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Historical memory prices 1960-2026

dam.stanford.edu · Read Story HN original

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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."

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.
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.
For windows 11, it seesms to be antivirus scanning. That's what's always blowing up my RAM
Neither do the developers, because until recently, RAM was so cheap it didn’t matter, and we were in a situation where almost no one ever needed to consider “how much RAM will this take?” when writing code.
More than code they write, the framework and runtime they use.
Like Homer Simpson said about alcohol: "To alcohol! The cause of, and solution to, all of life's problems", we developers can say about AI (assuming AI-assisted coding can save memory in popular programs, OSes, etc)
Mostly used by JavaScript parsers and HTML rendering, the rest is for telemetry.
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.
Well it's complicated. Y2K was a combination of logic issues and the consequences of certain inefficient ways to store dates, like text and BCD. Migrating to binary could fit plenty of dates into the same space or even less.

In particular, 16 bits is enough to store the entire date, year month day, from 1900 into mid 2079. Any date format that couldn't go past 1999 was probably using 24-48 bits.

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.
Discussion of memory in terms of words and their bit length, time to complete a task is more meaningful to intent on use and compaction, see greycode technique. Dollars of slop a unit sacrifice skills in the industrial base for the gain of paper profits at the repeating business meetings.
Have you ever actually tried using Windows 10 with 1GB of RAM? I wouldn't consider it suitable for "standard computing tasks."

And that's the hangup, what do you consider a "standard computing task?" On what OS? Running what software? How well? Plenty of people were still using XP in 2009, so is 256 MB of RAM okay for "standard computing tasks" in 2009?

> Have you ever actually tried using Windows 10 with 1GB of RAM? I wouldn't consider it suitable for "standard computing tasks."

I have [0], and it's actually not quite as bad as you would expect. It certainly wasn't fast, but I had no problem using it for basic web browsing and document editing. The painfully slow hard drive and processor speeds on that computer actually caused more issues than the lack of RAM.

[0]: https://news.ycombinator.com/item?id=45743066

I remember installing Windows 10 on my laptop when I was in high school, it was a decommissioned Thinkpad T410s from my dad's work. 4GB of RAM, Nehalem i5, a very early consumer SSD (OCZ, if I recall). I vividly remember my first thought being "wow this is really slow." Win7 ran like a champ on that machine.

My experience with Win10 on that laptop actually led me to buy a dumb gamer laptop for college. As those all do, it died prematurely, so I ended up back on the T410s for a while. I put KDE Neon on it. It was great!

If you're saying that you can install and use Win10 on a laptop with 1 GB of RAM, well yes I acknowledge that is true. But it's a purely academic exercise, it's not actually a usable computer for the overwhelming majority of people.

Maybe it would have been fine for my grandma. She was using a Pentium II running Windows XP to go on Facebook in the early 2010s.

IIRC the Cray 2 was offered in a 1GB configuration by the mid 80s.
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.
The graph wouldn't be a lot taller because it's using a logarithmic scale
Back then was it 1000KB or 1024KB?
The natural unit of measure for integrated circuits is a power of 2 since that's what the systems operate in. It's so natural that early 9 and 36 bit architectures were squeezed into 8 and 32 bits as it just works so much more efficiently.

Long term storage and communications? Those start to introduce things like human division of timings, frequencies, and other analog systems like rotating disks. It still generally makes sense fab actual flash chips in various powers of 2 though. The discrepancy there tends to be various forms of 'overhead' for the translation table / wear level indirection, over-provisioning, and even variations in density caused by different levels of physical cell utilization.

Still, most network stuff ships around packets of 'up to' 1500 bytes ( https://en.wikipedia.org/wiki/Ethernet_frame and lets just exclude jumbo frames ) so arguably it'd be better to talk about all computer measures in binary powers of two, exclude the marketing huckster trying to make things more impressive by shoehorning SI engineering units into a realm that uses binary math.

Also maybe you want price per average program footprint size...
> adjusting those 1960-1980 prices for inflation would make the graph a lot taller.

