431–440 of 524 posts
I bought 96GB of RAM a couple of years ago for ~$250. That same RAM now costs $1200!
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.
Bought an extra one by accident, paid $218.99 March 2025
Goes for $1400 now. I haven't gotten around to selling it.
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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.
Used enterprise HDD’s also jacked up now. It’s absurd lol
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Seems unlikely. Increased demand usually causes increased investment in increasing supply.
Not necessarily in a notoriously cyclical industry where everyone has already been burned by doing exactly that multiple times
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Yes, this will definitely renew interest in Stadia type products.
Why? Those servers still have to pay the same price for components plus a markup for the service. In theory you can serve more gamers per GPU, but these GPUs have to be physically located in your city to have a usable latency, and that means you'll have issues with peak utilization being most users gaming at the same time of day. I just don't see the cost savings of sharing a GPU overcoming the extra expense + profit…
Not if Nvidia is running the service.
Seems quite possible to me that Nvidia sells to the public just enough graphics cards to keep any frisky antitrust investigators off its back and reserves the rest for GeForce NOW, its "pay monthly for limited access to a remote gaming PC" service. The cards for NOW are billed to the BU running NOW at or below cost, the few cards available to consumers and System Integrators naturally have a huge markup due to extremely constrained supply, and Nvidia uses the fact that they are the thing behind the LLM Boom to ensure that they have -what a System Integrator in 2022 would recognize as- a reasonable price for just enough RAM for the computers that NOW rents access to.
Downvoters: notice the speculative nature of the previous paragraph. I'm not claiming that this is happening right now. I'm claiming that it's quite possibly more profitable for Nvidia to bill monthly for limited remote access to computers with Nvidia graphics cards in them than it is to sell those cards at retail and to SIs.
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).
The memory makers will not expand demand drastically. It is in the nature of their business to keep the market under-supplied, otherwise the following oversupply will kill them. Instead, supply is just rerouted from less profitable segments such as mobile and personal computing.
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Why? Those servers still have to pay the same price for components plus a markup for the service. In theory you can serve more gamers per GPU, but these GPUs have to be physically located in your city to have a usable latency, and that means you'll have issues with peak utilization being most users gaming at the same time of day. I just don't see the cost savings of sharing a GPU overcoming the extra expense + profit…
> Those servers still have to pay the same price for components... Not if Nvidia is running the service. Seems quite possible to me that Nvidia sells to the public just enough graphics cards to keep any frisky antitrust investigators off its back and reserves the rest for GeForce NOW, its "pay monthly for limited access to a remote gaming PC" service. The cards for NOW are billed to the BU running NOW at or below cos…
It's just far more likely that these GPUs actually do cost a ton to make right now.
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You don't need "very much" expert overlap to see aggregate gains at scale, you just need some of it; that's where the "birthday" framing becomes relevant. Memory for context is an issue, but recent models like DeepSeek V4 use very little of it even at relatively large contexts.
>You don't need "very much" expert overlap to see aggregate gains at scale, you just need some of it I'm not sure what you are claiming. Decode is bottle-necked by memory bandwidth. To see a speed up of 2x, you have to ensure each expert weight memory fetch can be used by 2 parallel streams. What exactly is the average factor you are claiming for 5x parallel streams (due to "birthday paradox" factors)? The Birthday p…
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Not necessarily in a notoriously cyclical industry where everyone has already been burned by doing exactly that multiple times
What would they be doing with their enormous profits instead?
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The memory makers will not expand demand drastically. It is in the nature of their business to keep the market under-supplied, otherwise the following oversupply will kill them. Instead, supply is just rerouted from less profitable segments such as mobile and personal computing.
I struggle to think of a line of business as cyclical as DRAM, maybe like certain kinds of mining would be my only thought. The DRAM fabs have been on a roundabout for 40 years going from getting accused of price fixing and cartel behavior, to struggling to keep the lights on. And imo it's not really their fault, it's all the lead time of advanced semiconductors, combined with the commodity dynamics of oil. And the g…
"Accused" makes it sound like these things may still be up in the air, when they very much are not. I would choose instead the much clearer "A number of those involved in DRAM production have a proven history of cartel behavior and price fixing."
For those who may not be familiar with some of the history in this area:
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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.
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.