The RAM shortage could last years
theverge.com
theverge.com
It doesn't stop there though. OpenAI is currently mired in a capital crunch. Their last round just about sucked all the dry powder out of the private markets. Folks are now starting to ask difficult questions about their burn rate and revenue. It is increasingly looking like they might not commit to the purchase order they made which kick-started this whole panic over RAM.
Soo ... how sure are we that the memory makers themselves are not going to be the ones holding the bag?
If they could make this stuff and sell it to regular people a decade ago for very palatable prices, why do they come up with the idea that this is the technology of the gods, unaffordable by mere mortals?
Heck, I have a phone with a 16bit memory bus for instance. The high(ish) clock rate only makes up the difference slightly.
But with general prices on all components going up, it might not be such a big factor any more.
HBM migght make sense for higher end products which can free up space for the lower end that will never use the tech.
Designing a part with a wide bus and putting the traces down on the board is what I would expect to be the easy part these days (surely).
But yield, yield comes for us all.
because the gods want it all and are willing to pay top dollar.
I wonder whether this is some kind of a racket.
The GB202 die that's in the GDDR7 based RTX 5090 and RTX 6000 Pro literally needed to be this big to support the 512bit memory bus. It's probably only getting worse with smaller node sizes. (see https://www.youtube.com/watch?v=rCwgAGG2sZQ&t=65s).
BTW: The 1TB/s is matched by RTX4090 and surpassed by the RTX5090 (1,79 TB/s).
5090 has 1.8 TB/s?
Even a RTX 5080 has a lower memory throughput than a Radeon VII from 2019, 7 years ago, while being much more expensive.
The memory throughput of GPUs per dollar has regressed greatly during the last 5 years, despite the fact that the widths of the GPU memory interfaces have been reduced, in order to decrease the production costs.
RTX 5080 has a 256-bit memory interface, while the much cheaper Radeon VII had an 1024-bit memory interface. RTX 5080 has almost 4-times faster memories than Radeon VII, but it has not used this to increase the memory throughput, but only to reduce the production costs, while simultaneously increasing the product price.
I'd absolutely buy another hbm consumer GPU if it had at least 8gb (and if I got the vibe/hope AMD will actually support for a couple years...)
AMD Hawaii GPUs still had 1:2 FP64:FP32, while the consumer variant of Radeon VII dropped to 1:4. The following AMD consumer GPUs dropped the FP64 performance to levels that are not competitive with CPUs.
Nowadays the only consumer GPUs with decent FP64 performance are the Intel Battlemage GPUs, which have a 1:8 performance ratio, which provides very good performance per dollar.
That is, memory capacity is reserved for datacenters yet to be built, but this will do weird things if said datacenter construction is postponed or cancelled altogether.
It says that in 2025, Netherlands was a net exporter of electricity (~14,000 GWh). My guess: Where they want to build data centers, the grid cannot handle it, but the overall system has more than enough power to build data centers. Do you think that sounds like a resonable guess?
Are the Netherlands a large proportion of global datacenters?
Had we done more 10 years ago we would have been better of. The second best time to start is now.
The problem in the EU is not renewables, it's the same problem that Democratic states in the US face. Regulations and permitting hurdles that block private renewable energy developers.
Do recent actions of Open AI give you the impression of a company that believes it is about to attain AGI imminently?
We've been projecting both FTL and AGI as future possibilities for almost 100 years now. Do LLMs get us a lot closer to AGI? I think they get us a little closer and Moore's "law" making compute faster probably is a much bigger factor, but I think we're still a very very long ways away.
I am not sure if OpenAI has that. Their edge regarding models is small, their strategy currently seems to be "buy ALL the hardware so nobody else can". Users can quite easily switch to other models.
(Well that and collusion)
If they had actually been communicating or colluding with each other, they would have put the screws to him, making it harder for OpenAI to assert control over the vast majority of the DRAM market.
Failing that, you'd like to think a regulatory agency somewhere would step in to keep a single player from hosing everybody else, but...
You get market signals that the demand is there, you acquire the necessary capital, you spend 5 years to build capacity, but guess what, 5 other market players did the same thing. So now you are doomed, because the market is flooded and you have low cash flow since you need to drop prices to compete for pennies.
