Hey Internet! Gordon Ung with PC World. Here I am.
Here we go.
This one's going to work here at Gordon Ung with PC World.
I'm here with a doctor Ian Cutress with
Eric Gordon, with PC World, here, with Dr.
Ian countries.
By the way, go over to the doctor.
Usually
here, Nick Gordon with Dr.
Ian Cutter's of Tech Tech Potato, by the way, go over there like unsubscribe
and of course, smash that belly button on his channel.
The reason I have Ian here is I want to know is a
I and Ian is one of the experts in our space on A.I.
is A.I.
the new core wars.
Because let's be honest, if Intel or AMD really give consumers,
you know, 32 cores, 64 cores in a consumer socket,
I don't think people get excited about it.
It would be about me.
It would be about it's getting excited about like another 300 megahertz
at this point, I think, oh, 6.2 gigahertz.
It's a law of diminishing returns.
Yeah. Yeah.
So I, I honestly think that going forward, the A.I.
hotness is here,
and the only thing that you will judge a new laptop or a new desktop.
By the way, this laptop is the first laptop we have seen with an actual A.I.
accelerator has risen.
A.I. has a new Ryzen nine 7948
chip with acceleration.
But is I the only thing we're going to care about going forward?
It's we're we're we're in this weird dichotomy space.
So, yes, obviously frequency was how much frequency have cores?
How many cores you have GPU, how many GPU cores you have frequency,
and then you also you have different tiers of performance parts within that
A.I. or machine learning.
However you want to delineate between the two is still so nascent that
what all the companies who are doing it, whether it's AMD with a new Rossini AI
engine, whether it's Intel with upcoming Vpu in meeting Lake, whether it's Qualcomm
with, you know, AI, engine hexagon, DSP,
they're all talking about
experiences right now and because it is more of a software
issue than a harder issue to a certain extent these days,
being able to provide users with the experience of what I can do.
I mean, the whole concept of having this machine learning hardware is
you could do it on a CPU, but you could do graphics on a CPU.
Why do we not do graphics on the CPU?
Because it's more power efficient to do it on you.
So why don't we do a I own a GPU because it's more efficient to do it
in dedicated silicon, dedicated silicon on the chip.
So you, you have this scale up and down of what works best for A.I..
So we're getting to a point
now where we have sufficient performance to do things like background.
Okay, blur or eye gaze, correction.
You know, we've seen that already in the big discrete GPUs.
Maybe we can bring that down to something
that's more tenable inside a mobile form factor.
So see,
one of the touchy point about all we in the coal was right now
the whole point about core wars
is we talk about the specifications, which one is better than the other.
You can talk about architecture, it's about frequency.
You can go about number.
Of course, when it comes to the the AI that we have currently in the space,
we don't have easily recognizable metrics
for that, you know, for two for two reasons.
It's not only the raw performance, but also the real world performance
and describing both in a non-technical way to a consumer audience is difficult.
Yeah, obviously people know if I have 32 cause I have a higher cinebench score
or I have a higher geekbench or higher handbrake encode, I get more.
How exactly is a consumer to be able to judge.
Yeah.
Because, because in I we, we don't have cores in, in an actual sense.
There's no decoder, there's no front end, no back end.
So what a lot of the companies are doing
now, especially in the smartphone space, because that's where A.I.
has kind of taken off a little bit
easier, is they just talk about raw throughput this
in and in enterprise.
We're used to talking about flops, just the raw performance of the compute
you have because these aren't floating point operations most than the integer.
We call them ops in AI.
So we're talking about gigabytes and teraflops.
So everybody with our hardware will talk about the amount of teraflops
they have inside that chip, which is a bit of a misnomer
because the software doesn't always run at the peak of,
you know, it's
you can talk about number of giga flops
or teraflops you have in your GPU, but your game is spiking
as and when it needs to performance right to hit your frame time.
So A.I. will be the same.
You'll have this teraflops value of the dedicated AI hardware.
You can then talk about the power that consumes so ops per watt.
So especially in mobile form factor devices like this,
that's going to be important.
Aside from that, there's no real good
fundamental way to talk about raw performance.
Now, we could talk about benchmark performance.
We do have some benchmarks that are already out there.
Geekbench six implement some of this.
You know, Procyon is going down that way.
There's no use of Cinebench for AI, which is unfortunate.
But that's that's indicative of the market we're currently in.
We have the hardware is such rapidly improving year on year
or almost month on month, the month the software is evolving even faster.
So finding the right way to implement this, these machine learning algorithms
is evolving its imagine, you know, a different x86 CPU
coming out every month or every two months,
but with a fundamentally different architecture every time.
