Says, not inflation-adjusted. With reason; adjusting those 1960-1980 prices for inflation would make the graph a lot taller.
Pricing "per GB" before 1990 is unrealistic, though; nobody thought in GB or purchased GB quantities, or conceived of GB systems. I remember a moment circa 1973 when I saw an IBM CE about to do an upgrade on a 370 system at Cal Berkeley. He had a box with several carefully-packed, large circuit boards. "So, is that a megabyte?" I asked. "Yup, that's a meg."
I wouldn't go so far as to say "nobody". Electric Boat had 2 GB memory in one of its systems at that time, with the hardware capacity to increase to 4 GB. It sounded insane at the time, but it absolutely existed, and thereby seems reasonable to include it in any research of historical pricing.
Yes, you really need "dollars per amount of RAM you need for standard computing tasks." Windows 11 requires a bare minimum of 4 GB of RAM, Window 10 only needed 1 GB.
If what you're interested in is fluctuations in production versus demand then you absolutely do not want a subjective metric. Measures of the form dollars per unit, units per watt, units per flop, etc are what you're interested in.
That's just as wrong in the opposite direction, y2k was a thing because two bytes were worth the saving in 1980, and we really needed those two bytes.
I still don't get where all that memory goes.
Abstractions on abstractions on abstractions; background tasks and their abstraction stacks; increased cache and buffer sizes to take advantage of increased typical memory capacity. For an example of the latter, handling TCP on a Commodore 64 is a problem because the memory can only fit about 45 packets with nothing left over, but now you can just allocate a megabyte receive buffer per connection.
IIRC the Cray 2 was offered in a 1GB configuration by the mid 80s.
Look at it this way: while the upfront cost to scale up production is huge, prices are now high enough to justify it even if demand is expected to drop abruptly later on. So if you can wait 5 years for your next PC, 1TB RAM might go for what 64GB would have cost without the AI demand spike.
Granted, if you need a new system before then, you're SOL.
One thing to look out for is supply capacity curiously going offline in 2030 or whatever. That would hint at market power or collusion.
> while the upfront cost to scale up production is huge, prices are now high enough to justify it even if demand is expected to drop abruptly later on.
Given the nature of the industry and how critical the product is I think it would make more sense for governments to bankroll fab construction in a way that the public takes on the risk of consumer prices falling below a certain level within some limited timeframe. Mildly subsidized chip production seems like a much better downside than the current sky high prices.
Memory prices per GB were cheaper in 2012.
It’s possible we’ll see a huge price drop on the near term but SSD + Cache + GPU’s seems to have changed the equation where RAM speed is considered more important than size. And from a pure architecture standpoint it makes sense.
They weren't though when you adjust for inflation. If you took inflation into account, ram is cheaper now by $0.89/GB for DRAM compared to 2012.
Even being vaguely in the same ballpark is a wild regression when you consider the difference in density.
One could also blame crypto and AI (they're clearly responsible for some of the volatility in the graph), but I can see the curve flatten in the 2010s, just as Moore's law ended.
Can you blame Moore's Law ending? The graph at https://en.wikipedia.org/wiki/Moore's_law looks steady up to the 2020s.
1979 to 2009 in the OP graph has a pretty steady drop from 10^7 to 10^1 USD/GB: 6 OOMs in 30 years. Then till before the recent spike it was around 1 OOM in 15 years: 1/3 the rate of progress on a log scale.
When it comes to CPU progress we blame the end of Dennard scaling several years before the knee in this memory curve. I'd guess the story of memory is similar in also hitting technical difficulties, but I don't know.
I am tickled that OOM can mean "out of memory" in another context. You clearly meant "orders of magnitude".
Moore's law didn't end in any broad sense and certainly not that far back. It's a tiring piece of misinformation that just won't die.
Progress has consistently become more difficult (ie more expensive) but has generally kept up. The scaling of a couple specific technologies noticably slowed down a few years back but that's not the general case.
The node names aren't representative of the reality.
turns out things are not that bad! we just rolled back to 2010.
oh, wait, now every app is a browser instance. shit.
EDIT: so, how did I arrive at 2010, you ask? I looked at DDR5 pricing and found the closest pricing per GB in the past. this turned out to be DDR3 memory. I think it's totally fair since it was the latest and greatest thing back then, much like DDR5 is now. although, if we compare DDR3 to DDR3, we still roll back pretty far - a very close to current price was spotted in 2018, '17, 15, '13, and '11.
Yeah but now apps will have to start shaving off memory and maybe going native again. So it'll end up okay.