Now you cannot find capital, you don't invest, but guess what, neither your competitors did. So now the demand is higher than the supply. Your price per unit sold skyrocketed, but you don't have enough capacity!
Rinse and repeat.
Capitalists claim that this is optimal.
If anything, it shows it's possible for you to arbitrage this and in doing so help "smooth out the cycle."
At least with capitalism you have many different people with different perspectives on the risk making independent bets. That mitigates the more extreme negative outcomes.
Because that does not happen exactly as you say for all players. The demand signals will be processed and long-term risk is balanced against short-term gain in a distributed fashion, so not everyone will do the same.
It's more optimal than planned economies until we have AI planned economies with realtime feedback, I guess.
Consumers get cheap goods during oversupply and most inefficient companies get elliminated during bust while consolidation leads to economies of scale.
That's not what capitalists claim. Capitalists claim communism is responsible for tens, if not hundreds, of millions of death due to famine. And overall miserable ways of life, as if humans were termites deprived of any individualism and any freedom.
Capitalists claim that France importing 500 000 people from the third world each year, out of which only 10% are ever going to work (and these are official numbers) and yet offering them all a safety net is unsustainable. And that that socialism is only going to lead to one thing: running out of taxpayers' money.
Capitalists don't claim they have the best system: what they claim is that they haven't seen a less worse one.
It is unlike a socialist system in that there are signals to read in the first place. What, do socialists claim that failure is optimal when you can't even tell if you've failed?
> Capitalists claim that this is optimal.
Compared to starving under communism coz someone at top got the number wrong, yes. And it only really happens when there are massive, unpredictable market movements and governments not doing their job. Govt should look at the whole thing and just say "no", blame them.
No market system self regulates well enough, and it's government job to file down the edge cases like this. But the revolution happened in country which has two utterly incompetent parties, both in pockets of billionaires, fighting for power, and the clowns from one that won last battle use AI to smokescreen the economic growth their actions cratered
The memory makers specifically did not scale up capacity to avoid being left holding the bag.
Wasn't the problem here that OpenAI was negotiating with Samsung and SK Hynix at the same time without the other one knowing about it? People only realized the implications when they announced both deals at once.
I hope they do, they did not have to agree to sell so much RAM to one customer. They’ve been caught colluding and price fixing more than once, I hope they take it in the shorts and new competitors arise or they go bankrupt and new management takes over the existing plants.
Don’t put all your eggs in the one basket is how the old saying goes.
We aren't. The remaining memory manufacturers fear getting caught in a "pork cycle" yet again - that is why there's only the three large ones left anyway.
China has memory makers who are creeping up through the stages of production maturity, and once they hit then there's no going back.
If the existing makers can't meet supply such that Chinese exports get their foot in the door, they may find they never get ahead again due to volume - that domestic market is huge so they have scale, and the gaming market isn't going to care because they get anything at the moment, which is all you'll need for enterprise to say "are we really afraid of memory in this business?"
The real issue is everyone wanting to upgrade to hbm, ddr5, and nvme5 at the same time.
Very few applications other than GPUs need HBM.
At least as I understand it.
It's worse. HBM have lower yields so they are essentially making less GB per wafer too
The specific mix of factors could change at any time, but the supply chain is relatively inelastic, it will take some time to show up on price labels.
this view isn't updated correctly post-claude code and codex. there will clearly be sufficient demand.
Which also explains why production is falling behind demand, companies aren't going to sink billions into creating product for a market that could dry up overnight.
OpenAI (or whoever) crashes and can't pay for the order leaving the memory makers in a tough spot.
Oh noes! Think of a poor memory makers!
The amount of money flowing both from the AI bubble and from quite literally scalping both the server and consumer market... They gambled on the opportunity and if they fail - it's their problem.
The others...they did not. Memory makers won't be holding the bag because Apple/Google/Samsung are contractually obligated to purchase, after the panic OAI caused.
Edit: also, that demand pressure is going to be applied constantly; there isn’t going to be a shock, it’s just going to keep prices high longer.
Are they really such a big RAM buyer?
> OpenAI’s rapid growth, fueled by the success of ChatGPT and other AI products, led to a landmark agreement in October to purchase 900,000 DRAM wafers per month from Samsung and SK Hynix—amounting to roughly 40% of global supply. This surge in demand, coupled with limited manufacturing capacity, sent prices for memory kits skyrocketing. [0]
[0]: https://peq42.com/blog/openai-canceling-many-large-purchase-...