You can't just create one benchmark and point at six different designs
because you'll be optimized for all of them, right?
So that's still a work in progress in machine learning,
especially for consumers. I mean,
at the end of the day, it
does mean that Intel AMD and again, you know,
Qualcomm, Apple, they don't really speak about performance right now.
They speak about experience.
And because that's also more widely applicable to the you know,
to a consumer audience. Right.
And to me, it sort of speaks to the problem,
you know, as hardware community is for reviewers
is looking for ways to measure it.
And, you know, you know, we do have this issue where
if you're you have standardized benchmarks and you're
running somewhat older, like even a two year old version of it,
it doesn't really move that much because honestly, there hasn't
been the revolutionary performance impact in a CPU general purpose in two years.
But like you're saying,
if it's like every month, then
your benchmarks are essentially outdated by the time you publish them.
Like the performance metrics are kind of meaningless
if the software has shifted so much.
Well, we made a point of gaming, right, Because we do get driver updates
seemingly every week, and sometimes they add an additional performance.
So it could just be a case
of every time you want to do an air test, you just have to get the latest variant.
But it's it's arguably more difficult than that because the hardware becomes
so optimized in very specific directions for machine learning.
You may have a hardware that's focused very much on visual.
We may end up with hardware very focused on things like chat with large
language models that should look natural language processing.
And there are also
features like Adobe
Generative Filled is currently in beta that's being tested a lot
now that could all again be run on CPU or it could be run on GPU
slightly more power efficient or is being run on this dedicated hardware.
One of the one of the unfortunate issues
right now, I think as well is
some of these AI, some of this A.I.
hardware is relying on
things that aren't standards, right?
So imagine if you have a model that you run a one man machine
and you get a result.
You run exactly the same model.
Say, you know, imagine it's like a cinebench render,
but you got a completely different result on a different AI hardware
because they're implementing certain things
in the architecture differently because they're not standards.
It's a space that's unfortunately doesn't have many data type standards.
In order to do that.
I've spoken with Intel and they've said across all their hardware,
whether it's CPU, GPU, they're trying to make sure it's unified.
You get the unified result
and I think wants to do something similar.
But even competing against companies, maybe it's not much of an issue with A.I.,
but in terms of for us for benchmarking, I mean,
as enthusiasts, we want to see the scale.
We don't care about binary.
Yes, No, we want to see the scale performance, performance per watt, right?
It's not there yet
because of a lot of issues which consumers shouldn't care about.
But unfortunately they're getting the hardware this year to do it. Hmm.
Yeah. I mean, again, because this is real hardware.
It's got I mean, honestly, you've got the CPU, you've got a dedicated
you've got the dedicated Ryzen API,
you've got the integrated graphics and the
Ryzen chip, and then you have the G forces as an RTX 47 here mobile.
So yeah.
And the guys that
get these laptops speaking about AMD specifically,
so we've got this, you know, 7040 series that has the AI engine,
but it very is like a version 0.3 because it will only be available
to certain business customers in order to develop their particular
software using Vitus AI, which is a Xilinx based software platform.
It's not the no tensor
TensorFlow PyTorch, so a typical machine learning environment.
So as a result, AMD's working very closely with Microsoft
to enable it through the Microsoft ecosystem.
So things like opening your camera app and getting background
blur, we've actually just tried to play with it a little bit now
and eye tracking, things like that.
So they want it.
They want it more to be an integrated consumer
or business experience, not a development platform right now.
I think once AMD with the A.I.
engine and then Intel with the mid-to-late CPU,
once they become developer friendly, we're going to get a lot
a number of people coming out with different ideas on how to use them,
and I'm
pretty sure they'll think of plenty of ideas
that either AMD or Intel haven't thought of.
Yeah, and you know, I mean, this this all points to the wild West
we're in right now.
It's like us.
It's almost like we're seeing a reset
because again, we've got gigahertz, gigahertz, gigahertz wars.
We've got the core wars now it's like everybody it's a wide open field
where everybody gets to come up with how they're going to market it as well.
Do you do the tier ops as you're saying?
Do you do troughs per watts and like I don't or.
Yeah, or do you talk about images generated per second on stable
diffusion, but maybe you've got a very customized stable diffusion model.
And yeah, we get into all that tit for tat.
I will say that as we progress, you know, between these wars, frequency,
gigahertz GPU, whatever it becomes more and more complicated
to understand and ultimately what is enough.
Yeah, I think we only figured we will only figure that out
in machine learning once we have the use cases available to us.
Things like having localized copilot, things like you,
you're helping do your say generation and or
things like that.
It's kind of Wild West for a good couple of years.
