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My former boss (Steve Parker, RIP) shared a story of Turner Whitted making predictions about how much compute would be needed to achieve real-time ray tracing, some time around when his seminal paper was published (~1980). As the story goes, Turner went through some calculations and came to the conclusion that it’d take 1 Cray per pixel. Because of the space each Cray takes, they’d be too far apart and he thought they wouldn’t be able to link it to a monitor and get the results in real time, so instead you’d probably have to put the array of Crays in the desert, each one attached to an RGB light, and fly over it in an airplane to see the image.
Another comparison that is equally astonishing to the RPi is that modern GPUs have exceeded Whitted’s prediction. Turner’s paper used 640x480 images. At that resolution, extrapolating the 160 Mflops number, 1 Cray per pixel would be 49 Tera flops. A 4080 GPU has just shy of 50 Tflops peak performance, so it has surpassed what Turner thought we’d need.
Think about that - not just faster than a Cray for a lot less money, but one cheap consumer device is faster than 300,000 Crays.(!) Faster than a whole Cray per pixel. We really have come a long, long way.
The 5090 has over 300 Tflops of ray tracing perf, and the Tensor cores are now in the Petaflops range (with lower precision math), so we’re now exceeding the compute needed for 1 Cray per pixel at 1080p. 1 GPU faster than 2M Crays. Mind blowing.
Nice, but the ~40 year latency is kind 0f high.
> 1 Cray per pixel would be 49 Tera flops. A 4080 GPU has just shy of 50 Tflops peak performance
Interesting, wonder how it compares in terms of transistors. How many transistors combined did one Cray have in compute and cache chips?
The Wikipedia article says the Cray-1 has 200k gates. I assume that would mean something slightly north of 2x the number of transistors? https://en.wikipedia.org/wiki/Cray-1#Description
200k * 300k Cray-1s would be 60B gates, whereas the 4080 actually has 46B transistors. Seems like we’re totally in the right ballpark.
But the Cray had a general purpose CPU while the GPUs have specialized hardware. Not exactly apples to apples.
The main part of the Cray was a compute offload engine that asynchronously executed job lists submitted by front end general purpose computers that ran OSes like Unix.
It was actually pretty close to the model of a GPU.
Back in 2020, someone built a working model of a Cray-1.[1] Not only is it instruction compatible, using an FPGA, it's built into a 1/10 scale case that looks like a Cray-1.
The Cray-1 is really a very simple machine, with a small instruction set. It just has 64 of everything. It was built from discrete components, almost the last CPU built that way.
[1] https://www.cpushack.com/2010/09/15/homebrew-cray-1a-1976-vs...
In 2013 I'd just built a new top-spec PC. I looked up the performance and then back-calculated using the TOP500† and I believe it would have been the most powerful supercomputer in the world in about 1993. If you back-calculated further, I think around 1980 it became more powerful than every computer on the planet combined.
Yes but can you sit on your Raspberry Pi like this https://volumeone.org/uploads/image/article/005/898/5898/hea...
And you can 3D print a Cray YMP case for your Raspberry Pi: https://www.thingiverse.com/thing:6947303
The pi has a sub $100 accelerator card that takes it to 30 TFLOPs. So you can add three more orders of magnitude of performance for a rough doubling of the price.
> but then again if you'd showed me an RPi5 back in 1977 I would have said "nah, impossible" so who knows?
I was reading lots of scifi in 1977, so I may have tried to talk to the pi like Scotty trying to talk to the mouse in Star Trek IV. And since you can run an LLM and text to speech on an RPi5, it might have answered.
You should have been watching lots of SciFi, too. (-:
I have a Raspberry Pi in a translucent "modular case" from the PiHut.
* https://thepihut.com/products/modular-raspberry-pi-4-case-cl...
It is very close to the same size and appearance as the "key" for Orac in Blake's 7.
I have so far resisted the temptation to slap it on top of a Really Useful Box and play the buzzing noise.
* https://youtube.com/watch?v=XOd1WkUcRzY
Obviously not even Avon figured out that the main box of Orac was a distraction, a fancy base station to hold the power supply, WiFi antenna, GPS receiver, and some Christmas tree lights, and all of the computational power was really in the activation key.
The amusing thing is that that is not the only 1970s SciFi telly prop that could become almost real today. It shouldn't be hard -- all of the components exist -- to make an actual Space 1999 commlock; not just a good impression of one, but a functioning one that could do teleconferencing over a LAN, IR control for doors and tellies and stuff, and remote computer access.
