Technology analyst Jeff Kagan said he doubts the delay will have any meaningful impact on enterprise IT operations.
“We have learned to always expect these kinds of glitches. Fortunately, they don’t stop progress and growth, although they can slow things down from time to time,” Kagan said. “In the end, this is not a long-term problem (as much as) one of many short-term issues that will be resolved.”
In the analyst call, Huang also explored his view of the future of enterprise computing, and the massive degree to which AI is going to change the nature of hardware and computing operations.
“We drove down the cost of training large language models or training deep learning so incredibly that it is now possible to have gigantic scale models, multitrillion-parameter models, and pretrain them on just about the world’s knowledge corpus, and let the model go figure out how to understand human language representation, and how to codify knowledge into its neural networks, and how to learn reasoning, and so which caused the generative AI revolution,” Huang said.
The CEO also argued that the financial underpinnings of IT environments are also changing rapidly.
“Whenever you double the size of a model, you also have to more than double the size of the data set to go train it. And so, the amount of flops necessary in order to create that model goes up quadratically,” he said. “It’s not unexpected to see that the next-generation models could take 10, 20, 40 times more compute than last generation. We have to continue to drive the generational performance up quite significantly so we can drive down the energy consumed and drive down the cost necessary to do it.”
