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."
What? Gold underwent a massive revaluation with the end of Bretton Woods in 1971. It was prior to then that government were actively involved in making the price of gold artificially low.
Fun fact: under 31 U.S. Code § 5117 a troy ounce of gold is still valued at 42 and 2/9ths dollars.
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.
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?
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.
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.
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).
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.
If my memory serves me correct (no pun intended), when I was a kid I remember bugging my mom to buy me like 2 or 4 1 MB modules, it was at least 50 bucks or 100 bucks each.
Now everyone's going to talk about how cheap everything is by comparison - but someone needs to talk about how oppressively hungry browsers and OSes are compared to in the past. This is no HIMEM.SYS
There’s been a sharp divergence in memory requirements. Talk to developers and they think that 32GB is the bare minimum these days, with 64GB or more preferred. They’ll point to Electron and Chrome tabs and everything else.
Then you sit down with an average computer user on their 8GB RAM MacBook Neo and they’re in love with how fast and smooth it is, even with their chrome tabs and the company Slack up and Spotify in the background.
I still have an older 8GB machine to kick around with on the go when I don’t want to haul the expensive laptop. It’s fine, even for a lot of development.
I think it would be better if one has the discipline to just use older machines and play older games and only visit certain websites that can be visited on older version of browsers. A second-hand 16GB laptop can go a long way.
But yeah that probably sucks from time to time, especially for young people.
My laptop has 8 GB. I write blog posts, I have a dozen-ish tabs open, I do KiCAD things (including 3D renders!). Works great. I was doing Verilog synthesis on a similar machine in college in 2020.
The truth is that, if you do the same things you were doing with your computer 10 years ago, well then you don't need a new computer!
If all you do is write books, a Pentium III will do the job just as well as a brand new PC.
Of course, the web throws a wrench in this. Word 2003 is still far more capable than Google Docs, yet tons of people opt for the cloud slop because it's convenient and free-as-in-beer. And, Google Docs will continue to become less efficient with time.
You can do a lot on old machines but developers also need to optimize a bit. Youtube almost plays on a 20-year-old machine, which means with some effort it'll play just fine. Most the other sites work just fine.
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.
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.
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/
> 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.
In the first graph, if you hover over the DRAM line you'll notice that the most recent data points are for DDR3. One of the data points from 2025 is a 2 GB stick. This paints a more rosy picture than the situation deserves.
TIL someone took over the now defunct jcmit dataset[1] (archive[2]). I expected his dataset to die off when his website did, but I guess someone found the data dump on archive.org and revived it. Which raises a question: how will this dataset fare five years from now?
It's amazing how consistently thr lower memory cost have expanded the set of economic viable applications : cheaper hardware doesn't just improve existing software it also enables software that was not possible before
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.
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.
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.
Unfortunately, this is unadjusted prices, and this failed to annotate where the cartel years and when the cartel was 'broken up'. Not a bad assignment's work but clearly lacking the domain awareness necessary to report the complete story through graphs.
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.
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.
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.
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?
I guess ‘per GB’ doesn’t really capture it, because the base number of gigabytes available to people (ie- the smallest compatible RAM kit you need to build a computer) and the base number of gigabytes you really need (OS bloat, feeling responsive, etc) have gone up so much.
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.
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.
Gas is priced in $/gal, not dollars per mile or hour of lawn mowing or whatever. The resource and the use are completely different concepts and the resource owner/producer cares not of the buyers purpose for it.
I use a Thinkpad T530 for reasons that are very important to me. It is the only laptop that I have, so it is what I use for every manner of portable computing.
It still does all the things I want it to do, including using modern websites with modern browsers on modern operating systems (including Windows 11).
The 2012 computer running a modern linux install will still work fine. I'm talking more about the specs, specifically memory. I had 8gb of ram in my computer in 2012, the Macbook Neo released this year still has 8gb and is usable for modern day tasks.
We don't _need_ that much ram, we just found new things to do with more.
This is extremely misleading and not very useful. It makes little sense to use pricing per GB during decades when RAM was at most in MBs. In that case, why not talk about price per TB or PB? Then the line will look pretty much flat and horizontal.
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."
> 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.
Governments in combination and coordination devalued the price of gold in 1969.
What? Gold underwent a massive revaluation with the end of Bretton Woods in 1971. It was prior to then that government were actively involved in making the price of gold artificially low.
Fun fact: under 31 U.S. Code § 5117 a troy ounce of gold is still valued at 42 and 2/9ths dollars.
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.
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?
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.
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
The graph wouldn't be a lot taller because it's using a logarithmic scale
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).
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...
IIRC the Cray 2 was offered in a 1GB configuration by the mid 80s.
If my memory serves me correct (no pun intended), when I was a kid I remember bugging my mom to buy me like 2 or 4 1 MB modules, it was at least 50 bucks or 100 bucks each.
Now everyone's going to talk about how cheap everything is by comparison - but someone needs to talk about how oppressively hungry browsers and OSes are compared to in the past. This is no HIMEM.SYS
There’s been a sharp divergence in memory requirements. Talk to developers and they think that 32GB is the bare minimum these days, with 64GB or more preferred. They’ll point to Electron and Chrome tabs and everything else.
Then you sit down with an average computer user on their 8GB RAM MacBook Neo and they’re in love with how fast and smooth it is, even with their chrome tabs and the company Slack up and Spotify in the background.
