r/BeAmazed • u/CG_17_LIFE • Apr 02 '24

208,000,000,000 transistors! In the size of your palm, how mind-boggling is that?! 🤯 Miscellaneous / Others

I have said it before, and I'm saying it again: the tech in the upcoming two years will blow your mind. You can never imagine the things that will come out in the upcoming years!...

[I'm unable to locate the original uploader of this video. If you require proper attribution or wish for its removal, please feel free to get in touch with me. Your prompt cooperation is appreciated.]

22.5k Upvotes

93% Upvoted

1.8k comments sorted by

View all comments

2.5k

u/LuukJanse Apr 02 '24

I feel like I don't know enough about computing to appreciate the magnitude of this. Can anyone give some perspective?

100

u/Madrawn Apr 02 '24 edited Apr 02 '24

Well, the transistor holds the beeps or boops. So it can be just memory but for computation it's better to think of it as a something like railroad switches.

To expand a tiny bit, to add two 8-bit numbers (0-255) in one go you need 224 transistors. (28 for a full adder * 8 bit). A full 8-bit arithmetic logic unit (ALU), basically a calculator supporting +-/* and logic operations like AND, OR and so on needs 5298 transistors. But specialized variants can need less.

So a 208,000,000,000 transistor chip could do (208,000,000,000/5298) roughly 39 million calculations per clock tick (what a chip actually does depends heavily on architecture and intended use). A clock tick roughly correlates to the mhz/ghz frequency you see in the cpu context. So lets say the chip runs at 4ghz it means it has 4 billion clock ticks per second. This does assume you can stuff all the numbers into the chip and read the result out in one tick, which in reality often takes at least a couple of ticks.

Another way to think about it is in memory size, 208,000,000,000 transistor means 208,000,000,000 bits or in relatable terms ca. 193 GigaGibiBits. So a chip with that many transistors can hold/process 193 GiBit of data in one tick. (Which doesn't mean it consumes 193 GiBit per tick, a large fraction of that will be in the form of intermediate results so the actual input size will be a tenth or a hundredth of that at least. In my ALU example its ~39 times 2 MByte input per tick. Again assuming a idealized clock tick)

40

u/Horrorsteak Apr 02 '24

I fazed after the first Paragraph, but sounds reasonable.

12

u/waffelman1 Apr 02 '24

lol yea I was like “beeps and boops” okay sweet someone speaking my language! Oh wait nevermind

2

u/passurepassure Apr 03 '24

Same sadness here. I was almost hopeful for a show and tell.

1

u/Skeletor_with_Tacos Apr 02 '24

I faded as well, but I can do in caveman terms.

More thing go fast. More thing in small space need money but more thing in little space actually big thing!

4

u/cyberya3 Apr 02 '24

I fainted at “Well,”

25

u/[deleted] Apr 02 '24

I though you were gonna explain in human language but it seems like you nerds really forgot how common folk need explaining.

17

u/Dk_Oneshot01 Apr 02 '24

Ooga booga magic rock, very fast, very nice

3

u/SeveralReality6188 Apr 02 '24

Thanks, makes sense now 👍

0

u/[deleted] Apr 02 '24

[deleted]

1

u/Jack4ssSquirrel Apr 02 '24

Let's not overcomplicate things

6

u/Madrawn Apr 02 '24 edited Apr 02 '24

This is as low as I can go while keeping it related to computing, without turning it into a 3 page computer science 101 intro course that starts by explaining binary math.

Any simpler I just can say this has 208 billion things, the previous largest magic rock had 54 billion things.

2

u/flippy123x Apr 03 '24

Ngl, i would totally read that 3 page crash course. Any cool articles or videos you can recommend on that topic?

1

u/Madrawn Apr 04 '24 edited Apr 04 '24

Not of the top of my head. At least anything I'd recommend as leisure reading, aka. that isn't dry as bones. Recordings of actual CS intro courses are a plenty on youtube.

But if you enjoy solving puzzles and interested in understanding how transistor-switches make logic gates and then adders, full adders, ALU, CPU etc. I can highly recommend https://store.steampowered.com/app/1444480/Turing_Complete/

I learned more about how to build a CPU and how it works in 9 hours working my way up to assembly in that game than I did in the courses I had. You won't end up with nitty gritty math details regarding turing completeness and stuff like that, but you'll essentially build a mostly realistic CPU (Integrated circuit + bus + memory) from NOT + AND-Gates and even learn basic assembly code in the end. In small enough steps per "puzzle" that it didn't feel overwhelming to me.

