cost reduction) without any technical innovation whatsoever, we just need to wait for dram supply to meet demand (either by manufacturing scaling or just waiting for the current rate of manufacturing to fill the demand spike).
Waterluvian 7 minutes ago [-]
What’s the lifespan/refurbishability of the capex elements like the “GPU” modules or even the DRAM soldered into them?
slicktux 2 hours ago [-]
I bought 96GB of RAM a couple of years ago for ~$250.
That same RAM now costs $1200!
trollbridge 9 minutes ago [-]
I bought 192GB of DDR3 a year ago for literally $60 ($5 a stick). It's about $22 a stick now, so more like $350 today. What on earth is _anybody_ doing with DDR3?
dawnerd 46 minutes ago [-]
I’m so mad I didn’t max out my main server when I had the chance. Used enterprise sticks were dirt cheap on eBay.
adroitboss 1 hours ago [-]
I paid $279 for crucial 96gb DDR5 5600 MHz SO-DIMM ram October 22 of last year. Amazon has the same kit going for $1,048.90 right now.
Joel_Mckay 49 minutes ago [-]
Nice, you were lucky. =3
bushbaba 1 hours ago [-]
Makes prior assumptions that getting tens of gigs of ram is cheap thrown out the window. Would likely lead to super fast SSDs such as optain being way more valuable
IshKebab 24 minutes ago [-]
I bought a couple of used computers with 256 GB of DDR 4 (total) a year ago. The ram is worth more than I paid for the whole machines now.
ksec 1 hours ago [-]
It is one of the thing with consumer when they remember they brought it at the absolutely lowest price point when DRAM maker were bleeding money.
Those are not normal pricing. Before the pricing collapse in early 2020, 96GB DDR5 would have cost about $450 to $500. And I will need to restate again the cost of DRAM hasn't really changed much in the past 20 years. Its price just goes up and down in cycles.
So in reality it is more like going from $500 to $1300. But consumer felt it was more like going from $200 to $1300.
Crucial are already selling DRAM made by CXMT. And China are already throwing money at it. I doubt the memory bubble will burst in next 12-24 months. As in going back to money losing DRAM pricing. As they will all pivot to HBM or other money making products. But the bulk of lower end consumer DDR5 or LPDDR5 will goes to Chinese Foundry. Assuming they have figure out how to do them well. Which they have improved but are still so far away from industry leaders.
Normally memory maker will push the next DDR standard to market just to push out Chinese competitors, I am not sure it will work the same this time around. DDR5 have plenty of other usage / demands.
DoctorOetker 1 hours ago [-]
> Crucial are already selling DRAM made by CXMT.
Crucial was disestablished this year.
trollbridge 7 minutes ago [-]
Ah, the old decrucialisestablishmentarianism.
DoctorOetker 4 minutes ago [-]
I found the phrasing weird myself, I quoted wikipedia
voxic11 1 hours ago [-]
He probably meant Corsair which is the DRAM brand selling CXMT produced chips.
giancarlostoro 24 minutes ago [-]
Ramflation
mchusma 1 hours ago [-]
Everything I read seems to suggest that RAM capacity is going to grow at 20-25% a year, which just doesn't seem good enough. Even in consumer use cases, phones and laptops would benefit greatly by double the amount of RAM. And then obviously, the AI need is gigantic.
I don't see it going away. I mean, it may not grow as fast as now, but I don't see it growing away either. I get why the memory makers do not want to bankrupt themselves, but it feels like there's got to be some way to push that risk off onto model providers and other people in the ecosystem to allow us to grow ram capacity more like 50% per year.
foota 2 minutes ago [-]
In theory the new futures markets for chip components would help here, since it would allow DRAM suppliers to insulate themselves from that risk.
DoctorOetker 21 minutes ago [-]
According to the recent article HBM memory is 3x less efficient wafer area wise than LPDDR; but the bandwidth is more than triple.
What if its in everyone's interest to buy computers at say 1/3rd the rate and switch everything over to HBM?
the discrepancy between compute and memory has been growing for ages, perhaps a painful switch to HBM is exactly what we need?
Would you rather have 3 intermediate computers with low memory bandwidth, or wait a little longer statistically so that we can all enjoy a new computer at 1/3rd the rate but much higher bandwidth than the area ratio?
FuckButtons 11 minutes ago [-]
These are fundamentally different points in design space though, hbm doesn’t have a 10mw idle draw like lpddr does.
aurareturn 7 minutes ago [-]
Can’t put HBM in smartphones and laptops. The power drain is too great.
minraws 59 minutes ago [-]
I mean the biggest risk is Chinese CXML benefits and capturing markets that others are leaving hanging and then being able to compete and push out the others when costs start to normalize.