And I think the other thing too is like,
you know, a lot of times consumers want to know,
I need to know what is the best, like the one thing that's the best.
And it kind of sounds like there is no sort of one thing, right?
Because you got like in this laptop itself, you've got two general purpose CPU
cores, you've got dedicated high core,
and then you've got the the discrete GPU as well as integrated.
Ridgeview.
Yeah, it's almost like you kind of want all of them in a way
because you don't know where the future's going to end up that you want.
You want the performance and yeah, performance.
And I think especially for mobile from its efficiency, it's
people have been asking, well if I run
a machine learning workload, will it run on the CPU, the GPU or the engine
or can it run across all three and or can it choose which one to run on?
So there's some so some companies are coming to the realization that maybe
I want to run it on device, maybe I want to run it in the cloud, you know,
which is more power efficient or which is what do I need the latency.
Instead,
though, it running across different
CPU GPU isn't a thing yet in the same way that in a game
your rendering happens on the CPU, but perhaps some of the control
happens on the CPU, you don't actually do the rendering on the CPU.
So there's also things like that to get to understand as well.
Again, very nascent, very early time space
while rapidly, rapidly growing.
Yeah.
And it just says it's going to be messy, it's going to be messy
for quite a few years.
But the thing to me that I got a question real quick and would you give up
general purpose cores for more dedicated function cores
like a course? Hmm.
Okay,
let me rephrase that.
Do you have enough cores, General?
You know, it's the whole point about using
new a process node is to get more transistors.
And what do we spend those transistors on?
Apple spends them on audio
video encoding accelerators, and they're doing very well with that.
Do we
spend more transistors now on machine learning?
I think it will grow and it will need the metrics
to essentially confirm how that will grow.
To be honest, really
as a desktop, I want everything
as as a mobile device.
I want the environment that gives me the most battery life
I think of, you know, for the performance, the most battery life.
It's that that's that's my bugbear right now, battery life.
That's only because I have a laptop that I kind of screwed up.
Yeah.
I mean yeah, actually that kind of brings up the thing is like
because when you think so, this laptop has risen.
I bet it has a GeForce RTX 4070 in it.
Yeah that does that whole fancy AI correction stuff and blurring too.
Why in the world do I need to duplicate that exact same functionality
elsewhere in the system?
Yeah. No, you only use it for one.
And there is an advantage though, right?
You were pointing this out earlier. Yeah.
You know, the power efficiency of running it on the eight cores.
But, you know, for this device, you know, razor blade 14 is the idea
is that it's a gaming laptop for streaming but you also may still be, you know,
using it for teams and zoom right
And so having that low power or so for my thing as a content creator,
I want some of those applications to be available offline.
So maybe I've got a prerecorded video
I want to adjust the tracking on for a certain section,
so having those tools available would be really nice.
I've been I've been begging
Nvidia to make broadcast viable for content creators like that.
Right.
But yeah, it's to your point, it's still the Wild West.
We're still trying to figure out the exact use cases
where it works, why it works, and where it works best. Hmm.
Okay. So it's going to be messy.
It's going to be messy for both you as a consumer.
It's going to be messy for us as hardware reviewers to tell you what is important.
But it's also very exciting because actually getting in the ground
floor, some, you know, again, you know, changing technology like AI is
is about as exciting as what it was like when we saw the first,
you know, CPU in a lot of ways, right?
Both Xilinx for the ancient AMD and the MIT videos team or the team
at Intel are absorbing all the feedback they can get for future generations.
It's if we keep coming up with ideas, with requests they want to implement
and cover most of the market that they that they possibly can.
And, you know, this is typically a more embedded style, you know, corporate thing.
So now moving it into consumer means that they have to think in a consumer mindset.
So yeah, they're being sponges right now and it's really exciting
to the fact that they're reaching out to people like me,
people like Gordon to take all that feedback.
And the reason why we have feedback is from our own experiences.
But B, from you guys, from all your comments down below,
you know, understanding what the audience wants to see in these parts.
So please do get your comments in below and let us know what you think
the future of AI needs to be.
Actually, that's what I want the A.I.
to do is o comment for me on social media
so I don't even have to get into social media.
I can go in or it learns how I'm going to respond.
Responds the exact same way you've been watching Black Mirror.
Oh really?
I haven't seen it, but I if they have that technology, that would be wonderful
because it might actually help with the Internet.
But anyway, this has been very enlightening.
Again, it's going to be a really exciting a few years now because
consumer clients, PCs
with a AI are formally here and the actual
air wars are officially starting today anyway.
And I'll take one of these. Thanks. Thanks.
Dr. Ian Kutcher.
She's running away with that Razer Blade 14 and go over
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