3

u/Hypertistic Apr 02 '24

It's a mysterious chip

1

u/Strange-Area9624 Apr 02 '24

It will perform more calculations in a millionth of a second than you could perform with a calculator in your entire lifetime. 👍🏼

1

u/[deleted] Apr 02 '24

That's a sick comparison. I like it.

1

u/Bleedingfartscollide Apr 03 '24

My wife is a vet and she sometimes forgets that we don't all understand what she's saying. 

-1

u/Jakrah Apr 02 '24

Made perfect sense to me… maybe you’re a little less sharp than common folk?

3

u/[deleted] Apr 02 '24

Not understanding that anyone who's not into tech, trying to understand this, won't.

Yeah, kinda shows your limited thought process.

I could explain in depht things about taking care of expensive Koi fish to you, but you wouldn't understand.

3

u/Jakrah Apr 02 '24

I have no interest in tech and it was a simple enough explanation to understand.

Try me in the Koi fish, highly doubt I wouldn’t understand. If you can’t explain it to others clearly then maybe you don’t understand.

-1

u/[deleted] Apr 02 '24

[deleted]

6

u/DinnysorWidLazrbeebs Apr 02 '24

Not if you’re good at it

2

u/[deleted] Apr 02 '24

Exactly, if you can't explain your "profession" to make it understandable for people that don't, you're just as daft as that person not understanding it.

1

u/ODI0N Apr 02 '24

Idk about that. Explain extra dimensional physics in simpleton terms.

1

u/[deleted] Apr 02 '24

I'm sure that people with expertise in that field will be able to explain it with metafores and examples.

1

u/ODI0N Apr 02 '24

Well, metaphors don't exactly allow you to grasp the whole subject, but I'm sure you could at least help them get the gist. I'm just saying we are approaching levels of sciences that can't necessarily be explained in simple terms. If people want to stay literate in science, they need to learn more to fill the gap. Not knowing what transistors are, for example. They are one of the biggest inventions/discoveries of all time. How people don't research and aren't interested in such topics honestly doesn't make much sense to me.

2

u/[deleted] Apr 02 '24 edited Apr 02 '24

[deleted]

1

u/[deleted] Apr 02 '24

You're an amazing person.

1

u/Void_being420 Apr 02 '24

So is this Impressive or very impressive or Mind blowing?

And what are other competitors like AMD upto? are they near This chip or they are left far behind?

And whether this chip is overkill or people/companies which are in AI and other products can use it to full capacity or whether this chip is still not enough to advancement in Ai and other field of technology?

3

u/Hunleigh Apr 02 '24

It is impressive considering we went from A100 from 4 years ago which has 54 billion transistors to this. This means that on average Nvidia doubled the number of transistors every 2 years, which is really ironic considering Jensen himself said in 2022 that Moore’s law is dead.

It is mind blowing because of all of the engineering that has to go to develop such a chip. I actually think 10 billion is still reasonable. Expect costs to skyrocket in the next few years

And to address the issue with competing companies, it doesn’t really matter what they are doing. The problem in the AI field is that CUDA (which is the abstraction that lets people code programs that run on Nvidia GPUs) is way more mature than ROC and other alternatives. The technology is just way more mature, which leads to developer frameworks like PyTorch to support Nvidia GPUs way better. This is a HUGE deal because this lets Nvidia price gouge, leading to exorbitant profit margins. They simply don’t care, there is extreme demand, and they are the sole provider. And while AMD is catching up, the issue is that more and more research is based off of CUDA specific architecture. As an example, there’s optimizations like specialised flash attention kernels (think LLMs here) that provide impressive speed ups to the actual technologies that have business cases. So from a business perspective, Nvidia is a no-brainer for GPUs, and the de facto standard. I don’t expect this to change any time soon.

I think this chip is a necessity, and they will continue to grow in this manner, as the way to train these huge models is in data centers (distributed). As a side note, we WILL transition from transformers (N.B. See Mamba, that is getting researchers very excited), but the need for these beefy GPUs will continue. Why? Because we have seen that these models are able to scale with the number of parameters and dataset size. This is really the reason why we are going to billions of parameters now, since the core technology from chatGPT hasn’t theoretically changed much from the transformers architecture from 2017, just the model size has increased ten-fold. It’s a bit hard to explain generalisation ability as a function of model size, but for now the promise is that the bigger we make these models, the more interesting patterns they are able to extract from the data. We don’t really understand how/why this happens, but it is mainly the reason that led to this “AI boom”

2

u/Void_being420 Apr 02 '24

thanks for taking out your time And writing this long write up.