As for 20-25% growth not being enough, I think it's not that far off, if we assume data center build out plans hit a wall and slow down significantly, and the AI heat starts to cool off.
I don't think 20-25% may be enough in the short term but if the AI build out stops within this year, we have a massive oversupply instead of a under supply.
blululu 4 minutes ago [-]
Looking at the history of the memory industry the biggest risk is that a firm would over produce and go bankrupt. Maybe this time is different but so far no memory chip maker has gone under because their competition increased capacity.
galangalalgol 19 minutes ago [-]
Is there any indication research is being focused on reducing menory footprint of inference for frontier class models? Is the low hanging fruit already gone there?
minraws 2 minutes ago [-]
Low hanging? how low hanging are we talking, the basic stuff is gone. Largely big challenges around quantization were solved 2 years ago, and we have just been improving from there.
But can massive gains still be made? Definitely.
The entire AI hype is based on the paper Attention is all you need, and Attention is basically loading a huge matrix of all the tokens in memory, how well you can optimize this attention layer is basically how most architectures are trying to solve for performance and memory usage.
Only one with significant gains in it is DeepSeek (or so I would like to believe because others don't make their work open for folks like me not in Big AI Labs to read). Their MLA architecture reduced KV-cache memory requirements by upto 90%, ofc that's purely architectural change.
With some quantization like Turboquant from google you could push it down to ~1/3 of that. So 96% memory savings when talking about kv-cache.
But the models are close to being saturated for quantization based memory optimizations. We will have to see some architectural changes for a significant shift now.
aurareturn 5 minutes ago [-]
If they manage to make memory more efficient, they’ll just increase the context size and/or model size.
We just haven’t reached the diminishing return of gen AI capabilities yet.
Models will get more useful if you have higher context size or higher param size. Then people will just use the models even more, leading to even more memory demand.
zx8080 56 minutes ago [-]
What is the risk? Competition is good for consumers.
LPisGood 53 minutes ago [-]
The risk is to the business not the consumers
johnvanommen 1 hours ago [-]
I really don’t want to give anyone ideas, but doesn’t this make the Nvidia 5090 an unbelievably good deal right now?
The VRAM in the 5090 is only made by one country in the world.
The 50xx series is special, because its ram is so dependent on a single commodity. It’s not like a 4090 or a 3090; their VRAM chips have been around for years.
If there’s a shortage or interruption in DDR7 VRAM, it seems like every GPU that requires it would explode in value.
I hope I don’t regret posting this because I’d really like to buy one myself…
layer8 58 minutes ago [-]
An unbelievably good deal at $4000 plus?
johnvanommen 57 minutes ago [-]
Possibly the best deal there is
I really need to shut up, or bite the bullet and by one.
If you graph the tokens per second on the 5090, your jaw will hit the floor at how cheap it is
gruez 33 minutes ago [-]
With only 32gb of vram, you can only run small/quantized models, in which case what's the point? At $4000, that gets you 20 months of 10x claude or chagpt subscriptions, which provide far better models. You'd need some use case where you can tolerate worse models, and use a steady supply of them. That doesn't match most people's usage patterns.
mattmanser 1 hours ago [-]
It's gone up like 300% in cost in the last year.
JacobAsmuth 1 hours ago [-]
Which surely is the highest it'll ever be! You're suggesting that the price will go down in the future? Would love to hear more about your thought process!
bcrosby95 29 minutes ago [-]
Are you saying we're entering a period where tech increases in price instead of decreases? I guess it depends upon time horizon, but your statement isn't very specific.
johnvanommen 55 minutes ago [-]
I believe msrp is $2000 right?
EnPissant 48 minutes ago [-]
There was only a very brief time it was selling for MSRP (last fall for $2000). Even if you use that as the previous data point, it's only 200% increased.
forrestthewoods 1 hours ago [-]
if you can buy one!
The RTX 5090 is faster than an H200. It just has less ram (32 vs 141), doesn't have NVLink, and technically isn't allowed to be used in a datacenter.
The datacenter GPUs sell at an 80% margin. They're incredibly overpriced. But the laws of supply and demand are undefeated and so here we all are.
alphabeta3r56 57 minutes ago [-]
> The RTX 5090 is faster than an H200. It just has less ram
H200 has HBM and much more 64-bit compute
forrestthewoods 32 seconds ago [-]
Let me try again.
RTX 5090 has more CUDA cores that run at a higher clock speed. H200 has more RAM and significantly more RAM bandwidth.
Which one is net faster depends on your use case. But you may be very surprised that many workflows are faster on an RTX 5090!
elorant 2 hours ago [-]
Bought a second hand Dell server a week ago. The entire rig with a 12-core CPU and 32GB DDR4 ecc RAM cost as much as I'd pay to buy 64 GB of DDR RAM alone. I hope there's an end to this absurdity soon enough otherwise the pain will affect other markets too. I read the other day that PC case sales have collapsed by more than 40%.