If I booked half a hotel's rooms then suddenly said "yeah never mind. Half my friends cancelled and we're not staying", basically any hotel would be coming at me for my money because there's no way they can fill their rooms now and they're losing revenue. But OpenAI can really get the whole world to pivot towards it then say "cool but we don't need your product anymore" and RAM makers are just going to let it go.
Whoever decided that was a good idea needs to be fired and publicly shamed.
The customer ran out of money. In terms of where you are in line of debtors when you haven't even delivered the product to a customer, it's so far back as to be assured you won't get your money.
If the memory makers got a deposit from OpenAI as part of this deal, that is likely to be the only money they will get for any undelivered memory, particularly if OpenAI runs out of capital.
They act as a de-facto monopoly and milk us. Why is this allowed?
Nobody is "allowing" this. It's a natural property of being both advanced technology and a commodity at the same time.
Recently they had a second price fixing lawsuit thrown out (in the US).
Now with the state of things I'm sure another lawsuit will arrive and be thrown out because the government will do anything to keep the AI bubble rolling and a price fixing suit will be a threat to national security, somehow. Obviously thats speculative and opinion but to be clear, people are allowing it. There are and more so were things that could be done.
It started with raegan, and even parties on the “left” in the west believe in it with very few exceptions.
The thing that enables this is pretty obvious. The population is divided into two camps, the first of which holds the heuristic that regulations are "communism and totalitarianism" and this camp is used to prevent e.g. antitrust rules/enforcement. The second camp holds the heuristic that companies need to be aggressively "regulated" and this camp is used to create/sustain rules making it harder to enter the market.
The problem is that ordinary people don't have the resources to dive into the details of any given proposal but the companies do. So what we need is a simple heuristic for ordinary people to distinguish them: Make the majority of "regulations" apply only to companies with more than 20% market share. No one is allowed to dump industrial waste in the river but only dominant companies have bureaucratic reporting requirements etc. Allow private lawsuits against dominant companies for certain offenses but only government-initiated prosecutions against smaller ones, the latter preventing incumbents from miring new challengers in litigation and requiring proof beyond a reasonable doubt.
This even makes logical sense, because most of the rules are attempts to mitigate an uncompetitive market, so applying them to new entrants or markets with >5 competitors is more likely to be deleterious, i.e. drive further consolidation. Whereas if the market is already consolidated then the thicket of rules constrains the incumbents from abusing their dominance in the uncompetitive market while encouraging new entrants who are below the threshold.
Oh no!
There’s virtually infinite capital: if needed, more can be reallocated from the federal government (funded with debt), from public companies (funded with people’s retirement funds), from people’s pockets via wealth redistribution upwards, from offshore investment.
They will be allowed to strangle any part of the supply chain they want.
Another point is I often see the money argument - like country X has more money, so they can afford to do more and better R&D, make more stuff.
This stuff comes out of factories, that need to be built, the machinery procured, engineers trained and hired.
> more can be reallocated from the federal government (funded with debt)
While this is the most reliable funding, it's still not very accessible. OpenAI is a money pit, and their demands are growing quickly. The US government has started a bunch of very expensive spending. If OpenAI were to require yearly bundles of it's recent "$120B" deal, that's 6% of the US' discretionary budget. 12.5% of the non-military discretionary budget. (And the military is going to ask for a lot more money this year) Even the idea of just issuing more debt is dubious because they're going to want to do that to pay for the wars that are rapidly spiralling out of control.
None of this is saying that the US government can't or wouldn't pay for it, but it's non trivial and it's unclear how much Altman can threaten the US government "give me a trillion dollars or the economy explodes" without consequences.
Further deficit-spending isn't without it's risks for the US government either. Interests rates are already creeping up, and a careless explosion of deficit may well trigger a debt crisis.
> from public companies (funded with people’s retirement funds)
This would be at great cost. OpenAI would need to open up about it's financial performance to go public itself. With it's CFO being put on what is effectively Administrative Leave for pushing against going public, we can assume the financials are so catastrophic an IPO might bomb and take the company down with it. Nobody's going to be investing privately in a company that has no public takers.