It won't though. One dollar in 1960 is just about ten dollars today. The graph is already in logarithmic scale so it won't make much difference.

Depressing to see how much discussion on HN (!!) has resulted from a objectively terrible graph reading. I mean... in the process of our infiltration by finance bros and VC money, have we genuinely forgotten how exponentials work?
Exponentials... that's the one with Sylvester Stallone and Jason Statham, right?
Yeah, I heard Statham got remaindered from the new Exponentials though, but don't worry, he's going to be joining The Mod Squad.
The Cray-2 had 2GB of RAM in 1985.
So total system cost per unit of memory is going up.. 2GB costs in 1985 was $2 million (from the graph), a cray-2 was $16 million (from wikipedia). A GPU server with 8xB200 today can be had for ~$500k (estimate), 1.5TB memory is $25k (from the graph).
Hmm, so memory is actually still cheap compared to historical highs. At least cheap relative to other computer components.
I once held in my hand the main part of a ferrite core memory module from the early 70s. It was kilobytes at best.

I also recall looking at recommended requirements for Dungeon Keeper 2 - 266MHz CPU, 64MB RAM and thinking "that's absurd - no such device exists!". I was a kid back then, so what did I know?

Later on in college a friend showed us his absolute monster of a laptop with a whopping 8GB of RAM - he could spin up several VMs on one device! Groundbreaking on a (nominally) portable device.

So yeah, safe to say the notion of gigabytes of RAM anywhere close to a regular person belongs firmly to the 21st century.

The author clearly wasn't implying that these were 1GB chips. They just wanted to show a graph scaled per unit of memory. It could just as well have been per byte, and the graph would've been identical but the values on the left would be changed by a factor of a billion.

You could argue that you'd rather see a "price per typical-sized RAM chip as sold at the time". That would also be a perfectly valid thing to graph (though a bit more subjective), but it doesn't invalidate this one. Since per byte (or GB or whatever you want to say) has continued downward all this time, it makes the recent spike all the more notable.

(I'm not sure it's right to label vacuum tubes and core memory as "DRAM" though.)

> I'm not sure it's right to label vacuum tubes and core memory as "DRAM" though.

Core memory does need a refresh after a read, but since it doesn't need refreshing otherwise, I'd mark it as SRAM.

Williams tube memories seem DRAM like enough to me though.

The problem with "price per typical-sized RAM chip as sold at the time" is not so much that it's subjective, but you'd get artificial spikes in the data.

For me, the key take home from the graph as stands is that price per GB right now is about the same as 2020. That seems reasonable, it's more expensive than it was, but only outrageous if you forget what it was like only a short while ago.

But back in 2020, 4GB or 8GB sticks were most common, a few years ago it was up to 8GB, 16GB or 32GB, and now 2x8GB seems to be the most common high-end configuration or 2x4GB for low-end again. If you'd jumped from 8GB sticks to 32GB sticks and back again, it would seem like there was a spike up around 2021-2 and that memory was cheaper now than a few years ago.

I think the main driver for the data is that probably consumers or the market decides on a reasonable price for memory, and people buy whatever they can get for that money. When I had a Z80 computer in the mid 80s, 64KB expansion RAM was about £100. For a similar computer but a few years earlier, a 32KB expansion RAM was about the same price. When I had an Amiga in the early 90s, a 512KB expansion RAM was again around the same price. In the 2000s, a couple of MB was around the same price. Maybe 5 years ago, the market was split a bit and a 4GB RAM was around £60 and 8GB around £120, but maybe this reflects "under $100" as the ideal target. A few years ago, it was similar but 8GB for around £80 and 16GB for around £160, now it's "doubled" in price, it's just back to 8GB for £120 again. But whatever the decade, it seems people are prepared to spend about £100 on memory for an average PC.

IMO you'd really need to graph the price of how much RAM is needed to comfortably run contemporary OS + software combinations. That will get you an actual picture of the pain inflicted by the RAM prices.