It makes me sad for the Neo 2.0.
More ram is the only thing stopping me switching to it from a Pro.
nik282000 1 hours ago [-]
I feel like by the time the AI bubble bursts the PC market will be irreparably damaged. Manufactures who have been making "enterprise" parts aren't going to go back to making consumer parts because there will be no market for it. And with a glut of datacenters not making any money on slop, they are going to be repurposed for saas, stuff like OnShape but for every application.
Most users don't seem to care about storing everything they generate in cloud services and this could easily be sold as an alternative to owning "expensive" desktop or laptop hardware.
dawnerd 43 minutes ago [-]
They’re going to pivot to you renting desktop cloud compute instead of owning anything.
bitwize 33 minutes ago [-]
Enjoy your HP laptop subscription, it's all the computer you're going to get moving forward.
nik282000 27 minutes ago [-]
It's the reason I just build a new PC, despite the insane prices, I'd rather overpay than have reasonable prices but no stock to buy. With any luck I'll get 8-10 years out of this one and by then the PC landscape will be something else entirely.
MattDamonSpace 13 minutes ago [-]
“Bubble”
oceansky 2 hours ago [-]
Awful time for gamers and PC hobbyists not fully into AI.
aunty_helen 59 minutes ago [-]
This is 100% going to kill the home built pc market. When I started building gaming pcs, the top top card was 750$ (NZD). Now they’re 10,000 just for the gpu and another 1-2000 for ram.
People used to get into gaming pcs as an affordable hobby, now it’s making general aviation look like plan B.
johnvanommen 53 minutes ago [-]
Yes, this will definitely renew interest in Stadia type products.
themafia 36 minutes ago [-]
It's more likely to kill the AI market. They're overbuilding capacity and most of it is going unused. The upcoming haircut is going to kill a lot of the major players.
They've intentionally crafted an unsustainable business model in an effort to get users in the front door and raise their MAUs. We've seen this story before. We should know precisely where it's headed.
Joel_Mckay 42 minutes ago [-]
Indeed, Gamers Nexus is doing interviews with PC component manufacturers, and some are hurting bad right now. The PC market is no longer in competition, but rather survival mode. =3
I think it's the opposite. Sure in short term hobbyists are getting squeezed, but the amount of capital that they can put into pushing the edge is small compared to Fortune 500. Sooner or later hobbyists will benefit, especially if the market crashes.
lacunary 2 hours ago [-]
also for ones fully into AI
I_am_tiberius 29 minutes ago [-]
It seems to me the max memory you can buy in a laptop stagnated for the past 3 years or so.
ffaccount2 10 minutes ago [-]
My several years old laptop has 128GB of RAM, is that not enough? I admit that it's a pretty heavy one.
grapedangackle 4 minutes ago [-]
[dead]
giancarlostoro 22 minutes ago [-]
I have always felt insulted that most laptops even offer a low 4 GB of RAM I rather take 16 GB in previous gen memory
KronisLV 1 hours ago [-]
I'm not moving past my DDR4 build (and the 32 GB of DDR4 2133 MHz backup chips I still have around from way back, before I got the current 3200 MHz ones) until the prices go back to being at least partially sane. This also means that CPU manufacturers are not getting my money (since the 5800X is fine for now) and I have no reason to get a new GPU either (though admittedly the B580 isn't perfect).
johnvanommen 54 minutes ago [-]
What if this is the lowest that prices will ever be?
skiing_crawling 1 hours ago [-]
I recently built a system at insane ddr4 prices ($2000 for 256gb). But that’s only after seeing how ddr5 prices were 3-4x that!
preisschild 1 hours ago [-]
Yeah I upgraded all of my systems to DDR5 last year, so now I have to buy for ddr5 memory upgrades.
Joel_Mckay 1 hours ago [-]
Had to fork over almost $1k for a 64G DDR5 kit a few weeks back. At least AMD chips large L3 cache allows folks to get away with lower grade udimms.
Also had to do an Intel build, and there was no way we were going cudimm at current prices. =3
Legend2440 1 hours ago [-]
I wonder why the hyperscalers aren't vertically integrating more and building their own fabs. Sure, a fab costs a billion dollars, but they're currently spending hundreds of billions of dollars purchasing chips from NVidia and others.
epistasis 1 hours ago [-]
I'm not sure if they should vertically integrate, it would probably be a better idea to directly fund the expansion of capacity, much like Apple does when they scale up a new technology for iPhones.
However, that the hyperscalers and AI companies aren't doing this says a lot about their true beliefs about how much future demand AI will have.
AI companies claim they will need a ton of massive expansion, but are unwilling to take on the risk of the capital needed for that expansion.
