Original subtitles

it's pretty clear where nvidia's

priorities lie these days we're here at

the computex booth of one of their

Partners gigabyte and this is the entire

gaming showcase

that's because they like the rest of the

industry understand that the future of

computing lies in the data center

that is where the grace Superchip comes

in under each of these gigantic heat

spreaders are 72 of nvidia's Grace CPU

course connected together using what

Nvidia calls the Envy link chip to chip

interconnect for a total of

144 cores except that's just one of the

nodes This Server from gigabyte accepts

not one not two but four of these

modules in its four separate nodes that

is an absolutely mind-bending

576 cores in a 2u server rack but these

are not the types of CPUs that you have

in your gaming PC at home those

processors from the likes of AMD and

Intel are based on the x86 architecture

so similar to what Apple did with their

M series M1 and M2 processors Nvidia is

making use of a different processor

architect picture called arm

and uh we actually did get permission to

do this we're going to be taking a

closer look here

it doesn't look much like it but this is

the same style of processor that you

might find in your phone arm processors

have a lot of advantages first and

foremost being that they're typically

more power efficient thanks to their

relatively lightweight and structure set

so so much so that Nvidia claims these

gray CPUs have twice the performance per

watt of the latest x86 chips

but the disadvantage is they also

require software like your operating

system and all the programs you need to

run to be coded and compiled

specifically for arm now for the PC

market because 86 has been the standard

for so long it's difficult to justify

switching over to arm it would cost you

so much in terms of backwards

compatibility but in the data center the

types of customers who are going to buy

a processor like this are usually

developing their own software anyway

like let's say Google to run the

algorithms that power Google search or

YouTube recommendations for them

switching over to arm isn't as big a

deal and in fact companies like Amazon

who are developing their own arm-based

CPUs are already doing it and very

effectively I mean hey if my next gaming

CPU could be half the power draw and the

same performance of my current one I'd

be stoked but this is even better

imagine if instead of one computer

you're talking thousands or tens of

thousands the savings start to become so

large that it's less a question of can

we afford this migration and more a

question of can we afford not to make it

now I didn't ask permission for this

part but nobody seems to be stopping me

or even really paying attention to me so

let's take apart Grace Superchick

on each gray Superchip is up to 480

gigabytes of LP ddr5x ECC memory per CPU

and what's really cool is that that can

actually be accessed by either CPU over

the nvlink interconnect that's how fast

this new Envy link is the only downside

to this approach since we're making

comparisons to Apple is that just like

with your M2 MacBook you better decide

how much memory you want in your server

right at the time you buy it unless you

want to replace the entire compute

engine while you perform a memory

upgrade

given that the rumored price of their

h100 gpus is a hundred thousand dollars

I don't even want to know what this

thing costs but hopefully you get a bit

of a discount when you buy it together

with the gray Superchip CPU let me show

you this

can't believe they're letting me take

this off the wall

foreign

okay

success we have dropped nothing

important so far today

this

is Grace Hopper on the one side we've

got the same at 72 core Grace arm CPU

that we just saw but on the other side

the ooh shiny latest Nvidia h100 Hopper

GPU you can probably see where this is

going just like with the Dual CPU Grace

module these two are also Envy link chip

to chip interconnected meaning that the

CPU and GPU have a whopping 900

gigabytes per second of theoretical

bandwidth to talk to each other so for

some perspective a GPU using a full 16

Lane Gen 5 pcie slot would only have

about 64 gigabytes a second of peak

throughput that is 1 14 as much as this

and that's far from the only

mind-bending number that this thing is

capable of while the CPU side uses the

same up to 480 gigabytes of lpddr5x

for the GPU side they need much faster

hbm3 memory that runs at a whopping four

terabytes per second

it's about four times faster that's why

the memory needs to be right on the

package right next to the GPU

now all that is great and cool and all

but hbm is very expensive and as you can

see there's only so much space here so

the h100 only gets 96 gigabytes of

memory okay yeah for gaming that

certainly sounds like a lot but AI data

sets can involve terabytes of data so it

can get used up very quickly that's

where the interconnect comes in it

allows the GPU to access the cpu's

memory in a very direct and transparent

way giving the h100 hopper GPU a

functional memory capacity of nearly 600

gigabytes in Practical terms according

to Nvidia that puts Grace Hopper

anywhere from about two and a half times

to nearly four times as fast as an x86

CPU paired with their last generation

a100 GP and where things get really wild

is in the data center with an Envy link

switch system you could connect up to

256 gpus together giving them access to

up to 150 terabytes of high bandwidth

memory I mean you guys remember that

crazy Mars Lander demo that we showed

off on the petabyte of flash array you

could load that entire 1 billion Point

data set into memory in that

configuration and still have 50

terabytes to spare now this module

get more power hungry than the Dual CPU

version a thousand versus 500 watts per

module but I mean that's for CPU GPU and

RAM for both of them and with this kind

of performance

of course not everybody wants to move to

an arm hybrid CPU GPU architecture so

Nvidia is still going to be supporting

their uh old-fashioned configurations be

they h100 gpus and a pcie form factor or

their hgx h100 with up to eight smx-5

gpus each of these draws a massive 700

Watts making an RTX 4090 look like a

child's play thing and supports Envy

link between these gpus and NV switch to

additional servers this is the

g593-sd 0 and gigabyte was very proud of

the fact that they are the first Nvidia

certified HDX h100 8 GPU server in a 5u

chassis man that is a lot of compute in

a tiny space Jake's in my ear here

telling me I should pull one of the

power supplies but if you've noticed it

getting darker it's because they're

actually shutting down the pre-show and

they're trying to get us out of here but

there is one more thing that we wanted

to talk about where'd it go dang it Jake

no oh my God oh my God

okay well this is uh no wait this isn't

the one I wanted okay it's a connect X7

this is an even faster network card so

this is probably the first Nvidia

developed melanox network card given

that uh the acquisition was what about

two years ago six six

yeah but Nvidia didn't buy melanox just

to make faster connectex cards

no it was to make these this is a blue

field three so it has networking on it

this is a 100 gigabit one but it's

available it speeds up to 400 gigabit

but what's really special about it is

that it has up to 16 processing cores on

it why you might ask well just like in

the old days when we started offloading

tcpip processing to our network cards

rather than having our CPU handle them

this is going to offload all kinds of

interesting things like encryption of

your network traffic or say for example

handling managing your file system

because when you're someone like an AWS

and you want to squeeze as much revenue

as possible out of every CPU in your

data center you don't want it handling

stupid BS that you could just offload to

your network card so the idea here is to

free up CPU resources that can be leased

to customers by putting them onto the

network card itself and this is

especially true for software where the

developer sells you a license per core

that's why even though these are going

to be wildly expensive

a lot more than the 4060 TI Nvidia is

going to sell shed loads of them just

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why don't you check out oh the petabyte

gosh

this is a good one well we're at the

gigabyte Booth come on uh the g-rad one

yeah actually no new one new new

one a three

One X four I mean damn it