BusinessWednesday, April 22, 2026· 2 min read

Thinking Machines Lands Multi-Billion Google Cloud Deal to Supercharge AI

TL;DR

Mira Murati’s Thinking Machines Lab has signed a multi-billion-dollar agreement with Google Cloud to power its AI infrastructure using Nvidia’s latest GB300 chips. The partnership promises to accelerate model development, scale compute access, and boost the pace of AI research and deployment.

Key Takeaways

  • 1Thinking Machines Lab struck a multi-billion-dollar deal with Google Cloud for AI infrastructure.
  • 2The infrastructure will be powered by Nvidia’s newest GB300 accelerator chips, enabling high-performance training and inference.
  • 3The partnership is poised to speed model development, scale workloads, and support more ambitious AI projects.
  • 4The deal signals major cloud and hardware investment into leading AI labs, likely accelerating research-to-product timelines.

Big cloud deal brings cutting-edge GPUs to an ambitious AI lab

TechCrunch has exclusively learned that Mira Murati’s Thinking Machines Lab has signed a multi-billion-dollar agreement with Google Cloud to run its AI workloads on infrastructure powered by Nvidia’s latest GB300 chips. This strategic tie-up brings together a leading AI lab, a top cloud provider, and state-of-the-art accelerators, creating a powerful platform for next-generation models.

The use of Nvidia’s GB300 accelerators on Google Cloud will give Thinking Machines access to denser, faster compute for large-scale training and inference. That level of performance can shorten experiment cycles, enable larger models, and reduce time-to-result for research and commercial deployments—advantages that can compound quickly for teams pushing the frontier of AI.

Beyond raw speed, the deal represents a broader win for the AI ecosystem: major cloud and hardware investment directed at mission-driven labs helps democratize access to high-end infrastructure and supports faster translation of research into products and services. For Thinking Machines, the arrangement should amplify its ability to iterate, scale, and collaborate.

Overall, this partnership is an encouraging signal that industry players are continuing to invest heavily in compute and infrastructure — a necessary ingredient for sustaining rapid progress across AI research and real-world applications.

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