I'm hearing a lot of sad whining from AI folks about how these chip makers are holding them back, but who actually has the money to finance the expansion easily? Chip makers have been through this game far longer, when Sam Altman went around claiming it was time for $7T of fabs the AI companies made it clear that they were willing to make ridiculous claims, eliminating credibility.
What's needed now is for them to funnel a tiny amount of their massive piles of cash into financing fabs directly.
energy123 5 minutes ago [-]
Oracle is getting sold because of how much capex they're spending on new data centers in the middle of a high rates environment. It's not like they're stockpiling cash due to doubting AI.
nicoburns 17 minutes ago [-]
Because fabs are about the most complex cutting edge technology out there: the "rocket science" of our day (or one of them). And merely having the money is not sufficient. It would be very easy to blow several billion dollars and end up with nothing to show for it.
Just look at how Intel has struggled to compete in recent years, and they have been in the business for decades.
tjwebbnorfolk 12 minutes ago [-]
Intel struggled because they bet the company that Moore's law was over back in ~2014, and instead of upgrading their fabs to EUV they sent the money back to shareholders.
They forgot Moore's main lesson: only the paranoid survive. They thought they could coast, and it nearly killed them.
jacekm 34 minutes ago [-]
A fab takes years to build even when you have the necessary know-how. If you don't it'll take some additional experimenting before you can compete with the established manufacturers. By the time you can produce a usable chip the shortage might be over.
DoctorOetker 1 hours ago [-]
It's still unclear to me: the shortage is semiconductor boules / wafers? or the shortage is semiconductor fab process step availability?
As long as the discussion seems focused on memory, I'd suspect the latter, but if its really the semiconductor boules/wafers, then I'd expect the boule growers to profit, not the memory makers, who just pass on the cost.
So which is it?
AnotherGoodName 1 hours ago [-]
It’s fab capacity. Fwiw dram is different enough that fabs are not transferable between dram memory and other usages. It’s nice to think ‘wow if they made the current 10nm dram on the latest 2nm processes it’d be much faster’ but it doesn’t work that way. The specific size is needed for the capacitance. Sram can be made on fabs that make other circuitry since it’s transistor not capacitor based but is less dense.
And that article is contradicting other voices. If that article were correctly identifying the bottleneck as wafer shortage due to switching to HBM, why is everybody discussing the memory makers instead of the boule growers. Memory makers can expand operations all they can, which makes no sense if wafer supply doesn't follow, and the article is suspicously light on semiconductor boule / wafer mfr's.
So which is the bottleneck: fabs or boule growing?
also consider how most solar panels are monocrystalline silicon, how credible is silicon wafer shortage ... really? there is so much disinformation in this market...
chvid 1 hours ago [-]
Time to let ASML sell to the Chinese memory producers … or not.
MrGilbert 1 hours ago [-]
I assume that memory manufacturers don’t really care where the money is coming from, as long as the "numbers go up" game is working.
NVIDIA in their recent quarterly report stopped categorizing "Geforce" as a single category, and merged it into "Edge-Computing".
If you are a PC Gamer or PC Enthusiast as I am, then we have some dark times ahead.
reactordev 1 hours ago [-]
Do we though? DLSS 5 changes that somewhat from a “we need powah” to “we need models”. I think the future consumer GPU market will be tuned for image and world inference while workstation cards will be tuned for image and video inference. The old way of thinking about this will come to an end when we stop looking at the render loop as the be-all-end-all…
Or, we could be fucked.
kg 20 minutes ago [-]
If DLSS 5 becomes the norm it's possible that just makes things worse. The DLSS 5 demos required an entire separate card to run the model, though IIRC NVIDIA did claim it would eventually work on a single card. Given what the model is doing (yassifying the whole scene instead of just upscaling/reconstructing) it makes sense to me that it would increase compute demand instead of reduce it like previous versions of DLSS.
TheGrassyKnoll 1 hours ago [-]
I wish I had figured that out a year ago. MU up ~10x, SNDK up ~37x. My crystal ball is woefully under performing.
alasdairnicol 21 minutes ago [-]
C
amazingamazing 1 hours ago [-]
A commodity rapidly increasing in price. What could go wrong?
positron26 2 hours ago [-]
The algorithm advances are going to crash this so hard.
Legend2440 2 hours ago [-]
Or will more efficient algorithms just mean we run even more AI models, increasing the demand for AI chips even more?
Coffeewine 2 hours ago [-]
I mean, god willing, but it'll be just as likely that we'll blissfully consume 100 million token contexts in that case.
iamtheworstdev 2 hours ago [-]
isn't there a law for that? as things become cheaper you consume more?
sobellian 1 hours ago [-]
You're probably thinking about jevons paradox. But you slightly mis-stated. It is the phenomenon that increasing the efficiency of resource consumption can end up increasing total consumption.