Will they..? It seems equally (or perhaps more) likely that we'll increasingly see vibe coded browser or Electron based applications as the bar is now lower to build such a thing.
Vibe coding also lowers the barrier of maintaining multiple native pathways. Also of adopting QT instead of electron.
So a price per GB today is about the same as it was in 2010. 16 year regression, wow!
Drawing a line backward from today's high water mark only goes back to 2018.
2010 prices were significantly higher.
The chart is also not inflation adjusted, which would bring the equivalent date forward even further.
Nowhere near a 16 year regression.
Nominal. The inflation-adjusted price today is 2/3 of what it was then.
sure but you also need more gb these days for various tasks so it's not 1:1
I wonder if developers will start trying to do more with less in certain areas
It's not 1:1 when you consider inflation either. Ram is still cheaper when inflation is a factor.
Arguably they already did with the "cloud native" systems. There were plenty of examples personally known to me in the mid and late 2010s of smaller tech companies trying to run production PostgreSQL on 8-16 GB of RAM because they didn't want to pay the cloud RAM tax. Many "cloud native" systems were designed under these (mostly artificial IMO) RAM constraints.
Is that because the amount of available memory is limited for a single process? You can always add more storage and storage access is relatively the same regardless of whether it comes from the SSD inside the server or sits in another rack. Storage is a pretty linear cost when you're a cloud host buying storage in the hundreds of PB numbers. Whereas for memory, if you want the whole thing, you need the whole server even if your process is light on CPU requirements.
is multi-level DRAM worth considering? storing multiple voltage levels per DRAM capacitor?
If you care about only capacity and cost yes, but not if you care about performance.
If it were possible, it would have been done already. The issue is the capacitors are already tiny, and barely can prevent a single bit decaying before refresh.
You could also do a computing pr dollar graph - which would be a similar sharp decline over the past decades - however it won’t show anything like the memory price spike of the past few years.
It certainly doesn't look as bad as it really is when presented on a log scale chart.
Going up is worse though because software has gradually got less and less memory efficient.
aaah, the 90s price crash. Good times.
“All that is human must retrograde if it does not advance.” -Edward Gibbon
My fellow humans, we have retrograded.
this is interesting. but i’d be more interested to see a graph starting at the point when developers got their own computer.
then the price of ram over time for whatever the daily functional workstation a developer would have needed then.
i mean this is a graph of the price of GIGS of ram from a time period when the space shuttle needed like 1 MB.
A perfect example of how graphs are often misleading. $/GB is a totally useless unit value because it's an arbitrary size. The unit needs to be tied to the relative usefulness for its time. The y axis should be something like $/average workstation memory or $/requirement for common compute task. It's obvious that ram is expensive right now, but it's not expensive per GB. It's expensive relative to what you need to accomplish a useful task.
But relative usefulness is entirely subjective, making it a meaningless unit. Depending on your use case you may need 256 GB or 0.5 GB.
The audience who would benefit from hypothetical $/usefulness would be people who don’t know what memory is and don’t know what’s inside of their computers, or what it does. This is a fine audience to be in and to serve, but obviously not the audience of that website and not HN.
If you think that audience is under served for memory market statistics, I encourage you to make such a website and serve that audience.
For people on HN, who do you know what memory is, $/GB is a fine metric.
This is assuming that the wide variety of use cases are evenly distributed and that larger use cases are not mostly just a lot of duplicated smaller use cases. If I have a website I will need X amount of ram. If you run a much larger website offering a comparable service you will need some multiple of X, but you don't actually need much more ram per user (assuming you're also accounting for extra infrastructure and not just the web servers). It's the same task just scaled. Relative usefulness is not subjective, you could look at a variety of tasks in different industries. Windows server 2012 had a minimum requirement of 512 MB. Windows server 2025 has a minimum requirement of 2 GB. That's 4x for the same task which totally distorts $/GBs usefulness for being able to tell you anything helpful economically. It's obviously good to collect this data, but you need to pair it with some kind of demand data for it to actually tell you anything.
> you need to pair it with some kind of demand data for it to actually tell you anything.
Again, this is entirely dependant on who is consuming the statistic and for what purpose. For some use cases, yes demand data will be quite crucial. For others it will not. It's quite apparent the site's author doesn't see this as crucial and for the purposes I need to consider memory pricing, I agree.
> The unit needs to be tied to the relative usefulness for its time.
That requires baking in assumptions, and makes the data less general.
You can go from $/gb to $/usefulness fairly trivially by adding assumptions, but you can't go the other way.
A useful task isn't a fixed thing though. Everything the 2012 computer did you can still do today with the same amount of ram we had back then.