“So, I think as we look at all those pieces, I think we feel good that the growth rate is going to be significant and sustained over the next few years.”

In context

“ook at is growth rate and how do we get to those growth rates. I think we expect units to grow sort of substantial double-digit percentage. But you should also expect that content is going to grow. So, if you think about how important memory and memory capacity is as we go forward, you can imagine that we'll see acceleration there and just the overall content as we go to more advanced technology nodes. So, there's some ASP uplift in there. And then, what we also do is, we are planning longer-term roadmaps with our customers in terms of how they're thinking about sort of the size of training clusters, the number of training clusters. And then, the fact that we believe inference is actually going to exceed training as we go into the next couple of years just given as more enterprises adopt. So, I think as we look at all those pieces, I think we feel good that the growth rate is going to be significant and sustained over the next few years. In terms of what's in that TAM, it really is accelerator TAM. So, within accelerators, there are certainly GPUs and there will also be some ASICs that are other accelerators that are in that TAM. As we think about sort of the different types of models from smaller models to fine-tuning of models, to the largest large language models, I think you're going to need different silicon for those different use cases. But from our standpoint, GPUs are still going to be the sort of the compute element of choice when you're talking about training and inferencing on the largest language models. Operator: And the next question comes from the line of Joe Moore with Morgan Stanley. Please proceed with your question. Joseph Moore: Great. Thank you. I think you talked about the MI300 cloud workloads bein”