Not quite in time for 1999, alas. (-:
No need for an RPi 5. Back in 1982, a dual or quad-CPU X-MP could have run a small LLM, say, with 200–300K weights, without trouble. The Crays were, ironically, very well suited for neural networks, we just didn’t know it yet. Such an LLM could have handled grammar and code autocompletion, basic linting, or documentation queries and summarization. By the late 80s, a Y-MP might even have been enough to support a small conversational agent.
A modest PDP-11/34 cluster with AP-120 vector coprocessors might even have served as a cheaper pathfinder in the late 70s for labs and companies who couldn't afford a Cray 1 and its infrastructure.
But we lacked both the data and the concepts. Massive, curated datasets (and backpropagation!) weren’t even a thing until the late 80s or 90s. And even then, they ran on far less powerful hardware than the Crays. Ideas and concepts were the limiting factor, not the hardware.
> a small LLM, say, with 200–300K weights
A "small Large Language Model", you say? So a "Language Model"? ;-)
> Such an LLM could have handled grammar and code autocompletion, basic linting, or documentation queries and summarization.
No, not even close. You're off by 3 orders of magnitude if you want even the most basic text understanding, 4 OOM if you want anything slightly more complex (like code autocompletion), and 5–6 OOM for good speech recognition and generation. Hardware was very much a limiting factor.
I would have thought the same, but EXO Labs showed otherwise by getting a 300K-parameter LLM to run on a Pentium II with only 128 MB of RAM at about 50 tokens per second. The X-MP was in the same ballpark, with the added benefit of native vector processing (not just some extension bolted onto a scalar CPU) which performs very well on matmul.
https://www.tomshardware.com/tech-industry/artificial-intell...
John Carmack was also hinting at this: we might have had AI decades earlier, obviously not large GPT-4 models but useful language reasoning at a small scale was possible. The hardware wasn't that far off. The software and incentives were.
> EXO Labs showed otherwise by getting a 300K-parameter LLM to run on a Pentium II with only 128 MB of RAM at about 50 tokens per second
50 token/s is completely useless if the tokens themselves are useless. Just look at the "story" generated by the model presented in your link: Each individual sentence is somewhat grammatically correct, but they have next to nothing to do with each other, they make absolutely no sense. Take this, for example:
"I lost my broken broke in my cold rock. It is okay, you can't."
Good luck tuning this for turn-based conversations, let alone for solving any practical task. This model is so restricted that you couldn't even benchmark its performance, because it wouldn't be able to follow the simplest of instructions.
You're missing the point. No one is claiming that a 300K-param model on a Pentium II matches GPT-4. The point is that it works: it parses input, generates plausible syntax, and does so using algorithms and compute budgets that were entirely feasible decades ago. The claim is that we could have explored and deployed narrow AI use cases decades earlier, had the conceptual focus been there.
Even at that small scale, you can already do useful things like basic code or text autocompletion, and with a few million parameters on a machine like a Cray Y-MP, you could reasonably attempt tasks like summarizing structured or technical documentation. It's constrained in scope, granted, but it's a solid proof of concept.
The fact that a functioning language model runs at all on a Pentium II, with resources not far off from a 1982 Cray X-MP, is the whole point: we weren’t held back by hardware, we were held back by ideas.
> we weren’t held back by hardware
Llama 3 8B took 1.3M hours to train in a H100-80GB.
Of course, it didn't took 1.3M hours (~150 years). So, many machines with 80GB were used.
Let's do some napkin math. 150 machines with a total of 12TB VRAM for a year.
So, what would be needed to train a 300K parameter model that runs on 128MB RAM? Definitely more, much more than 128MB RAM.
Llama 3 runs on 16GB VRAM. Let's imagine that's our Pentium II of today. You need at least 750 times what is needed to run it in order to train it. So, you would have needed ~100GB RAM back then, running for a full year, to get that 300K model.
How many computers with 100GB+ RAM do you think existed in 1997?
Also, I only did RAM. You also need raw processing power and massive amounts of training data.
You’re basically arguing that because A380s need millions of liters of fuel and a 4km runway, the Wright Flyer was impossible in 1903. That logic just doesn’t hold. Different goals, different scales, different assumptions. The 300K model shows that even in the 80s, it was both possible and sufficient for narrow but genuinely useful tasks.