As you stated it, it would merely be a property of (nearly) all demand curves. Jevons paradox only happens sometimes. It isn't a law.
dangus 1 hours ago [-]
An example of where it stopped happening is with gasoline in developed countries. Cars having better fuel efficiency doesn’t make me drive further to the grocery store or work.
Generally when someone replaces their vehicle the new one is more fuel efficient than the old one even if I bought the same car.
Why were tech savy investors unable to figure this out when the datacenter craze had already started?
How to explain this lag between quickly rising demand for all datacenter components besides memory?
johnvanommen 1 hours ago [-]
Nine years after Google's seminal paper lit the fuse on AI, a total lack of manufacturing foresight has trapped over a trillion dollars of incoming capital in a hardware bottleneck.
The entire sector is now facing a critical RAM starvation crisis where memory manufacturers are actively slow-rolling supply just to keep prices high and avoid running out entirely.
This has created an unprecedented supply-and-demand distortion where desperate companies are getting rejected even at a 5x markup, and mission-critical SKUs are skyrocketing to 10x and 20x their baseline value.
It is a macroeconomic squeeze at a staggering scale, and the massive venture scale opportunity lies in capturing the value created by this memory gatekeeper.
From the perspective of an armchair economist, the winners will be the investors who invest in RAM wisely. The losers will likely be cash strapped SAAS companies. They’re almost completely dependent on a fleet of servers in the hyperscalers, and they’re leasing those servers and services. That leaves small SAAS companies exposed to incoming inflation in the cost of hosting.
chairmansteve 7 minutes ago [-]
"That leaves small SAAS companies exposed to incoming inflation in the cost of hosting".
Which they will pass on to their customers. If their product provides enough value the customers will pay.....
vb-8448 33 minutes ago [-]
Capex expenditure start exploding after covid with the chart going hockey stick at the end of 23/start of 24, almost 2.5 years ago.
A lot of capex is supposed to go into the datacentres, didn't they know that datacentres need to be filled among other stuff with RAM? I wonder if at some point we will discover that there is a shortage of fibre optic cables of SFPs ...
PS: Obviously armchair economist here too ... but for it doesn't seem too difficult to foresee the increase of the demand.
irthomasthomas 39 minutes ago [-]
A lot of words to say that Sam Altman bought up the worlds total supply of ram chips for the next few years.
skybrian 49 minutes ago [-]
RAM is a boom-and-bust industry, so memory manufacturers were reluctant to invest. Here's a good blog post on the economics:
Maybe long-term purchase agreements from big buyers might have helped convince them it's okay to build, but apparently it didn't happen.
LPisGood 42 minutes ago [-]
The same reason they didn’t all sell everything to buy NVIDIA the day chatGPT came out
brcmthrowaway 1 hours ago [-]
Anyone invested in Micron stock?
lostlogin 7 minutes ago [-]
Up 700% in a year.
WallstreeetBets has been disturbingly accurate in its predictions - basically anything related to AI.
deadbabe 2 hours ago [-]
Here’s the thing, what if memory manufacturers take this opportunity to collude and basically never reduce the price of memory below the current levels since it’s too hard for a new competitor to just rise up and undercut them? Everything I hear about is how hard and risky it is to spin up a new fab.
And by doing this, they ensure local LLMs never become feasible for the vast majority of people and AI companies solidify subscriptions forever.
aurareturn 1 hours ago [-]
Keeping prices at this level is precisely how one or more competitor will rise up. Making memory isn’t super hard. That’s why it is a commodity. The problem with the memory market is that up and down cycles have bankrupted the vast majority of players in the past. Now we only have 3 players left except for a few smaller ones in China.
The reason memory prices can stay high for years in this mega cycle is because the 3 players will be very cautious on overbuilding. They’d rather under build, make great profit (not maximum) and reduce the risk of going bust if this suddenly ends.
Same for TSMC in chips.
Great opportunity for Chinese companies though. This shortage is exactly what Chinese companies need to scale.
dymk 1 hours ago [-]
> Making memory isn’t super hard.
Then why do only 3 companies make it?
aurareturn 1 hours ago [-]
Bankruptcy risks.
When Samsung had to sell memory at a loss after COVID, no one came to save them. They buffered their memory division using profits from their other businesses. That’s how Samsung survives memory downturns.
According to some stories, this is how Samsung convinced TSMC to not enter the memory business - that you need a nation or other lines of business to prevent bankruptcies.
The market has stabilized to 3 players.
dymk 1 hours ago [-]
...And why do they go bankrupt?
Because it's an incredibly capital intensive process, involving billions of dollars of investment into manufacturing infrastructure.