I still have an older 8GB machine to kick around with on the go when I don’t want to haul the expensive laptop. It’s fine, even for a lot of development.
I think it would be better if one has the discipline to just use older machines and play older games and only visit certain websites that can be visited on older version of browsers. A second-hand 16GB laptop can go a long way.
But yeah that probably sucks from time to time, especially for young people.
My laptop has 8 GB. I write blog posts, I have a dozen-ish tabs open, I do KiCAD things (including 3D renders!). Works great. I was doing Verilog synthesis on a similar machine in college in 2020.
The truth is that, if you do the same things you were doing with your computer 10 years ago, well then you don't need a new computer!
If all you do is write books, a Pentium III will do the job just as well as a brand new PC.
Of course, the web throws a wrench in this. Word 2003 is still far more capable than Google Docs, yet tons of people opt for the cloud slop because it's convenient and free-as-in-beer. And, Google Docs will continue to become less efficient with time.
Second-hand? lol my main driver has 16gb and just peachy. What do you folks do that needs so much ram to browse the web.
You can do a lot on old machines but developers also need to optimize a bit. Youtube almost plays on a 20-year-old machine, which means with some effort it'll play just fine. Most the other sites work just fine.
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.
> 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.
Would be nice to have hard drives on the same chart
In the first graph, if you hover over the DRAM line you'll notice that the most recent data points are for DDR3. One of the data points from 2025 is a 2 GB stick. This paints a more rosy picture than the situation deserves.
Why has there been such an obvious repeating price cycle in the last 20 years?
Is that due to node sizes or generations or fabs coming online or what?
Memory semis are a classical example of a cyclical industry: simulaneous capacity investments -> overproduction -> price crash -> ...
This cycle is the first one than truly breaks the trend. It seems that the industry NEVER needed thus nuch memory for this long.
Also, given the history, producers are afraid to overivest, and newer players from china are lagging behind for now.
Cyclical industries are very common.
TIL someone took over the now defunct jcmit dataset[1] (archive[2]). I expected his dataset to die off when his website did, but I guess someone found the data dump on archive.org and revived it. Which raises a question: how will this dataset fare five years from now?
[1]: https://www.jcmit.com/mem2010.htm [2]: https://web.archive.org/web/20250716092935/https://jcmit.net...
The dataset about memory prices now has a memory preservation problem. Very meta.
Nice to know we still have a ways to go till 1960s pricing!
It's amazing how consistently thr lower memory cost have expanded the set of economic viable applications : cheaper hardware doesn't just improve existing software it also enables software that was not possible before
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.
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.
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.
Unfortunately, this is unadjusted prices, and this failed to annotate where the cartel years and when the cartel was 'broken up'. Not a bad assignment's work but clearly lacking the domain awareness necessary to report the complete story through graphs.
Do we have a chart of memory production per year? (Are any large expansions, by incumbents or new entrants, planned for the near term?)
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
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.
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.
Except you didn't when you consider the prices aren't adjusted for inflation.
Usefulness per gigabyte should be taken into account too. We could do way more with 32MB in the 90s than 4GB today
I knew I paid more per GB in 2018 for DDR4 than even today's inflated prices of DDR5...
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?
Were we really paying more for RAM per GB in the late 2010s than we are now?
Just really doesn't feel like it. Interesting.
We're paying more now than we paid in the late 2010s, but less than we paid in the early 2010s.
I guess ‘per GB’ doesn’t really capture it, because the base number of gigabytes available to people (ie- the smallest compatible RAM kit you need to build a computer) and the base number of gigabytes you really need (OS bloat, feeling responsive, etc) have gone up so much.
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’s weird to see supply and demand battle moores law.
aaah, the 90s price crash. Good times.
Which manufacturers quickly thereafter put to an end:
https://en.wikipedia.org/wiki/DRAM_price_fixing_scandal
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.
Note that the chart scales by powers of 10
So, it’s on a logarithmic scale
Except no one was buying 1 Trillion $ for a GB of RAM in 1960. Even Professor Frink would agree:
https://www.youtube.com/watch?v=ykxMqtuM6Ko
"on a galactic timescale, the prices haven't risen at all..."
“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.
the problem with this analysis is that it doesn't account for memory speed, which doubles with each generation of ddr
DRAM and chill
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.
Gas is priced in $/gal, not dollars per mile or hour of lawn mowing or whatever. The resource and the use are completely different concepts and the resource owner/producer cares not of the buyers purpose for it.
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.
I use a Thinkpad T530 for reasons that are very important to me. It is the only laptop that I have, so it is what I use for every manner of portable computing.
It still does all the things I want it to do, including using modern websites with modern browsers on modern operating systems (including Windows 11).
The T530 was released in June of 2012.
The 2012 computer running a modern linux install will still work fine. I'm talking more about the specs, specifically memory. I had 8gb of ram in my computer in 2012, the Macbook Neo released this year still has 8gb and is usable for modern day tasks.
We don't _need_ that much ram, we just found new things to do with more.
This is extremely misleading and not very useful. It makes little sense to use pricing per GB during decades when RAM was at most in MBs. In that case, why not talk about price per TB or PB? Then the line will look pretty much flat and horizontal.
The line would look the exact same if you switched it to price per PB. Only the labels on the y-axis would change.