We simply weren’t looking, blinded by symbolic programming and expert systems. This could have been a wake-up call, steering AI research in a completely different direction and accelerating progress by decades. That’s the whole point.
"I mean, today we can do jet engines in garage shops. Why would they needed a catapult system? They could have used this simple jet engine. Look, here is the proof, there's a YouTuber that did a small tiny jet engine in his garage. They were held back by ideas, not aerodynamics and tooling precision."
See how silly it is?
Now, focus on the simple question. How would you train the 300K model in 1997? To run it, you someone to train it first.
Reductio ad absurdum. A 300K-param model was small enough to be trained offline, on curated datasets, with CPUs and RAM capacities that absolutely existed at the time, especially in research centers.
Backprop was known. Data was available. Narrow tasks (completion, summarization, categorization) were relevant. The model that runs on a Pentium II could have been trained on a Cray, or across time on any reasonably powerful 90s workstation. That’s not fantasy, LeNet 5 with its 65K weight was trained on a mere Sun station in the early 90s.
The limiting factor wasn’t compute, it was the conceptual framing as well as the datasets. No one seriously tried, because the field was dominated by symbolic logic and rule-based AI. That’s the core of the argument.
> Reductio ad absurdum.
My dude, you came up with the Wright brothers comparison, not me. If you don't like fallacies, don't use them.
> on any reasonably powerful 90s workstation
https://hal.science/hal-03926082/document
Quoting the paper now:
> In 1989 a recognizer as complex as LeNet-5 would have required several weeks’ training and more data than were available and was therefore not even considered.
Their own words seem to match my assessment.
Training time and data availability determined how much this whole thing could advance, and researchers were aware of those limits.
Someday real soon, kids being shown episodes of 'Knight Rider' by their grandparents won't understand why a talking car was so futuristic.
It’s impossible to explain to kids now why it was funny on Seinfeld when Kramer pretended to be MoviePhone and says “why don’t you just tell me the name of the movie you selected!”
Like James Bond's Aston Martin with a satnav/tracking device in 1964's Goldfinger. Kids would know what that was but they might not understand why Bond had to continually shift some sort of stick to change the car's gear.
Gear shifting it's still a thing in Europe, and mandatory if you want to get your driver's license.
I grew up watching Kitt and when I watched it again a few days ago, I didn't feel anything. Much less my kids.
Tried explaining what a Tamagotchi was to someone recently. Looks of utter bewilderment
That is a natural reaction.
Really? Tamagotchis seem to be one of those things that have charm beyond straight up nostalgia :o
Kitt was funny though. (For its time)
> Someday real soon, kids being shown episodes of 'Knight Rider' by their grandparents won't understand why a talking car was so futuristic.
Maybe in 100 years. The talking car was more intelligent than Siri, Alexa or Hey Google.
It is not that we are not able to "talk" to computers, it is that we "talk" with computers only so that they can collect more data about us. Their "intelligence" is limited to simple text underestanding.
I think maybe you missed the last three years. We're not talking about Alexa or Hey Google level.
We're talking about Google Gemini or ChatGPT.
Was that point not almost a decade ago?
Not really. My 1983 Datsun would talk, but it couldn't converse. Alexa and Siri couldn't hold a conversation anywhere near the level KITT did. There's a big difference. With LLMs, we're getting close.
Your car had a tiny record player.
https://www.autoweek.com/car-life/but-wait-theres-more/a1875...
Commodore 64 had text to speech in late 80s.
Also, my friend's father in the 80s was the driver of a French Consulate's member in Turkey. His car (a Renault) had speech functionality.
Test to speech it's trivial with Dr. Sbaitso or Flite in ARMv5/Pentium 90 machines.
Early 80s (1982), according to Wikipedia:
> Commodore 64 had text to speech in late 80s.
Yes, and Windows had Narrator. And that's all. Since 20 years.
The self driving aspect, amazingly, is already here and considered mundane.
Oh really? What vehicle can I buy today, drive home, get twice the legal limit drunk, flop in the back alone to take a nap while my car drives me two hours away to a relative's house?
I'd really like to buy that car so I await your response.
That's a jurisdiction problem, not a technology problem. No tech is foolproof, but even with the current technology someone would be much safer (for others, too) in the back seat than trying to drive tired, borderline DUI at night in unfamiliar town. Which many folks regularly do, for example on business travel.
The reason I cannot do this today is laws, not technology. My 2c.
