“Well, we would like to serve all of it. And I think we're on track to serve most of it.”

In context

“for questions? Sarah: Thank you. At this time, I would like to remind everyone in order to ask a question, please press the star key followed by the number one on your telephone keypad. Your first question comes from the line of Joe Moore with Morgan Stanley. Your line is open. Joe Moore: Great. Thank you. You guys have talked about this scaling up of inference around reasoning models for, you know, at least a year now, and we've really seen that come to fruition. As you talked about, we've heard it from your customers. Can you give us a sense for how much of that demand you know, you're able to serve and give us a sense for maybe how big the inference business is for you guys and, you know, do we need full-on NBL72 Rackscale solutions for reasoning inference going forward? Jensen Huang: Well, we would like to serve all of it. And I think we're on track to serve most of it. Grace Blackwell and VLINK72 is the ideal engine today, the ideal computer thinking machine, if you will, for reasoning AI. There's a couple of reasons for that. The first reason is that the token generation amount, the number of tokens reasoning goes through, is a hundred, a thousand times more than a one-shot chatbot. You know, it's essentially thinking to itself, breaking down a problem step by step. It might be planning multiple paths to an answer. It could be using tools, reading PDFs, reading web pages, watching videos, and then producing a result, an answer. The longer it thinks, the better the answer, the smarter the answer is. And so what we would like to do and the reason why Grace Blackwell was designed to give such a giant step up in inference performance is so that you could d”