Lambda raises $320 million in funding for GPU cloud infrastructure.

Lambda, a GPU cloud service provider, has secured $320 million in Series C funding to expand its AI infrastructure offerings. The company joins competitors like Vultr, CoreWeave, and Voltage Park in providing GPU-based processing for AI training and inference, moving beyond traditional CPU processing. The Series C round is led by B Capital, SK Telecom, and T. Rowe Price Associates, Inc., alongside existing investors including Crescent Cove, Mercato Partners, 1517 Fund, Bloomberg Beta, and Gradient Ventures.

With the funding, Lambda will deploy "tens of thousands" of Nvidia GPUs, including current-generation H100 Hopper accelerators, Nvidia's forthcoming G200 GPU accelerators—which are set to double the performance of the H100—and Nvidia's hybrid GH200 CPU/GPU superchips. Lambda's stated mission is to build "the #1 AI compute platform in the world," requiring "lots of Nvidia GPUs, ultra-fast networking, lots of data center space, and lots of great new software to delight you and your AI engineering team." The company plans to "accelerate the growth of our GPU cloud, ensuring AI engineering teams have access to thousands of Nvidia GPUs with high-speed Nvidia Quantum-2 InfiniBand networking."

This strategy aligns with Nvidia CEO Jensen Huang's vision for dedicated AI data centers, called AI factories, populated entirely with GPUs rather than the x86 CPUs found in traditional data centers. Founded in 2012, Lambda began working with GPU systems in 2017 when it first started experimenting with transformer models. The company offers co-location services tailored for dense deployments and resells access to Nvidia's DGX SuperPODs, a business model that capitalizes on the rising trend of AI as a service, where customers rent compute time rather than invest in their own equipment.

However, Lambda faces a significant challenge: acquiring the necessary hardware. While TSMC continues manufacturing chips at maximum capacity, demand remains enormous, with a backlog of several weeks and months. This constraint could limit Lambda's ability to fulfill its ambitious expansion plans with the newly secured capital.