The only thing stopping a Waymo from doing that is laws.
You can do all that in a Waymo except for the “buy” part. When asked about that Sergey said “why do you want to own a car? You have to maintain it, insure it, park it at home and at work. Don’t you really just want to get where you’re going and have someone else figure out the rest?” This was back before google ate the evil pill. Now their philosophy is more like “don’t fall asleep, we can get a good deal on your kidneys, after that we’ll sell your mom’s kidneys too”
A Tesla is pretty close. https://www.youtube.com/watch?v=4RZfkU1QgTI
They’ve been “close” for over a decade now.
Tesla is in no way close.
They're "cold-fusion" close. Which means a perpetual "few years".
Reading this I wonder, say we did have a time machine and were somehow able to give scientists back in the day access to an RPI5. What sort of crazy experiments would that have spawned?
I'm sure when the Cray 1 came out, access to it must have been very restricted and there must have been hoards of scientists clamoring to run their experiments and computations on it. What would have happened if we gave every one of those clamoring scientists an RPI5?
And yes I know this raises an interface problem of how would they even use one back in the day but lets put that to the side and assume we figured out how to make an RPI5 behave exactly like a Cray 1 and allowed scientists to use it in a productive way.
First of all, how would they talk to it? You'd have to give them an RPI5 with serial console enabled, and strict instructions not to exceed the 3.3 volt limits of the I/O. Now it's reasonable that you could generate NTSC video out of it, so they could see on the screen any output.
When you then explained it was just bit-banging said NTSC output, they'd be amazed even more.
Serial port
Cray 1 was released 1975, teletypes were old tech at that time.
Give it also an hdmi screen and usb keyboard, what more do you need to type code and see the result
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> What sort of crazy experiments would that have spawned?
Scientists then (at least a lot of them) knew what they wanted to do, and it required faster computers rather than more of them. A lot of that Cray power at the national labs was doing fluid simulation (i.e. nuclear explosions), and with the computers they had in the 80s, it was done in one or two dimensions, relying on symmetry. Going from n^2 to n^3 grid cells was the obvious next step, but took a lot more memory and CPU speed.
Do you think they would have run experiments that have been missed in the meantime? Why?
Cray1 should be compared to nowadays raspberry pi pico 2 / rp2350 which has similar specs (using external ram).
I won't rest until the average microcontroller in an optical mouse is more powerful than a Cray 1.
A lot of touchscreens meet that requirement. Turns out it's often cheaper to solve problems with algorithms than avoid them by design.
I'm not aware of any optical mouse using a general purpose MCU, to my knowledge they are all using ASICs
There are millions, if not tens of millions of USB and PS/2 keyboards and mice out there powered by Cypress MCUs with 8051 cores.
Nordic dominates the market for keyboards and mice. Programmable MCUs with BLE radios are required for any wireless devices.
Some gaming mice do for running RGB lights, macros, or whatever.
try as you may, but that mouse will never work as a lounge center piece.
Comparing against a raspberry pi 5 is kind of overkill. While a Pico 2 is close to computationally equivalent to a cray 1 now (version 2 added hardware floating point), the cray still has substantially more memory - almost 9MB vs 520k.
For parity, you have to move up to a raspberry pi zero 2, which costs $15 and uses about 2W of powerm
A million times cheaper than a cray in 2025 dollars and quite a bit more capable.
The memory in the Cray was external and there are RP2350 boards with 16MB of QSPI flash, here’s one of them:
https://www.olimex.com/Products/RaspberryPi/PICO/PICO2-XXL/o...
It is a frequent fantasy of mine to bring tech back to historical figures, like to show my phone to Galileo or to take Leonardo da Vinci for a ride in my car. But I guess you don't need to go that far to blow minds.
What happened to the programs/problems the Cray 1 solved? If anyone can do it on commodity hardware - is it being done? Is it all solved?
No. With more computing power the level of detail increased.
And some problems are even more complex.
My father spent his career on researching coil forms for Stellerator fusion reactors. Finding the shapes for their experiments then was a huge computational problem using then-state of the art machines (incl. cray for a while) and even today's computing power isn't there, yet.
Other problems we now solve regularly on our phones ...
A “famous” instance was the use of a Cray to render the collapse of Jupiter in the movie “2010”. A very early example of CGI in cinema.
It was pretty basic models for tasks like weather forecasting and simulating nuclear reactions. We've come a long way on both the software modeling and hardware front.