That is to say, making memory is quite hard.
aurareturn 59 minutes ago [-]
The technical process of making memory is relatively easy. Hence, it is a commodity.
I didn’t say owning a memory business is easy.
kortilla 32 minutes ago [-]
You’re confusing two independent things. There are simple processes that are extremely capital intensive with long lead times and then there are complex processes that require lots of R&D and industry secrets. Memory is the former in the chip world.
Other examples from outside of tech of easy but capital intensive processes are power generation and railroads. Very easy to do, but easy to end up broken by overbuilding for demand that fails to materialize or stay stable for the duration of your financing.
DoctorOetker 1 hours ago [-]
Making the memory can be much easier than predicting future demand.
Placing the bet isn't as hard as making an accurate prediction.
jazzyjackson 1 hours ago [-]
> up and down cycles have bankrupted the vast majority of players in the past
Exactly, so what’s the incentive for anyone to sink half a billy into building out more capacity.
The existing players get to rest on their laurels and succeed whether or not the AI bubble busts.
aurareturn 1 hours ago [-]
The incentive is that your 2 competitors will build more than you and gain market share on you if you are too conservative.
Samsung, SK Hynix, and Micron all have to balance between capex spending, making as much profit as possible, and risk of bankruptcy.
deadbabe 1 hours ago [-]
So are the new competitors currently in progress of starting up? Because it takes at least several years.
aurareturn 1 hours ago [-]
Only Chinese companies have a chance. Problem is that China can’t buy EUV machines and the most advanced memory chips now use EUV.
Heck, the US is now pressuring ASML to not sell even DUV machines to China, period.
jtbayly 1 hours ago [-]
When costs are high enough, you can recoup that, if you have an appetite for risking the downturn.
1 hours ago [-]
YetAnotherNick 1 hours ago [-]
If the collude to say make the price $1000 for a component that costs them $100(including opportunity costs), then either a new company or a greedy company in the collusion can make their price secretly $900 and get massively more profit.
Right now their opportunity cost is too high.
> risky it is to spin up a new fab
You don't need a new fab. You can build memory in 20 years old fab.
stavros 1 hours ago [-]
Then that's a cartel and hopefully regulators will act.
deadbabe 1 hours ago [-]
They won’t.
aurareturn 1 hours ago [-]
They will. DOJ prosecuted memory makers in the late 90s and 2000s for collusion.
This boom is magnitudes higher than before. The attention will be endless.
deadbabe 1 hours ago [-]
Current DOJ is corrupt as fuck, it will not happen. Get back to reality.
kortilla 30 minutes ago [-]
Corrupt doesn’t mean “acts without incentives”. If you assume a corrupt system, then the inputs are going to be who has influence over the DOJ. If there is more money to be made by breaking a cartel, then they would absolutely do it.
aurareturn 1 hours ago [-]
They will respond when people are loud enough. If memory stays at $1200 for 128GB for years and investigative journalists say it could be colluding, enough people will make enough noise.
I’m sure Nvidia, Elon, Tim Cook, OpenAI, Anthropic are already whispering in Trump’s ears to do something.
BigTTYGothGF 32 minutes ago [-]
> I’m sure Nvidia, Elon, Tim Cook, OpenAI, Anthropic are already whispering in Trump’s ears to do something
You can't expect me to believe that any of those would want any kind of antitrust action against anybody.
aurareturn 20 minutes ago [-]
Sure they do. They all have money interests in this. They all want lower memory prices.
Memory prices and shortages directly impact all of their profit margins and revenue.
wahnfrieden 50 minutes ago [-]
Once the masses are disenfranchised network state serfs according to plan, loudness won't matter
CamperBob2 59 minutes ago [-]
That was a very different DOJ. They no longer work for us. They act as Trump's personal law firm.
shaky-carrousel 1 hours ago [-]
Then China will come and eat their lunch. I for one will only buy Chinese RAM from now on, no matter the prices.
granzymes 53 minutes ago [-]
>I for one will only buy Chinese RAM from now on, no matter the prices.
Memory is a commodity, so I think you will be very lonely in your quest.
sieabahlpark 1 hours ago [-]
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ck2 24 minutes ago [-]
if we survive the bubble bursting and there isn't a "too big to fail" bailout with public money manipulation by bought politicians
we are going to have amazing cheap used hardware for a decade
thinklanceai 14 minutes ago [-]
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Lapsa 45 minutes ago [-]
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Rendered at 18:58:35 GMT+0000 (Coordinated Universal Time) with Vercel.
Those are not normal pricing. Before the pricing collapse in early 2020, 96GB DDR5 would have cost about $450 to $500. And I will need to restate again the cost of DRAM hasn't really changed much in the past 20 years. Its price just goes up and down in cycles.
