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Chapter 1: What changes are AI bringing to network architecture?
Good morning, Michael.
Good morning, Sam. Now, Sam, have you ever tried to do something with the wrong equipment? Like for me, often I will need to unscrew something and I'm too lazy to go downstairs. So I use whatever I have in front of me, car keys, maybe a credit card. None of them work. And then I have to then be like, fine, I'll go downstairs and find a screwdriver.
I feel like you would get the right tool for the right job every time.
Usually I try. What immediately came to mind was last Discover Barcelona, I brought our editor some food very late at night and I also hadn't eaten, but I forgot to bring utensils. And so we had to make do with what was around and those were wooden coffee stirrers. So we ate trying to use them kind of similar to chopsticks. It was not very effective, but we did at least eat.
I've definitely done that before. And it's now why I have wooden cutlery in the glove box of my car. Anyway, as you know, sometimes something new comes along and suddenly everything that came before it has to change to keep up with the times. And as per this episode, even the way that we design our technological infrastructure, it will all make sense. I promise. I'm Michael Bird.
I'm Sam Jarrell.
And welcome to Technology Now from HPE. Now, Sam, AI hasn't just changed how we interact with our technology. AI itself interacts with our current technology infrastructure in a completely novel and different way.
And it's literally forcing us to rethink how everything is designed, right?
Yeah, exactly. AI has a set of quite specific requirements, which means that the network architecture required to support it has to be completely different to what came before. AI doesn't sleep and the data needs to be kept live to give accurate results.
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Chapter 2: How do AI requirements differ from traditional computing?
What does an AI native network look like? And how does that differ to a traditionally architected network?
In the case of AI, whether you're doing ingest for training purposes or you're doing inferencing, you're sending rich content upwards. And you were downloading rich content because you could be using AI-based classes or virtual gaming machines and things like that. You were sending rich content upstream and you were delivering rich content downstream for what you were doing.
So the AI started superimposing symmetric data patterns and traffics. The second aspect what AI did was it really created a notion of agentic AI where the person is no longer there and agentic AI is running all the time, every time, which means the data never has peaks and valleys and you can't multiply, so you can't actually load balance across different things.
And you have to actually have a network that is provisioned to handle this load constantly every time. The last thing that AI did, which we didn't talk about in the streaming services, when you stream the service, if it was coming from a distant place in the network, it would say buffering and you would get really irritated. So we started building caches towards the edge.
But with AI and AI data, you no longer can build it with caches because AI data gets obsoleted the moment you try to cache it. And so the network architectures have to rethink the way we do stuff in AI. Network is symmetric. the network is always on and the network is not cacheable.
Caching in a traditional network is quite a crucial way of saving bandwidth. We're saying in the sort of post-AI world, can't really cache things because every request is essentially unique. Correct. So these networks run 24-7. How can you ensure they're being used efficiently?
Because presumably, if you're running your network 24-7 or if you're architecting a network, if you're running 24-7, that's potentially more investment in terms of equipment, in terms of power, energy. So how do you make sure actually that is being used effectively?
You hit upon something very interesting. AI is automatically a distributed element. We all know that a single GPU does not carry the AI workload. So you have to build clusters of GPUs to do AI. Now with power and space constraint, you can't put the clusters geographically located. It has to be distributed. And we talk about AI build-outs that scale up within a rack of a data center.
scale out within the data center, interconnecting all these GPUs within the data center. And now we're talking the third dimension of scale across geographies, where the network elements and the network pieces have to actually tie in and build the scale across.
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Chapter 3: What does traditional network architecture look like?
we could be using AI to do our work and getting answers. There has to be a consistent way by which AI responds to us and gives us predictability in terms of how it operates so that the user experience is enhanced, which goes back to all of the things that we talked about in terms of network architecture,
transformation in the core transformation in the dci transformation in the edge so that you can deliver ai in the best possible user experience and we need to think about it and we need to also think about the growth when this explodes and inference is taking on it is going to grow exponentially so we need to think about how these networks are going to just multi-fold grow in the next few years people even talk about 5x growth in the next three to five years so that is what is upon us
okay that brings us to the end of technology now for this week thank you to our guest ae nadarajan and of course to our listeners thank you so much for joining us yes and if you've enjoyed this episode please do let us know rate and review us wherever you listen to episodes don't forget to subscribe so you can listen first every week and if you want to get in contact with us send us an email to technology now at hpe.com subject line any ideas sam
Can't cash.
Can't cash. No cash. Technology Now is hosted by Sam Jarrell and myself, Michael Bird. And this episode was produced by Harry Lampert, Izzy Clark and Eva Higginbotham. With production support from Alicia Kempson-Taylor, Zoe Revis, Becky Bird, Elissa Mitry and Janessa Ayosh. Our theme music was composed by Greg Hooper.
Our social editorial team is Rebecca Wissinger, Judy N. Goldman, and Jacqueline Green. And our social media designers are Alejandra Garcia and Ambar Maldonado.
Take Mind You Now is a Fresh Air production for Huda Packard Enterprise, and we'll see you at the same time, the same place next week. Cheers.
Bye, y'all.
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