Explained: Why Google’s new partnership with Nvidia and its GB200 AI chips marks a major shift in the AI hardware race
Google has started using Nvidia’s new GB200 AI chips in its data centers marking a significant moment in the global AI hardware race. By adopting one of the world’s fastest AI chips, Google aims to accelerate model training, strengthen Google Cloud and compete more aggressively with Amazon and Microsoft.
Google’s recent decision to deploy Nvidia’s GB200 superchips represents a major shift in the company’s AI strategy. For years Google relied heavily on its own Tensor Processing Units to power model training and inference tasks. However the explosive demand for AI computing has pushed cloud providers to seek faster and more efficient solutions. Nvidia’s GB200 is currently one of the most advanced AI chips available and its adoption reflects Google’s need for larger and more scalable compute power.
The GB200 is part of Nvidia’s Blackwell platform and is designed to train massive AI models while reducing energy consumption. It offers far greater performance than previous generations enabling faster training cycles and more efficient inference. For cloud customers this directly translates into quicker deployments lower operational costs and the ability to run more complex models.
Experts believe Google’s move is aimed at boosting Google Cloud which has been trailing Amazon Web Services and Microsoft Azure in AI-focused infrastructure. By offering GB200-powered clusters Google can attract enterprises that require high-end compute for generative AI products large language models simulation workloads and real time processing.
Google says it will continue to develop its TPUs but acknowledges that combining them with Nvidia’s leading hardware offers greater flexibility. The partnership signals that cloud providers may rely on a mix of in-house and third-party chips to meet the growing demands of AI.
With Nvidia dominating the global AI chip market the collaboration strengthens Google’s ability to scale and compete while also highlighting the ongoing race among tech giants to secure the fastest AI infrastructure.
About the Author
