HomeGoogle Cloud unveils two AI chips to rival Nvidia
On Wednesday, Google Cloud announced that its eighth generation of custom AI chips, known as tensor processing units (TPUs), would be divided into two types. One chip, the TPU 8t, is designed for model training, while the other, the TPU 8i, targets inference.
Inference refers to the ongoing use of models—essentially what happens after users submit prompts.
Unsurprisingly, Google touts impressive performance improvements for these new TPUs compared to earlier generations: up to 3 times faster AI model training, 80% better performance per dollar, and the ability to connect over one million TPUs in a single cluster. The result should be significantly more computing power with much less energy—and lower costs for customers—than previous versions. These chips are called TPUs rather than GPUs because Google's custom low-power chips were originally named Tensor.
However, Google’s chips are not a full-scale challenge to Nvidia’s future, at least not yet. Like other major cloud providers such as Microsoft and Amazon, Google uses these chips to complement, not completely replace, the Nvidia-based systems it offers in its infrastructure. In fact, Google promises that its cloud will feature Nvidia’s latest chip, Vera Rubin, later this year.
At some point, the hyperscalers building their own AI chips—including Amazon, Microsoft, and Google—may rely less on Nvidia as enterprises migrate their AI workloads to their clouds and adapt their applications to these chips.
Still, betting against Nvidia today is not a profitable strategy. As notable chip market analyst Patrick Moore joked on X, he predicted back in 2016 that Google’s TPU could spell trouble for Nvidia (and Intel) when the search giant launched its first TPU. Nvidia now has a market cap of nearly $5 trillion, meaning that prediction hasn’t exactly stood the test of time.
If Nvidia’s plan holds, Google’s growth as an AI cloud provider could actually generate more business for the chip maker, not less—even if many workloads run on Google’s own chips.
In fact, Google also announced that it has agreed to work with Nvidia on designing computer networking to make Nvidia-based systems even more efficient in its cloud. Specifically, the two tech giants are collaborating to enhance the software-based networking technology called Falcon, which Google created and open-sourced in 2023 under the Open Compute Project, the umbrella organization for open source data center hardware.
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On Wednesday, Google Cloud announced that its eighth generation of custom AI chips, known as tensor processing units (TPUs), would be divided into two types. One chip, the TPU 8t, is designed for model training, while the other, the TPU 8i, targets inference.
Inference refers to the ongoing use of models—essentially what happens after users submit prompts.
Unsurprisingly, Google touts impressive performance improvements for these new TPUs compared to earlier generations: up to 3 times faster AI model training, 80% better performance per dollar, and the ability to connect over one million TPUs in a single cluster. The result should be significantly more computing power with much less energy—and lower costs for customers—than previous versions. These chips are called TPUs rather than GPUs because Google's custom low-power chips were originally named Tensor.
However, Google’s chips are not a full-scale challenge to Nvidia’s future, at least not yet. Like other major cloud providers such as Microsoft and Amazon, Google uses these chips to complement, not completely replace, the Nvidia-based systems it offers in its infrastructure. In fact, Google promises that its cloud will feature Nvidia’s latest chip, Vera Rubin, later this year.
At some point, the hyperscalers building their own AI chips—including Amazon, Microsoft, and Google—may rely less on Nvidia as enterprises migrate their AI workloads to their clouds and adapt their applications to these chips.
Still, betting against Nvidia today is not a profitable strategy. As notable chip market analyst Patrick Moore joked on X, he predicted back in 2016 that Google’s TPU could spell trouble for Nvidia (and Intel) when the search giant launched its first TPU. Nvidia now has a market cap of nearly $5 trillion, meaning that prediction hasn’t exactly stood the test of time.
If Nvidia’s plan holds, Google’s growth as an AI cloud provider could actually generate more business for the chip maker, not less—even if many workloads run on Google’s own chips.
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