So in reality it is more like going from $500 to $1300. But consumer felt it was more like going from $200 to $1300.
Crucial are already selling DRAM made by CXMT. And China are already throwing money at it. I doubt the memory bubble will burst in next 12-24 months. As in going back to money losing DRAM pricing. As they will all pivot to HBM or other money making products. But the bulk of lower end consumer DDR5 or LPDDR5 will goes to Chinese Foundry. Assuming they have figure out how to do them well. Which they have improved but are still so far away from industry leaders.
Normally memory maker will push the next DDR standard to market just to push out Chinese competitors, I am not sure it will work the same this time around. DDR5 have plenty of other usage / demands.
Crucial was disestablished this year.
I don't see it going away. I mean, it may not grow as fast as now, but I don't see it growing away either. I get why the memory makers do not want to bankrupt themselves, but it feels like there's got to be some way to push that risk off onto model providers and other people in the ecosystem to allow us to grow ram capacity more like 50% per year.
What if its in everyone's interest to buy computers at say 1/3rd the rate and switch everything over to HBM?
the discrepancy between compute and memory has been growing for ages, perhaps a painful switch to HBM is exactly what we need?
Would you rather have 3 intermediate computers with low memory bandwidth, or wait a little longer statistically so that we can all enjoy a new computer at 1/3rd the rate but much higher bandwidth than the area ratio?
As for 20-25% growth not being enough, I think it's not that far off, if we assume data center build out plans hit a wall and slow down significantly, and the AI heat starts to cool off.
I don't think 20-25% may be enough in the short term but if the AI build out stops within this year, we have a massive oversupply instead of a under supply.
But can massive gains still be made? Definitely.
The entire AI hype is based on the paper Attention is all you need, and Attention is basically loading a huge matrix of all the tokens in memory, how well you can optimize this attention layer is basically how most architectures are trying to solve for performance and memory usage.
Only one with significant gains in it is DeepSeek (or so I would like to believe because others don't make their work open for folks like me not in Big AI Labs to read). Their MLA architecture reduced KV-cache memory requirements by upto 90%, ofc that's purely architectural change.
With some quantization like Turboquant from google you could push it down to ~1/3 of that. So 96% memory savings when talking about kv-cache.
But the models are close to being saturated for quantization based memory optimizations. We will have to see some architectural changes for a significant shift now.
We just haven’t reached the diminishing return of gen AI capabilities yet.
Models will get more useful if you have higher context size or higher param size. Then people will just use the models even more, leading to even more memory demand.
The VRAM in the 5090 is only made by one country in the world.
The 50xx series is special, because its ram is so dependent on a single commodity. It’s not like a 4090 or a 3090; their VRAM chips have been around for years.
If there’s a shortage or interruption in DDR7 VRAM, it seems like every GPU that requires it would explode in value.
I hope I don’t regret posting this because I’d really like to buy one myself…
I really need to shut up, or bite the bullet and by one.
If you graph the tokens per second on the 5090, your jaw will hit the floor at how cheap it is
The RTX 5090 is faster than an H200. It just has less ram (32 vs 141), doesn't have NVLink, and technically isn't allowed to be used in a datacenter.
The datacenter GPUs sell at an 80% margin. They're incredibly overpriced. But the laws of supply and demand are undefeated and so here we all are.
H200 has HBM and much more 64-bit compute
RTX 5090 has more CUDA cores that run at a higher clock speed. H200 has more RAM and significantly more RAM bandwidth.
Which one is net faster depends on your use case. But you may be very surprised that many workflows are faster on an RTX 5090!
Most users don't seem to care about storing everything they generate in cloud services and this could easily be sold as an alternative to owning "expensive" desktop or laptop hardware.
People used to get into gaming pcs as an affordable hobby, now it’s making general aviation look like plan B.
They've intentionally crafted an unsustainable business model in an effort to get users in the front door and raise their MAUs. We've seen this story before. We should know precisely where it's headed.
https://www.youtube.com/@GamersNexus/videos
Also had to do an Intel build, and there was no way we were going cudimm at current prices. =3
However, that the hyperscalers and AI companies aren't doing this says a lot about their true beliefs about how much future demand AI will have.
AI companies claim they will need a ton of massive expansion, but are unwilling to take on the risk of the capital needed for that expansion.
I'm hearing a lot of sad whining from AI folks about how these chip makers are holding them back, but who actually has the money to finance the expansion easily? Chip makers have been through this game far longer, when Sam Altman went around claiming it was time for $7T of fabs the AI companies made it clear that they were willing to make ridiculous claims, eliminating credibility.
What's needed now is for them to funnel a tiny amount of their massive piles of cash into financing fabs directly.
Just look at how Intel has struggled to compete in recent years, and they have been in the business for decades.
