Good morning, Ronnie.
Good morning.
How are you doing?
Good, thanks. Good.
Thanks so much for coming to AWS Studio.
Yeah, it’s a pleasure.
Thanks for inviting me.
Why don't you tell us a little bit more about your role at NVIDIA?
Sure. So I'm the Senior Vice President responsible for telecom
at NVIDIA, which has kind of a far-reaching aspect
because NVIDIA’s involved in so many different
avenues, from applications to infrastructure,
and telecom is now a big focus for us, as one of our focus verticals.
And so how are you using the cloud today to digitize industries and beyond?
NVIDIA, we really focused a lot on moving workloads to the cloud.
The kind of workloads that NVIDIA has focused on
has been ones where you can accelerate, compute and acceleration
performs a distinct advantage to that particular application.
And you see it in things like intelligent video analytics
or optimizations of certain algorithms for computation.
And then also, of course, AI,
and the different forms that AI keeps evolving.
And NVIDIA is very much focused on moving those workloads from the cloud.
But what's very important for NVIDIA is that traditionally,
we always keep the same infrastructure, whether it be on the edge or in the cloud.
So the software is independently run, whether it be on the edge
or within a centralized public cloud, for instance.
I really want to jump in to the different nuances around the edge,
but maybe talk to me a little bit about the customer journey,
a specific customer example or pain point that you've seen
specifically with
new form factors in the evolution to the cloud itself.
Okay. Good question.
So as we're in the telco environment, I'm going to focus in on telcos
and telecom operators.
You know, telecom operators really now have some kind of unique challenges
with 5G.
One is the ability to monetize the investments they've made
in spectrum and infrastructure.
And the other is to optimize their operations
and to enable their operations to be taking advantage
of some of the newer technologies that are out there.
So if you look at, say, the first case, which is the ability
to run new business cases, new monetization schemes,
it hasn't really traditionally
been within the telco customer base.
And that enterprise is a new entity.
So as a telco starts to go through that process,
there really is an ecosystem play, which is quite unique and quite new.
What NVIDIA has been able to do is bring our AI
and cloud ecosystem to the telco.
So, if you think about autonomous
retail, for instance, 5G connected cameras
within a retail store. Somebody walks into the retail store
and shows a credit card, picks up
items, and walks out of the retail store, never having to checkout.
That hasn't traditionally been a telco engagement, but more and more
like Verizon is working with AFI for instance, with NVIDIA and now
intelligent video analytics,
this now becomes a new business model that Verizon can support.
And so we're able to bring out,
as I said, our ecosystem of ISV developers,
as well as SIs to the telco
and say, okay, now in your network that you're invested in,
you have all these new opportunities to monetize.
So it's really about building an ecosystem
with the partners, with new form factors, with the technology.
That's definitely a really critical part.
Maybe let's dive a little bit deeper in terms of the edge itself.
Help me understand a little bit better the distinction between the edge,
the far edge, how’s NVIDIA playing a role in that distinction?
And how does that distinction manifest itself in the cloud?
For us, there isn't,
I mean, the location is is really the distinction.
There isn't really a difference in infrastructure per se.
It's scale of infrastructure. It's the same, as I said earlier, the same software,
the same applications can run on the edge as can run in the centralized cloud.
What is unique, I think, is some of the application driven requirements.
So, for instance, latency is often talked about.
You know, if you're putting something at the edge and you're
using robots,
for instance, in a warehouse, you want that
roundtrip delay to be as low as possible because you're actually monitoring
robot movement and you want to be able to control that robot movement real time.
Some of the intelligent video analytics where
you don't need that latency,
for instance, for other applications
where you're uploading information, can be centralized in the cloud.
Training of new models is probably best done in the cloud.
On the NVIDIA infrastructure, we can run both
training and inference on exactly the same
infrastructure, but it's just the scale that's
the larger training is more efficiently done in the cloud.
But, you know, I think people get hung up on what is near edge far edge.
It really is very much application
dependent and also data gravity dependent.
Many enterprises want to retain data OnPrem,
and that then becomes what used to be called
kind of a local data center or server farm becomes now a far edge.
So maybe let's dive
a little bit deeper in terms of how AI and ML will continue to evolve.
Maybe specifically for telcos, what are you seeing in
some of the use cases that you're most excited about or that
you think will start to pick up in the years to come?
Yeah.
So the first really was intelligent video analytics.
It was the
enablement of smart cities with cameras.
It's surprising how much deployment there’s already been in these areas.
And then we start to see robotics primarily in industrial use cases.
So factories and warehouses,
those are kind of the first movers in the AI
pipeline of applications.
And as we go through this now, we start to see more and more deployment
and use cases of autonomous mobile robots, for instance.
Where there we can create digital twins of an environment
and you can have remote control,
remote access through a digital twin of an environment.
So digital twinning in our platform that we call Omniverse is very exciting for us.
It's the complete visualization of an environment in real time.
And we're training cars every day in simulation and then
deploying software over the air to cars to enable them to learn.
So I think the digital twin aspect is going to be very, very exciting.
As well as now, some new forms of AI as well, like the generative AI
that we're starting to see becoming more, I was going to say more prevalent.
It's not prevalent yet, but we definitely anticipate that happening.
Absolutely.
And another use case that I think is very important to us at AWS
and I'm sure important to you as well as sustainability.
And I think there's a lot of really cool things that NVIDIA’s
doing today around driving carbon efficiency for operators.
Can you tell me a little bit more about some of those stories?
Yeah, you know, so the whole premise of accelerated
compute is one of sustainability, really.
You can run workloads on traditional CPU's,
but as Moore's Law has started to come to an end,
and I think this last year or last year really did come to an end,
now the cost per function cost per transistor is actually going up.
What's been happening is more and more functions have been offloaded
to be accelerated, sometimes at closer to the point of use at the edge.
But that overall drives down the power footprint
because now you're running more efficient compute
either at the point of use or just generally in the cloud.
And that has a way of still maintaining innovation in the compute cycles,
but driving down the power required to enable that compute.
Maybe just to close out here, are any things you're looking forward
to most as you continue to collaborate with AWS?
Yeah,
so I mean, AWS is one of the, I think, very strong partners for NVIDIA.
We are moving much more, as I said earlier, to workloads
in the cloud. And I think in the telco environment
that really means that there's operators that will need the infrastructure,
available to be able to run a lot of those workloads.
And I think AWS is the perfect partner in being able to provide that infrastructure
and then the orchestration and support to enable those applications to be,
you know, where you need it, when you need it.
Well, it was really great having you here today.
Fascinating discussion and hope to see you again soon.
Well, thank you very much for the invite. Thank you.