Getting money through other companies is also running into limits. Big Tech has deep pockets but they've already started slowing down, switching to debt to finance AI investment, and similarly are increasingly pressured by their own shareholders to show results.
> from people’s pockets via wealth redistribution upwards
The practical mechanism of this is "AI companies raise their prices". That might also just crash the bubble if demand evaporates. For all the hype, the productivity benefit hasn't really shown up in economy-wide aggregates. The moment AI becomes "expensive", all the casual users will drop it. And the non-casual users are likely to follow. The idea of "AI tokens" as a job perk is cute, but exceedingly few are going to accept lower salary in order to use AI at their job.
There's simply not much money to take out of people's pockets these days, with how high cost of living has gotten.
> from offshore investment.
This is a pretty good source of money. The wealthy Arabian oil states have very deep slush funds, extensively investing in AI to get ties to US businesses and in the hope of diversifying their resource economies.
...
...
"Was". Was a good source of money.
Given that TurboQuant results in a 6x reduction in memory usage for KV caches and up to 8x boost in speed, this optimization is already showing up in llama.cpp, enabling significantly bigger contexts without having to run a smaller model to fit it all in memory.
Some people thought it might significantly improve the RAM situation, though I remain a bit skeptical - the demand is probably still larger than the reduction turboquant brings.
> Given that TurboQuant results in a 6x reduction in memory usage for KV caches
All depends on baseline. The "6x" is by stylistic comparison to a BF16 KV cache; not a state of the art 8 or 4 bit KV cache scheme.
Current "TurboQuant" implementations are about 3.8X-4.9X on compression (w/ the higher end taking some significant hits of GSM8K performance) and with about 80-100% baseline speed (no improvement, regression): https://github.com/vllm-project/vllm/pull/38479
For those not paying attention, it's probably worth sending this and ongoing discussion for vLLM https://github.com/vllm-project/vllm/issues/38171 and llama.cpp through your summarizer of choice - TurboQuant is fine, but not a magic bullet. Personally, I've been experimenting with DMS and I think it has a lot more promise and can be stacked with various quantization schemes.
The biggest savings in kvcache though is in improved model architecture. Gemma 4's SWA/global hybrid saves up to 10X kvcache, MLA/DSA (the latter that helps solve global attention compute) does as well, and using linear, SSM layers saves even more.
None of these reduce memory demand (Jevon's paradox, etc), though. Looking at my coding tools, I'm using about 10-15B cached tokens/mo currently (was 5-8B a couple months ago) and while I think I'm probably above average on the curve, I don't consider myself doing anything especially crazy and this year, between mainstream developers, and more and more agents, I don't think there's really any limit to the number of tokens that people will want to consume.
For example Gemma 4 32B, which you can run on an off-the-shelf laptop, is around the same or even higher intelligence level as the SOTA models from 2 years ago (e.g. gpt-4o). Probably by the time memory prices come down we will have something as smart as Opus 4.7 that can be run locally.
Bigger models of course have more embedded knowledge, but just knowing that they should make a tool call to do a web search can bypass a lot of that.
That is the sad reality of the future of memory.
Given the current tech, I also doubt there will be practical uses and I hope we’ll see the opposite of what I wrote. But given the current industry, I fully trust them so somehow fill their hardware.
Market history shows us than when the cost of something goes down, we do more with the same amount, not the same thing with less. But I deeply hope to be wrong here and the memory market will relax.
mind that you're quoting marketing material that's largely based on unfair baseline testing (like comparing 4 bit vs 32 bit to get "8x speed")
I hate to mention Jevons paradox as it has become cliche by now, but this is a textbook such scenario
RAM is built on a foundation of sand.
Claude Max subscriptions have gone up, but do you think every Netflix user will pay for one?..
https://www.tomshardware.com/tech-industry/artificial-intell...
the hope is that Ai is "the next semiconductor" and "the next internet"
Supposedly AI drives down the cost of producing software,not the "price".
> How are software companies going to make enough revenue to pay for AI, when the amount of money being spent on AI is already multiples of the current total global expenditure on software?
Currently, the cost of AI is between $20/month and around $200/month per developer.
I think the huge billions you're seeing in the news are the investment cost on AI companies, who are burning through cash to invest in compute infrastructure to allow both training and serving users.
> This demand for RAM is built on a foundation of sand, there will be a glut of capacity when it all shakes out.