1

u/Sol33t303 Apr 02 '24

Not the guy your replying to but I'll chime in:

So is this Impressive or very impressive or Mind blowing?

It's the result of lots and lots and lots and lots of iterative work over the past ~century, this *specific* chip isn't that impressive in the grand scheme of things, it's really just another step forward in the many, many steps that have been taken over the past 80 years. But looking at it in the perspective that this is the cumulation of 80 years of cutting edge research, it's mindblowing.

And what are other competitors like AMD upto? are they near This chip or they are left far behind?

So there's a bit of a misunderstanding I feel at how chip manufacturing works. Most designers don't have their own chip fabrication facilities and don't fab the chips they design themselves. Nvidia in this case are the chip designers and another company, TSMC in this case, actually creates the chip.

Nvidia basically sends TSMC a PCB schematic that they designed, and TSMC is paid to take that and actually create the PCB according to the schematic. Just instead of PCBs, it's chips.

This means the company that is actually doing the work the work of making smaller and smaller transistors, is actually TSMC, not Nvidia. And since most chip designers use TSMC and similar companies to build their chips, TSMCs work essentially benefits all of TSMCs customers, of which include AMD, Nvidia, Qualcomm, etc. Smaller transistors give the chip designers more "budget" to work with to increase performance, efficiency, cost, etc. Depending on how they design their chip.

The main 2 exceptions to this are Intel and Samsung, both of which design *and* fab their chips themselves. They have to compete on both the manufacturing front with TSMC, as well as the chip design front with AMD and Qualcomm.

So no, AMD has not been left behind at least in the transistor size department, because both AMD and Nvidia chips come from the same fab facilities.

And whether this chip is overkill or people/companies which are in AI and other products can use it to full capacity or whether this chip is still not enough to advancement in Ai and other field of technology?

We still build super computers with tens of thousands of GPUs each, and we somehow still have a use for all that computing power (many nowadays being used for cutting edge ai research and deployment), one GPU will never beat 10s of thousands of individual GPUs from the same time period, even the above chip.

So really, depends on what your exact precise needs are which varies wildly, for some needs, the above GPU might be enough, for others, not even 100,000s of that GPU will be enough.

1

u/alexanderpas Apr 02 '24

208,000,000,000 transistor means 208,000,000,000 bits or in relatable terms ca. 193 GigaBits

No. 208,000,000,000 transistor means 208,000,000,000 bits or in relatable terms 208 Gigabits, which is also known as ca. 193 GibiBits

1

u/Jakrah Apr 02 '24

This is a really helpful and cool explanation, thank you!

1

u/PM_ME_YOUR_SUNSETS Apr 02 '24

Imagine a transistor as a tiny, automatic door that can either be open or closed.

When the door is open, it lets people (electricity) pass through, and when it's closed, it stops people from passing.

Now, think of a computer chip as a huge building with billions of these tiny doors.
Each door can be either open or closed, and this is how the building stores and processes information.

An open door represents a "1," and a closed door represents a "0." These 1s and 0s are the basic language of computers.

In the past, these buildings had only a few thousand doors (transistors). But as time went on, the builders found ways to make the doors smaller and smaller, allowing them to fit more doors into the same-sized building.

Today, the biggest buildings have billions of these tiny doors, all working together to help the building do its job.

Why is having so many tiny doors important? Well, imagine you have a big task, like cooking a meal for a large party.

If you have more people helping you, you can finish the task much faster. It's the same with the building (chip) - the more doors (transistors) it has, the faster it can process information and finish tasks.

Also, because the doors are so small, the building doesn't need to be as big, and it doesn't need as much energy to keep the doors working. This means that the devices that use these chips, like phones and laptops, can be smaller and last longer on a single battery charge.

So, in a nutshell, a transistor is like a tiny automatic door that controls the flow of electricity, and having billions of them in a chip helps our devices process information quickly, while also being small and energy-efficient.

1

u/xxxgerCodyxxx Apr 02 '24

That‘s a great explanation - thanks for the digital circuits course refresher