And yes, RAM demand goes up with the average RAM in computers but it does lag and it's not yet clear if it will go down with increasing ram prices as IT corporations can still afford the more expensive RAM needed for the developers to run the RAM-hungry applications they need to run, which means they won't be dogfooding their software in a normal budget user environment and are less motivated to optimize for a reasonably priced amount of RAM.

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.
yeah but you also have commercial licensing with Qt specifically :))

or we are going to see an explosion of vibe-coded GPL apps.

anyhow, the likes of Linear and Notion ain't gonna abandon web and go Qt. or!! if we are very lucky, we can see a native app framework that ticks all the boxes of a modern UI framework and is permissively licensed, but we need this crunch to stay there for years.

> you also have commercial licensing with Qt

That doesn't apply so long as you are willing to accept the LGPL. In practice that means you can statically link everything except QT so that the end user is free to drop in a modified QT version if he would like.

You can even statically link QT if you really need to as long as you dump the unlinked objects on some server somewhere.
If you are going to vibe code you can just pick any language you want. I had a go vibecoding in Rust and it worked perfectly fine. Even better than vibe coding in JS/Python because the type hints give the LLM a faster way to check progress.
Except you didn't when you consider the prices aren't adjusted for inflation.
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.
Can you back that up with anything about semi-recent nodes? The voltages are so fragile that I'm not convinced you would actually save space once you adjust the design to handle more levels.
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.
do you have a reference to exact / realistic scaling laws for the leakage currents as function of capacitor/dielectric dimensions and access transistor dimensions?

using 4 (or 2^N) voltage levels stores 2 (or N) bits, so we can afford to make the structures larger

why would this approach make sense for NAND flash but not DRAM?

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.
It'll get more efficient now people aren't upgrading. Software will be exactly as efficient as it needs to be to run on most peoples computers.
I don't think that's true - instead software will be exactly as efficient as it needs to be to run on the developers workstations. And companies throwing sky high salaries at software developers aren't as price sensitive when it comes to those workstations as the average user.
So a price per GB today is about the same as it was in 2010. 16 year regression, wow!
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

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.
It's not 1:1 when you consider inflation either. Ram is still cheaper when inflation is a factor.
Nominal. The inflation-adjusted price today is 2/3 of what it was then.
Two wrongs do not make a right?
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.

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".
Heh, unintentional. Another term shorter than spelling out "orders of magnitude" is "decades", but I figure that's less familiar and even more confusing here. "Memory price started out falling two decades per decade..."
Well if you underestimate your memory requirements by orders of magnitude you'll be out of memory for sure.
Moore's law is about transistors doubling every interval¹ *on the most economical package*.

Wikipedia is misquoting it, and extraordinary expensive chips being more capable doesn't change the economical situation.

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.

aaah, the 90s price crash. Good times.
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.

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.
Lowest 2012 price listed is 3.7 (2012-10-30) vs highest listed in 2026 is 5.375 (2026-2-1), which overlaps based on the margin for error involved. https://www.usinflationcalculator.com/
Even being vaguely in the same ballpark is a wild regression when you consider the difference in density.
So you are trying to compare the lowest with the highest and not the current price. RUn that number with the current price.
The most recent price listed is from a month ago and the prior prices are increasing. So really you can roughly compare years but not Today.

There’s plenty of data to say prices in 2012 and 2016 overlap, which is wild.

It's crazy that a AAA video game cost $60 when I was a child in the 90s and costs $60 today, sticker price! Not adjusted for inflation!
And to think that AAA video game was distributed on cartridge with expensive memory in cardboard packaging with a manual while these days that same $60 game is just a digital download from a CDN.
How is it crazy? The marginal cost to produce a copy of a video game is ~$0 and the market is much larger now then it was so fixed costs can be spread around more. The sticker price was always more or less arbitrary chosen - i.e. to maximize profits, which includes how the number is perceived.
> 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.

“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.
No, not if it involves a web browser. Most web sites today will not work on a 2012 web browser.

The PC stopped existing in isolation, for most useful tasks now, it needs an Internet connection.