Nvidia’s Jensen Huang breaks down ‘CEO math’

Nvidia’s Jensen Huang breaks down ‘CEO math’

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Jensen Huang broke down “CEO math” at a recent talk.

  • Nvidia’s Jensen Huang explained ‘CEO Math’ ahead of Computex in Taiwan.
  • Huang said by investing in GPUs and CPUs, companies can drastically reduce time spent on AI tasks.
  • “The more you buy, the more you save,” he said. 

There’s math, and then there’s what Nvidia’s Jensen Huang calls “CEO Math.”

“The more you buy, the more you save,” Huang said ahead of Computex, an annual technology exposition held in Taiwan. “That’s called CEO math. It’s not accurate, but it is correct.”


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Huang explained the concept by describing why companies should invest in both graphics processing units (GPUs) and central processing units (CPUs). The two processors can work autonomously, reducing the time it takes to carry out a task from “100 units of time down to 1,” he said.

So, the more you buy, the more you save. Sounds like a good sales pitch for a CEO who sells processors.

Combining the two processors is already common practice in the personal computing industry. “We add a GPU, a $500 GPU, to a $1,000 PC, and the performance increases tremendously,” he said. “We do this in a data center. A billion-dollar data center, we add $500 million worth of GPUs, and all of a sudden, it becomes an AI factory.”

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Huang then presented a diagram showing that when companies use both, their speed will increase by 100, at just 1.5 times the cost. 

In March, Nvidia unveiled the Blackwell B200 GPU, a $70,000 chip it claims is the “world’s most powerful AI chip.” It is packaging the chip into larger designs like the GB200 NVL72, which combines 72 GPUs and 36 CPUs and is intended for the “most compute-intensive workloads” and reduces cost and energy consumption by up to 25 times. 

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Over the past few months, the chipmaker has shot into headlines as a critical player in the AI boom. It raked in over $22 billion in the fourth quarter of 2023. Tech execs from Sam Altman to Mark Zuckerberg have become reliant on its chips to power their AI ambitions.

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