They forgot Moore's main lesson: only the paranoid survive. They thought they could coast, and it nearly killed them.
As long as the discussion seems focused on memory, I'd suspect the latter, but if its really the semiconductor boules/wafers, then I'd expect the boule growers to profit, not the memory makers, who just pass on the cost.
So which is it?
Dram is just extremely specialised.
So which is the bottleneck: fabs or boule growing?
also consider how most solar panels are monocrystalline silicon, how credible is silicon wafer shortage ... really? there is so much disinformation in this market...
NVIDIA in their recent quarterly report stopped categorizing "Geforce" as a single category, and merged it into "Edge-Computing".
If you are a PC Gamer or PC Enthusiast as I am, then we have some dark times ahead.
Or, we could be fucked.
As you stated it, it would merely be a property of (nearly) all demand curves. Jevons paradox only happens sometimes. It isn't a law.
Generally when someone replaces their vehicle the new one is more fuel efficient than the old one even if I bought the same car.
https://en.wikipedia.org/wiki/Jevons_paradox
Why were tech savy investors unable to figure this out when the datacenter craze had already started?
How to explain this lag between quickly rising demand for all datacenter components besides memory?
The entire sector is now facing a critical RAM starvation crisis where memory manufacturers are actively slow-rolling supply just to keep prices high and avoid running out entirely.
This has created an unprecedented supply-and-demand distortion where desperate companies are getting rejected even at a 5x markup, and mission-critical SKUs are skyrocketing to 10x and 20x their baseline value.
It is a macroeconomic squeeze at a staggering scale, and the massive venture scale opportunity lies in capturing the value created by this memory gatekeeper.
From the perspective of an armchair economist, the winners will be the investors who invest in RAM wisely. The losers will likely be cash strapped SAAS companies. They’re almost completely dependent on a fleet of servers in the hyperscalers, and they’re leasing those servers and services. That leaves small SAAS companies exposed to incoming inflation in the cost of hosting.
Which they will pass on to their customers. If their product provides enough value the customers will pay.....
A lot of capex is supposed to go into the datacentres, didn't they know that datacentres need to be filled among other stuff with RAM? I wonder if at some point we will discover that there is a shortage of fibre optic cables of SFPs ...
PS: Obviously armchair economist here too ... but for it doesn't seem too difficult to foresee the increase of the demand.
https://davidoks.blog/p/ai-is-killing-the-cheap-smartphone
Maybe long-term purchase agreements from big buyers might have helped convince them it's okay to build, but apparently it didn't happen.
WallstreeetBets has been disturbingly accurate in its predictions - basically anything related to AI.
And by doing this, they ensure local LLMs never become feasible for the vast majority of people and AI companies solidify subscriptions forever.
The reason memory prices can stay high for years in this mega cycle is because the 3 players will be very cautious on overbuilding. They’d rather under build, make great profit (not maximum) and reduce the risk of going bust if this suddenly ends.
Same for TSMC in chips.
Great opportunity for Chinese companies though. This shortage is exactly what Chinese companies need to scale.
Then why do only 3 companies make it?
When Samsung had to sell memory at a loss after COVID, no one came to save them. They buffered their memory division using profits from their other businesses. That’s how Samsung survives memory downturns.
According to some stories, this is how Samsung convinced TSMC to not enter the memory business - that you need a nation or other lines of business to prevent bankruptcies.
The market has stabilized to 3 players.
Because it's an incredibly capital intensive process, involving billions of dollars of investment into manufacturing infrastructure.
That is to say, making memory is quite hard.
I didn’t say owning a memory business is easy.
Other examples from outside of tech of easy but capital intensive processes are power generation and railroads. Very easy to do, but easy to end up broken by overbuilding for demand that fails to materialize or stay stable for the duration of your financing.
Placing the bet isn't as hard as making an accurate prediction.
Exactly, so what’s the incentive for anyone to sink half a billy into building out more capacity.
The existing players get to rest on their laurels and succeed whether or not the AI bubble busts.
Samsung, SK Hynix, and Micron all have to balance between capex spending, making as much profit as possible, and risk of bankruptcy.
Heck, the US is now pressuring ASML to not sell even DUV machines to China, period.
Right now their opportunity cost is too high.
> risky it is to spin up a new fab
You don't need a new fab. You can build memory in 20 years old fab.
This boom is magnitudes higher than before. The attention will be endless.
I’m sure Nvidia, Elon, Tim Cook, OpenAI, Anthropic are already whispering in Trump’s ears to do something.
You can't expect me to believe that any of those would want any kind of antitrust action against anybody.
Memory prices and shortages directly impact all of their profit margins and revenue.
Memory is a commodity, so I think you will be very lonely in your quest.
we are going to have amazing cheap used hardware for a decade