Who knows? What I know is that I need >64GB of RAM to run local models, and that means most people will need to upgrade from their 8Gb/16GB setup to do the same. Graphics cards follow mostly the same pattern.
You can run huge local models slowly with the weights stored on SSDs.
Nowadays there are many computers that can have e.g. 2 PCIe 5.0 SSDs, which allow a reading throughput of 20 to 30 gigabyte per second, depending on the SSDs (or 1 PCIe 5.0 + 1 PCIe 4.0, for a throughput in the range 15-20 GB/s).
There are still a lot of improvements that can be done to inference back-ends like llama.cpp to reach the inference speed limit determined by the SSD throughput.
It seems that it is possible to reach inference speed in the range from a few seconds per token to a few tokens per second.
That may be too slow for a chat, but it should be good enough for an AI coding assistant, especially if many tasks are batched, so that they can progress simultaneously during a single read pass over the SSD data.
Batching inferences doesn't necessarily help that much since as models get sparser the individual inferences are going to share fewer experts. It does always help wrt. shared routing layers, of course.
Depends how big the models are, how fast you want them to run and how much context you need for your usage. If you're okay with running only smaller models (which are still very capable in general, their main limitation is world knowledge) making very simple inferences at low overall throughput, you can just repurpose the RAM, CPUs/iGPUs and storage in the average setup.
Not exactly.
LLMs are already quite useful today if you use them as a tool, so they are there to stay. The remaining problem is scalability, a.k.a. how to make LLMs cheap to use.
But scalability is not really a requirement when you look the bigger picture. If smaller software company/projects can't afford to use AI, the bigger ones might just. Eventually they will discover variable use cases for such tech, even if it only serves big firms i.e. defense, resource extraction, war, finance etc.
To the other end, if scalability is achieved, the use of LLM products will be cheaper too, so smaller project can also use them. But of course, if LLM usage is too cheap, then many were-to-be-consumers will just create software projects by themselves at their homes.
The advanced workflows such as programming are not the only use cases for LLMs. There are much simpler work such as answering simple questions or arrange simple tasks. Both will improve productivity. And more advanced model should improve productivity even more.
I would like a source for that statement. Additionally, I want to know by who? Because it certainly isn't end users. Inflating token usage doesn't make it any more economically viable if your user base, b2b or not, hasn't increased with it. On the contrary, that is a worse scenario for providers.
1. As a consultant pretty much every company I have worked with in the last 2 years are doing some kind of in-house "AI Revolution", I'm talking making "AI Taskforce" teams, having weekly internal "AI meetings" and pushing AI everywhere and to everyone. Small companies, SMEs and huge companies. From my observation it is mainly due to C-level being obsessed by the idea that AI will replace/uplift people and revenue will grow by either replacing people or launching features 10x quicker.
2. Did you see software job-boards recently? 9/10 (real) job listings are to do with AI. Either it is fully AI company (99% thin wrapper over Anthropic/OpenAI APIs) or some other SME that needs some AI implementations done. It is truly a breath of fresh air to work for companies that have nothing to do with AI.
The biggest laugh/cry for me are those thin wrappers that go down overnight - think all the "create your website" companies that are now completely useless since Ahtropic cut the middleman and created their own version of exactly that.
I know plenty of engineers being forced to use these tools whether they want to or not. A lot of which are okay with using AI liberally, but don't particularly like generative AI and see it as pretty irresponsible (which feels more true by the week and it is clear from first hand experience). I don't know, there is a huge gradient of users, but I would argue that in previous revolutionary technologies, we didn't have to force people to use a good tool. I didn't have to be forced to use Google search or Google Maps, tech that is now ubiquitous with western society. It seems really suspect that suits have to enforce the use of something that is supposed to change the way we work and be a force multiplier.
The recent enterprise revenue numbers of Anthropic
And hopefully kill Electron.
I have never seen the point of spinning up a 300+Mb app just to display something that ought to need only 500Kb to paint onto the screen.
Anyways, I'm both cases you don't really have to write it twice.
Native to the OS: write only the UI twice, but implement the Core in Rust.
Native to the machine: Write it only once, e.g. in iced, and compile it for every Plattform.
It's bad enough having to run one boated browser, now we have to run multiples?
This is not the right path.
