Nvidia’s AI Push: CEO Jensen Huang Enthusiastic About Applications
Chip giant Nvidia, which dominates the training of artificial intelligence, sold $31 billion worth of the systems last quarter, it reported Wednesday evening.
Nvidia CEO Emphasizes Importance of AI Inference
Nvidia CEO Jensen Huang, who co-founded the company, is also the chief evangelist of all things AI these days. As such, he spent Wednesday evening telling Wall Street analysts about the important applications being made possible with the technology.
Google’s NotebookLM: A Favorite Application
Huang emphasized one of his favorite applications, Google’s Notebook LM, saying he “used the living daylights” out of the AI tool.
The NotebookLM program, which is free to use with a Google account, lets users upload documents and create a summary of their contents. It suggests questions to ask about the documents, engages in a chat about the documents, and it will even create an audio conversation about the contents between two speakers in the style of a podcast.
Nvidia CEO’s Personal Experience with NotebookLM
“I put every PDF, every archived paper into it just to listen to it as well as scanning through it,” he said.
Nvidia’s Focus on Inference
Huang talked about NotebookLM in response to one Wall Street stock analyst’s question about the trends in “inference,” the practice of making predictions with an AI model after the model has been trained.
“Our hope and dream is that someday, the world does a ton of inference,” replied Huang. “That’s when AI has really succeeded,” he continued, “is when every single company is doing inference inside their companies, for the marketing department and forecasting department and supply chain group and their legal department and engineering, of course, and coding.”
Physical AI and Future Predictions
Huang cited other examples of AI inference, such as “physical AI,” meaning AI models that “understand the meaning of the structure” of the physical world and how to “reason” about it. Such models can “not only understand but can predict, roll out a short future.”
Nvidia’s Future Plans
Huang is betting that inference tasks will be the new growth engine for his company’s data center GPU chip sales, after having dominated the market for chips for training AI models for years.
Huang told Wall Street his company’s latest GPU, “Blackwell,” unveiled in March, is “in great shape,” and will generate billions of dollars of revenue in the fiscal fourth quarter ending in January.
“Blackwell production is in full steam,” said Huang. “We will deliver this quarter more Blackwells than we had previously estimated. And so, the supply chain team is doing an incredible job of working with our supply partners to increase Blackwell, and we’re going to continue to work hard to increase Blackwell through next year. It is the case that demand exceeds our supply.”
Conclusion
Nvidia’s focus on AI inference and its applications is expected to drive growth for the company’s data center GPU chip sales. With the success of its latest GPU, Blackwell, and its enthusiasm for Google’s NotebookLM, Nvidia is poised for a strong future in the AI market.
FAQs
Q: What is NotebookLM?
A: NotebookLM is a free AI tool provided by Google that allows users to upload documents and create a summary of their contents, engage in a chat about the documents, and even create an audio conversation about the contents between two speakers in the style of a podcast.
Q: What is inference in AI?
A: Inference in AI refers to the practice of making predictions with an AI model after the model has been trained. It involves using the trained model to generate new outputs or make decisions based on the input data.
Q: What is Nvidia’s focus on AI inference?
A: Nvidia’s focus is on using AI inference to drive growth for its data center GPU chip sales. The company believes that inference tasks will be the new growth engine for its business, after having dominated the market for chips for training AI models for years.
Q: What is physical AI?
A: Physical AI refers to AI models that “understand the meaning of the structure” of the physical world and how to “reason” about it. Such models can “not only understand but can predict, roll out a short future.”
