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Reshaping Workflows with Dell Pro Precision and NVIDIA RTX PRO GPUs
GTC Bonus: Accelerating 6G Innovation with Emeka Obiodu of NVIDIA
00:05:13
On our final GTC Bonus episode, NVIDIA Product Marketing Manager Emeka Obiodu shares how the company is giving researchers a full-stack platform to train, simulate, and deploy next-gen wireless networks.
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Transcript13 lines
- [00:00]
Unknown Speaker
Foreign.
- [00:05]
Host/Announcer
Welcome to reshaping workflows with Dell Pro Precision and Nvidia, where innovation meets real world impact in high performance computing.
- [00:20]
Logan
This is Logan live from GTC 2026. We're back, shockingly enough, with another demo with this one, Telco 6G on the Dell Promax GV10 and the Nvidia Spark. I have Emeka here who is the expert. So Ameka, before we get in, ask some questions, tell us a little bit about what you do in Nvidia and what your role is.
- [00:39]
Emeka
So I do 6G developer marketing at Nvidia and today at the booth here we are showcasing how we are training, simulating and deploying 6G on the djs pack.
- [00:50]
Logan
Okay, you've already blown my mind, so let's dive into it a little bit. So we know GB10, you know, AI personal supercomputer, really great for, you know, training, development, computer science work. But it's the first time I've heard anything about Telco 6G on the Spark especially come from a simulation standpoint. Right. So tell me a little bit about what you're doing and kind of the workflow that's on the Nvidia Spark with,
- [01:14]
Emeka
with 6G, it's pretty straightforward for us really. It's really how do we democratize and accelerate CZ research? So you know, 6G is currently under development now We've got thousands of researchers around the world who are working on new concepts, new algorithms to develop the 6G AI native 6G algorithms. And what we are doing for them is to provide them a platform that they can use to accelerate their work. So we've got on Spark you could use our aerial framework which enables them to actually code program New 6G algorithms in Python and then compile this in Tensor ROT engines. And they're able to iterate on this until they get an optimized workload that they are comfortable with. Now once they are finished with the training, they can then move on to the simulation. Right. Again, here we have the Spark able to support a simulation environment with the Nvidia Aerial Omniverse Digital Twin. And once this is a physics, in a physics accurate real time simulation environment and if you're able to put the algorithm in that space and you're able to understand and see exactly how much performance improvement you're getting and when you've done your training and you've done your simulation, what next you deploy it, you know, so instead of waiting for let's say the big deal to come and tell you, okay, I'm gonna put that there you actually, inside that small lab you're in, you're actually able to deploy it and understand the exact performance capability that you are getting out of that scenario. That's really what we are showcasing here on the booth here. Enabling researchers around the world to be able to accelerate their CC development work.
- [02:50]
Logan
Okay, makes a lot of sense. So for example, who's not, somebody's not a telco expert, right? At the end of the day with 6G, I mean we're talking simulating, iterating and like where do we put the towers, where do we put the sensors, where do we put everything to optimize performance to cover an area like for example the city of Chicago or Austin, Texas? So do you have any success stories of where customers have used this to. You don't have to name names, nothing proprietary, anything like that. But where you've seen them put this into practice and maybe the time savings that they've had or more rapid deployment or any kind of use cases or proof points from what we were running on Spark.
- [03:29]
Emeka
What we are running on Spark is still for development stage so it's not something that goes commercial for now. So it's still pretty much at a deployment st and we are working with lots of researchers around the world who are doing some great stuff on this.
- [03:42]
Logan
So it's still, I mean, I mean that's the best part I think about Nvidia is that there's always something new, you know what I mean? I mean whether it's, what are we calling it like Open Shell or Safe Shell or Nemo Claw or any of the new stuff that we're launching, they're always doing something new. So do you have, I mean you probably can't share a timeline but ultimately with kind of the development work that's being done here, like what do you see kind of 6G deployment simulation looking like and say once it launches like a year from then like what will the world look like from you know, a telco standpoint in like a year after this product launches?
- [04:17]
Emeka
I mean as Jensen said in his keynote, we are building the AI infrastructure for the next generation. That's really what CC is going to enable. And we're here to support researchers who are working towards that goal. That's our ambition and we will deliver for the industry.
- [04:32]
Logan
You heard it first. Nvidia is here to deliver. No, it's true. Like, I mean I didn't get to catch the keynote cuz I was running around like a crazy person. But this was great. I really appreciate the time. Thank you so much. So we'll see you on the next one.
- [04:52]
Unknown Speaker
Do what you want. Do what you want. Do it. Do what you want. Do what you want.
- [05:05]
Host/Announcer
This podcast was produced in partnership with Amaze Media Labs.