We still use a lot of the same software for nuclear reactor simulations. They just run a lot faster.
Most are not solved but modern systems can generate better solutions. Think about problems like forecasting weather or finite element analysis of mechanical systems.
Work in computational fluid dynamics is limited by computing power. Bigger and faster computers give more accuracy and speed.
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I guess I'm old because this hasn't really been that insightful of interesting observation just by itself anymore. People often talk about technological advancement of computing as if it is a force of nature whereas the amazing specs of say a rp2350 compared to the cray-1 is more of a story of the economies of scale as opposed to merely technical know-how and design. The reason a rp2350 is a few dollars is because of fabs, infrastructure, and institutional knowledge that likely dwarf the cost of producing a cray-1. I wouldn't even be surprised if someone bothered to do a similar calculation of the cost of infrastructure needed behind each cray-1 at the time that it could even be less what is needed to produce rp2350s today. The unit price of a rp2350 to consumers being so cheap (right now that fabs still want to make it) somewhat elides the actual costs involved.
Animats below said that the Cray-1 was made from discrete components. Good luck making a rp2350 from discrete components, it likely wouldn't even function well at the desired frequency due to speed of light and RF interference issues--it would likely be even worse for GHz broadcoms used in the rpi5. This means that in a post-apocolyptic future you could make another cray-1 given enough time and resources. In 20 years when the fabs have stopped making rp2350s there simply will not be any more of them.
The cray 1 did look futuristic like something out of star trek.
It kinda reminded me of the trash can mac. I wonder if it was inspiration for it
> It kinda reminded me of the trash can mac. I wonder if it was inspiration for it
Ironically the trash can Mac actually looked strikingly similar in size and shape to actual small trash cans that were all over the Apple campus when I worked there. I’d see them in the cafeteria every day. They were aluminum though, but otherwise very similar. I always wondered if they had anything to do with the design of the computer, even if only subconsciously.
If I ever have reason to build a Pi cluster, I'm putting in a Cray X-MP shaped case.
Related: a Pi Pico cluster that looks like a Cray computer https://hackaday.com/2023/04/09/parallel-computing-on-the-pi...
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Finding more & more that power efficiency is what's driving me towards new gear rather than lack of horsepower.
A few niche uses aside (gaming, llm) a vaguely modern desktop is good enough regardless of details.
These comparisons are fun at all but a better one would be the difference between whatever "computer" a citizen lambda would have used back in the day and the cray1 and whatever on can use now and the current "cray" (or whatever humans use now) and see the difference of cost.
I did a little poking round and I think the modern equivalent to old super computers is a mainframe. Modern super computers take up entire warehouses, cost upwards of $100 million, and are measured in exaflops.
Cray 1 costs US$7.9 million in 1977 (equivalent to $41 million in 2024) (Source: Wikipedia)
I have no idea what IBM z-series mainframes cost but I think it would be less.
$41 million can buy you one or more thousands of rack-mounted servers and the associated networking hardware.
My rough guess would be the difference in 2024 iphones to mainframes is an order of magnitude more between them than Cray and anything else on the market at the time.
It’s also interesting to note how much software has changed. The actual machine code may be less optimized, but we have better algorithms and we have the option of using vast amounts of memory and disk to save cpu time. And that’s before we get into specialized hardware.
Mainframes aren't supercomputers. The point of a mainframe (anymore) is reliable transactions without downtime. They're not necessarily beasts at computation.
Supercomputers were and are beasts of not only computation but memory size and bandwidth. They're used for tasks where the computation is highly parallel but the memory is not. If you're doing nuclear physics or fluid dynamics every particle in a simulation has some influence on every other. The more particles and more state for each particle you can store and apply to every other particle makes for a more accurate simulation.
As SCs have improved in memory size and bandwidth simulations/modeling with them has gotten more accurate and more useful.
The first Cray-1 was installed at Los Alamos National Laboratory in 1976. That same year Gary Kildall created CP/M and Steve Wozniak completed the Apple-1.
What I find somewhat puzzling is that these machines were used for the "really big problems". We used supercomputers for weather forecasting, finite element simulations, molecular modeling. And we were getting results.
I don't feel we are getting results that are thousands of times better today.
> I don't feel we are getting results that are thousands of times better today.
You are getting results that are way better than thousands of times. You just aren't aware where they are showing up.
