Vinny Santos

speaker

168 appearances 1 recordings 1 series first heard Jul 2026 last heard 31 Jul

Vinny Santos’s voice in public audio — every appearance, attributed to the second.

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Recordings per month over the last 12 months — 1 in all, peaking in Jul 2026 with 1.

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Heavy Networking · HN836: Optimize AI Traffic on the WAN with Ciena’s Integrated IP Networking (Sponsored) · 31 Jul 2026 podcast

LLM is just like the beginning of the story.

We are like the tip of the iceberg.

There's a lot that will come with AI and mood model inference that will really change the model.

Yeah, I think that a good example of an amplification of, I'm not saying of a problem, but a change in the behavior, the characteristic of how the traffic flows is like multi-cloud.

So multi-cloud is not new, okay?

We've been like playing with multi-cloud, mostly private and public clouds for a while now, but with this, they grow with the market of the newer clouds or newer scalers, where they're going to bring more specialized agents or AI applications and enterprises start to take advantage of

I want this application from this specific provider.

And maybe I want to store my data in another provider and I'm going to use an agent of a third one.

So this multi-cloud characteristic of the network and how the service providers will deal with how to really work this traffic in the proper way, it's an additional challenge or a big change on how this network needs to be architecture.

And it's an amplification

that will be really, really important on this AI moment.

Yeah, even if you decide I'm going to have like a bigger pipe with my service provider, the service provider will need to be able to interconnect those multiple clouds.

Because specializations, I think that will be a big part of the game.

Okay.

And mostly for multimodal AI.

And then I think like on enterprise side, they're going to definitely want to take advantage of who can help them better.

Yeah, I think that when we evolve the network infrastructure for this centralized fabric, as Rav mentioned, and go to the edge inferencing, we start to have a different type of connectivity.

So if inference will move from the centralized to be more distributed, the service providers are the ones that have the capillarity.

Because on the training side, it's just having almost like a point-to-point connectivity between big data centers and to try to push as much bandwidth as you can on that link.

But now when we start to do edge inferencing and there's more multi-cloud type of environment, you need to have capillarity.

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