Now that everyone who cant be bothered, vibe codes, and electron apps are the overevangelized norm… People will probably not even worry about writing js and electron will be here to stay. The only way out is to evangelize something else.
Like how half the websites have giant in your face cookie banners and half have minimalist banners. The experience will still suck for the end user because the dev doesnt care and neither do the business leaders.
About the only thing they share is curly braces.
This comment makes no sense.
There ought to be a short one-liner that anyone can run to get easily installable "binaries" for their PyQt app for all major platforms. But there isn't, you have to dig up some blog post with 3 config files and a 10 argument incantation and follow it (and every blog post has a different one) when you just wanted to spend 10 minutes writing some code to solve your problem (which is how every good program gets started). So we're stuck with Electron.
and if not?
Then again, after many, many years of claims that the following year would be the year of the Linux Desktop, there seems to be more and more of a push into that direction. Or at least into a significant increase in market share. We can thank a current head of state for that.
At a cost of simplicity and beauty. And two lost decades of mediocre performance. Sigh
Ton of software out there where optimisation of both memory and cpu has been pushed to the side because development hours is more costly than a bit of extra resource usage.
[0] https://techwireasia.com/2026/04/chinese-memory-chips-ymtc-c...
>CXMT still trails Samsung, SK Hynix, and Micron by approximately three years in advanced DRAM node development, and yield rates on new production lines remain the variable that determines whether capacity targets translate into reliable supply. Liu notes that lines launched in the second half of 2026 are unlikely to change the global supply-demand balance until 2027.
The Verge article talks about demand exceeding supply in 2028. Your article suggests it'll take until 2029 before Chinese production catches up to current technology.
It'll help drive prices down in five yearss, but the Chinese memory production won't be ready and efficient enough to prevent the shortages from continuing to grow.
Basically, the optimizing that can happen is that I ditch heavy tools in favour of lighter ones, and hopefully enough other people do the same to help lighter tools with finances/dev resources.
If I look at the Activity Manager in macOS, of apps that are less trashy but currently taking up a lot of memory, they mostly aren't apps that I'm willing/able to move away from to save on resource use: Firefox, Safari, 1Password. (For browsers, you can blame poorly optimized websites for a lot of it, but I just don't see anyone rushing to create lightweight clones of websites in order to save users' RAM.)
But software optimisation helps all hardware and that doesnt drive sales.
Linux however, they dont have to worry about that. Maybe it is finally the era of Haiku OS as the ghost of BeOS rises!
Assuming China takes TSMC in one piece (unlikely without internal sabotage in the best case scenario), it would still probably take years before it produces another high end GPU or CPU.
We would probably be stuck with the existing inventory of equipment for a long time…
The risk with China taking over Taiwan is that they mostly expedite their own production research by a couple of years.
Then, mostly by chance, I saw that my local Microcenter had some pre-builts for sale, and I ended up picking one up for <$5k that had "best in slot" components across the board, including a 5090 and even a high-end power supply.
The last time I built a gaming PC was upwards of a decade ago, and at that time the prevailing wisdom was to never buy a pre-built unless you had a massive amount of disposable income and couldn't spare even just one weekend to dedicate to a hobby project that could benefit you for years. Now, it was absolutely a no-brainer.
I'm struggling to put this in context. For comparison, what was your budget for refreshing the pc you had? Were the planned upgrades going to exceed $5k at current prices? Or is the situation that a pre-build machine with far better components was now only marginally more?
Or is it that pre-built gaming PCs have stopped being a joke? I had the experience building a bicycle: I was certain I was taking the frugal path sourcing each component individually and putting it together myself. At the end I was horrified to realize I spent far more than a new bike with superior components. It was pointed out that bicycle makers are buying by the pallet and will beat diy every time — so long as they're building something I want to buy.
That's still the case, and always will be — with a pre-built you're at the very least paying for someone to assemble it for you, so it's always going to be more expensive as a baseline.
Beyond that, the chance they've chosen good components and haven't tried to screw you over on less flashy ones like the motherboard and power supply is low.
That's not to say it's literally impossible to ever find a good deal. You very well might have. Doesn't change anything though.
Except isn't it possible that pre-built companies actually get better deals on hardware bought in bulk, and therefore could offset the labor costs with cheaper materials?
