Google Cloud signs multi-billion-dollar AI infrastructure deal with Thinking Machines Lab

Exclusive: Google deepens Thinking Machines Lab ties with new multi-billion-dollar deal | TechCrunch

Former OpenAI executive Mira Murati's startup, Thinking Machines Lab, has signed a new multi-billion-dollar agreement to expand its use of Google Cloud's AI infrastructure, including systems powered by Nvidia's latest GPUs, TechCrunch has exclusively learned. The deal is valued in the single-digit billions, according to a source familiar with the matter, and includes access to Google's latest AI systems built atop Nvidia's new GB300 chips, alongside infrastructure services to support model training and deployment. Google has been actively striking a number of cloud deals with AI developers as it aims to wrap together its cloud offerings with other services like storage, a Kubernetes engine, and Spanner, its database product. Earlier this month, Anthropic signed an agreement with Google and Broadcom for multiple gigawatts of tensor processing unit (TPUs) capacity (these are Google's custom-designed AI chips for machine learning workloads). But the competition is fierce. Just this week, Anthropic also signed a new agreement with Amazon to secure up to 5 gigawatts of capacity for training and deploying Claude. Earlier this year, Thinking Machines partnered with Nvidia in a deal that included an investment from the chipmaker. But this is the first time the lab has struck a deal with a cloud services provider. The deal is not exclusive, so Thinking Machines may use multiple cloud providers over time, but it's still a sign that Google is looking to lock in fast-growing frontier labs early. Murati left her job as OpenAI's chief technologist and founded Thinking Machines in February 2025. The company, which soon afterwards raised a $2 billion seed round at a $12 billion valuation, has remained highly secretive, but launched its first product in October. Dubbed Tinker, it's a tool that automates the creation of custom frontier AI models. Wednesday's deal provided some insight into what Thinking Machines is developing. In a press release, Google noted that it can support the startup's reinforcement learning workloads, which Tinker's architecture relies on. Reinforcement learning is a training approach that has underpinned recent breakthroughs at labs, including DeepMind and OpenAI, and the scale of the Google Cloud deal reflects how computationally expensive that work can get. Thinking Machines is among the first Google Cloud customers to access its GB300-powered systems, which offer a 2X improvement in training and serving speed compared to prior-generation GPUs, per Google. "Google Cloud got us running at record speed with the reliability we demand," Myle Ott, a founding researcher at Thinking Machines, said in a statement.

Google Cloud inks AI infrastructure deal with Thinking Machines - SiliconANGLE

Google Cloud inks AI infrastructure deal with Thinking Machines Thinking Machines Lab Inc. will expand its use of Google LLC's cloud platform as part of a partnership the companies announced this morning. TechCrunch cited a source as saying that the deal is valued in the "single-digit billions". Thinking Machines is an artificial intelligence startup led by Chief Executive Mira Murati, the former Chief Technology Officer of OpenAI Group PBC. It offers a cloud service called Tinker that enables developers to create custom versions of open-source large language models. Tinker performs customization by attaching add-ons to LLMs' core code. Thinking Machines will move some of its workloads to Google Cloud's A4X Max instances, which are specifically optimized for AI models. Each virtual machine provides access to 4 of Nvidia Corp.'s Blackwell Ultra graphics processing units. They're supported by 2 central processing units that feature 72 cores apiece. The chips run in liquid-cooled GB300 NVL72 appliances. The systems, which are made by Nvidia, come with 37 terabytes of memory and 130 terabits per second of internal bandwidth for moving data between processors. According to Google Cloud, Thinking Machines is one of the first customers to use its NVL72 infrastructure. The chips in A4X Max instances use a technology called RoCE to exchange data. It skips several of the steps usually involved in processing packets, which boosts throughput. The data travels over a network that features a so-called rail-aligned topology. Packets often have to travel through multiple network devices before reaching their destination. Each such device is known as a hop. A rail-aligned topology creates dedicated network links, or rails, between GPUs that minimize the number of hops data must go through, which boosts workload performance. Google uses Nvidia's ConnectX network interface cards, or NICs, to coordinate GPU traffic. A NIC is a chip that acts as an interface between a server and the data center network to which it's attached. The ConnectX chips run alongside Google's internally-developed Titanium NICs. A4X Max instances use the latter modules to process traffic between GPUs and external systems such as other Google Cloud services. Thinking Machines is already using several of those services to run its workloads. The company keeps information in Google Cloud Storage, the Spanner relational database and a custom cache. It relies on a fourth Google Cloud service called Cluster Director to automatically fix certain technical issues.