“And as I indicated in my remarks, we see that revenue as part of networking revenue, $4 billion and networking -- AI networks and going -- doubling almost during 2024.”

In context

“that if a customer has a business model that is substantial and have resources which obviously supports that, is getting to a stage that it might make a lot of sense to design AI compute engines, which comprises memory as well as the compute engine itself, then that can be tailored or better word, customized for their particular requirements on applications, on their particular LLM model. And we’re seeing this as we all are seeing LLM models continue to change and the face -- the shape of generative AI dynamically change more and more, where training and inference are now starting to, in a way, converge and the chip designs are changing. And we are seeing that in the way we design specific custom chips for hyperscalers. That’s interesting. So that’s a very interesting opportunity for us. And as I indicated in my remarks, we see that revenue as part of networking revenue, $4 billion and networking -- AI networks and going -- doubling almost during 2024. Nothing new. We have said that before. And if anything else, we are reinforcing that particular guidance. Now, networking is particularly interesting as you heard in my opening remarks. It is accelerating as fast as our AI accelerators, the compute engines are growing. And we see that growing hand in hand and particularly so as training -- continuous training of very large language models with very different and very large parameters keep going on and things keep changing. So we’re seeing no slowdown, in fact, in the update on building out this AI networks. If anything else on average, we are seeing a doubling on size of those networks across the board. So, that’s -- to your answer question. Yes, I fully concur with AMD, when they indicate that it looks like demand appears to be accelerat”