Hardware pricing and availabilty pre-COVID was pretty predictable and stable, which meant the consumer could extract a meaningful cost advantage if they were willing to do the relatively modest amount of work of sourcing components individually and personally assembling the build. Right now, though, some places like Microcenter appear to have a cost advantage that fundamentally relies on market and pricing instability and can only be achieved through deeper integration with the supply chain and bulk purchasing in advance -- something a retailer like Microcenter can do, but I personally cannot.
Demand increased, everyone built new fabs, then prices dropped and they couldn't pay off their investments. Many went out of business. It happened in the 80s, it happened in the 90s, it happened in the 2000s.
Now there's only three manufacturers left, and they know very well that demand for their product tends to be cyclical.
The lawsuits in the past prove that statement to not be basically but actually.
I've been in the industry for 30 years and I've worked at companies with fabs were demand was high and customers would only get 30% of what they ordered. Then just 2 years later our fab was only running at 50% capacity and losing money. It takes about $20 billion and 3-4 years to make a modern new fab. If you think that AI is a bubble then do you want to be left with a shiny new factory and no products to sell because demand has collapsed?
From now on, RAM will always be super costly for consumers, because they can't make massive deals like Apple/OpenAI/etc. We are the bagholders.
Have they really ever been cheap? Also Tesla 3 is cheaper now, Yaris is still cheap as well.
Now it's high again, but give it a couple years and it'll once again crash.
even if gaming is and will remain very popular for years, it and the desire to upgrade gaming rigs is still a discretionary activity with more price elasticity of demand than corporate uses for RAM in the dawn of the AI age. gamers live on the margin of this market, where low prices will stimulate upgrades and high prices will lead to holding out. The complaints about price are real, but that segment of the market is some combination of less large and less important.
letting the market set prices ensures that the chips go to the critical markets and uses. less critical uses will not allocate funds for purchases.
Can you please elaborate what you mean by "critical market"?
Edit: formatting
Everybody’s getting pinched, not just the gamers.
At the moment, nothing is certain. Could this last? Sure. Could it not last? Yup.
the current relative spike in the prices misses the medium-term trend of the vast decrease in memory price post-covid that led to the recent surge. the cartel got another opportunity to make bank and they will use that lever to the max.
funnily enough i've been personally stuck with 16 gigs since 2015, across three memory generations! but i am used to the past when you would spend 80-100 on an 8gb stick (jdec timings, nothing fancy but from a major brand) without accounting for inflation.
Another thing I've been thinking about is what happens when the next generation of NVidia chips comes out? I suspect NVidia is going to delay this to milk the current demand but at some point you'll be able to buy something that's better than the H100 or B200 or whatever the current state-of-the-art for half the price. And what's that going to do to the trillions in AI DC investment?
I'm interested when the next bump in DRAM chip density is coming. That's going to change things although it seems like much of production has moved from consumer DRAM chips to HBM chips. So maybe that won't help at all.
I do think that companies will start seeing little ot no return from billions spent on AI and that's going to be aproblem. I also think that the hudnreds of billions of capital expenditure of OpenAI is going to come crashing down as there just isn't any even theoretical future revenue that can pay for all that.
They'll just spend whatever they were planning to spend and get more performance.
I don't want to pay more because of AI companies driving the price up. That is milking.
Think I will scrap my PC and sell its parts.
I wonder if there are any niche companies building decent rigs with DDR3 and 5/6th generation Intel CPUs out there, it is cheap and might be a business opportunity?
All computers in my household are 8+ years old.
There's a future where RAM makers tool up for this massively increased demand, then the AI companies go broke as the bubble bursts, so RAM is cheap as. So laptop manufacturers get on that and start making laptops with 1TB+ memory so we can run decent LLMs on the local machine. Everyone happy :)
We have RAM shortage now, we will have very cheap RAM tomorrow. It’s not like production is bottlenecked by raw materials. Chip companies just need to assess if the demand by AI companies will last so it’s better to scale up, or perhaps they should wait it out instead of oversupplying and cutting into their profits.
There are two RAM suppliers...
I cannot stand how you and people like you try to justify everything by supply and demand. Also you act like it's some natural law of nature. It's not a law of nature- if you took an economics class you would realize it's try to maximize PROFIT. It's not for the good of the people.
All of these things are a CHOICE that people are making to now completely screw the average person for, again, the needs of big corporations and the top 0.01%.