To give you a glimpse, the same modelling problems which a couple of decades ago tool days to come up with a crude solution are now being executed within a loop in optimization problems.
You are also seeing multiphysics and coupling problems showing up in mundane applications. We're talking about problems that augment the same modelling problems that a couple of decades ago tool days to solve with double or triple the degrees of freedom.
Without the availability of these supercomputers the size of credit cards, the whole field of computer-aided engineering would not exist.
Also, to boot, there are indeed diminished returns. Increasing computational resources unblocks constraints such as being able to use doubles instead of floats. This means that lowering numerical errors in 3 or 4 decimal places comes for free at the expense of taking around 4 times longer to solve the same problem.
To top things off, do you think the results of two decades ago were possible without employing a great deal of simplifications and crude approximations? As legend has it, the F117 Nighthawk got it's design due to the computational limits of the time. Since then, stealth planes became more performant and with a smoother design. That's what you get when your computational resources are a thousands times better.
> If AI systems continue to improve at the current rate and we combine that with improvements in hardware that are measured in orders of magnitude every 15 years or so then it stands to reason that we'll get that "super-intelligent GAI" system any day now.
Oh come off it now. This could have been just a good blog post that didn't make me want to throw my phone across the room. GenAI is a hell of a drug. It's shocking how many technical professionals fall into the hype and become irrationally exuberant.
Even if you are a GAI / super intelligence booster, the limiting factor is clearly software and data. If it is possible, the big tech AI labs already have all the compute they need to make one deployment work. Hardware is limiting for deploying at scale and at a profit.
I was more about to point out that the 10x per 15 years for hardware hardly holds anymore for silicon and it's ridiculous to expect that to continue.
> it stands to reason that
The upper-class "trust me bro"
That was a weird turn to AI at the end, but otherwise an interesting reflection. I'm a little too young to have grown up in the era of the Cray-1, but even in the early 90s, processors ran at 90 MHz and hard drives cost $1 per megabyte. Back when personal computers ran at single-digit megahertz and had kilobytes of RAM, a Cray was mind-blowing.
The exciting part back then was that, while computers were never "good enough," they were getting noticeably better every few months. If you were in the market for a computer, you knew you could get a noticeably better one for the same price if you just waited a little while. The next model was exciting, because it was tangibly better. At some point personal computers became "good enough" for most people. Other than compensating for creeping software bloat, there hasn't been much reason for most people to be excited about new computers in a decade or more.
> the Cray had about 160MFLOPS of raw processing power; the Pi has... up to 30GFLOPS. Yes... that's gigaFLOPS. This makes it almost 200 times faster than the Cray.
Imagine traveling back to 1977 and explaining to someone that in 2025 we've allocated all that extra computing power to processing javascript bundles and other assorted webshit.
That actually wouldn't be so bad, but in reality the number one usecase for raspberry pi is blinking leds for some time and collecting dust afterwards
Still a better user than crunching Javascript to show you ads and track you around.
Are there any details or examples of computational work the Cray 1 used for?
No benchmarks. Hard to take this seriously.
Hardware has gone a long way...
...software... well, that's a different story.
While a cray could compute millions of things and did a bunch of usable stuff for many groups of people who used it back then, a raspberrypi today has trouble even properly displaying a weather forecast at "acceptable speeds", because modern software has become very bloated, and that includes weather forecast sites that somehow have to include autoplaying video, usually an ad.
This thread should be a MasterClass. Awesome reading. Seriously. -a gen x'er.
Adjust the price of the Cray-1, for inflation, but not the power, for Moore's law? Need I get my napkin out for a few calculations? or do we just FORGET MOORE'S LAW ( that is mention no less that 4 times, without quantification? Cray-1 (1976 ). RPi ( 2012 ). 37 years of elapsed time. 24. 2/3 elapsed generations. 26,509,000 times increase in power. Cray 1 160Mf. In a 26M times faster, would yield 4,241Gf ( 4.2Pf) , while the PI1 is capable of 13.5Gf, so the RPi-1 ( 2012 ) is about 0.31% of where Moore's law power doubling is.
Now lets compare this to the top 500. ( see the point? )( do not speak of Moore's law, while ignoring the mathematical implications. ) ( and yes, 3/1000s is three thousandths ).
Top 500 is 1.7 Exaflops, but by Moore's law should be 4,241Gf or 4.2Xf. So the top 500 is not keeping up with Moore's law.
However Moores Law refers to the number of transistors. Not FLOPS.
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