The battle for AI computing power capital has escalated again! Lambda, part of the NVIDIA ecosystem, plans to raise $3 billion to bolster its computing power framework and prepare for its IPO.

NVIDIA Corporation has invested in and supported a new cloud force, Lambda, a new AI cloud computing service provider, which is considering raising up to $3 billion in funding prior to a potential IPO, with a target valuation of up to $12 billion or higher. Lambda has received several investment term sheets. If the transaction is completed, this will constitute a crucial round of capital injection for Lambda before its possible listing next year, while also serving as a concentrated test of the strong demand from the private equity market for new cloud computing platforms, their growth visibility, and valuation ranges. Lambda expects revenues to exceed $1.5 billion this year, and the funding size could reach nearly double its annual revenue, highlighting the AI computing business's heavy dependence on capital related to GPU procurement, power capacity, and data center construction. NVIDIA Corporation's shareholder status helps reinforce Lambda's computing power supply and ecosystem endorsement, but the competition for computing infrastructure and external leasing expansion among Nebius, Nscale, CoreWeave, and IREN also means that investors will ultimately focus on GPU utilization rates, customer concentration, capital efficiency, and free cash flow generation capability. Lambda is considering raising up to $3 billion in funding before its IPO. According to media reports citing informed sources, AI cloud computing service provider Lambda, supported by "AI chip superpower" NVIDIA Corporation (NVDA.US), is in talks for a funding round of up to $3 billion, preparing for a potential IPO next year. The report added that the new cloud computing service provider is discussing a fundraising effort that values it at up to $12 billion or higher. Negotiations are ongoing, and the transaction terms have not yet been finalized. The report noted that Lambda has received several term sheets regarding this funding round, and some individuals close to the company indicated that this round of funding could pave the way for its IPO as early as next year. According to the company, the California-based enterprise expects to achieve revenues exceeding $1.5 billion this year. Lambda did not immediately respond to any media requests for comment. In November of last year, Lambda raised over $1.5 billion in a funding round led by TWG Global. Other supporters include Andra Capital, Scott Hassan's family office SGW (an early investor in Alphabet Inc. Class C), OpenAI co-founder Andrej Karpathy, Wall Street top fund manager Cathie Wood's Ark Invest, and venture capital firms under NVIDIA Corporation. The company is in fierce competition with other new cloud computing service providers like Nebius (NBIS.US), Nscale, CoreWeave (CRWV.US), and IREN (IREN.US). According to media reports, London-based Nscale is seeking to raise up to $3 billion through an IPO in the United States. GPU leasing takes center stage on the capital market! "New cloud" AI computing factories enter the IPO era. Founded in 2012, Lambda is a pure AI infrastructure company rather than a large model developer or traditional data center real estate firm. Its main business involves building and operating "AI factories" centered on NVIDIA Corporation GPUs, renting out computing power for training, fine-tuning, and inference through the cloud. Its products cover on-demand rental instances of 1 to 8 NVIDIA Corporation various AI GPUs, one-click computing clusters with 16 to over 2,000 AI GPU components, and single-tenant super AI clusters with 4,000 to over 165,000 GPUs under contracts of more than three years, integrating high-density power supply, liquid cooling, high-speed optical interconnects, and network infrastructure cluster operations. Lambda plans to stop traditional local workstation and server leasing business by 2025, fully transitioning to AI cloud computing and ultra-large-scale dedicated AI computing infrastructure delivery. Its multi-year agreement with Microsoft Corporation involves deploying tens of thousands of NVIDIA Corporation GPUs. Lambda and CoreWeave are both "NVIDIA Corporation preferred" new cloud computing service providers, but there are notable differences in business maturity and platform depth: Lambda focuses more on GPU computing power, dedicated superclusters, and joint engineering services, with a relatively simple product structure; CoreWeave has developed into a full-stack AI cloud computing platform covering bare metal Kubernetes, Slurm scheduling, object and distributed storage, high-speed networking, dedicated and serverless inference, and AI agent sandboxes. As of the end of March 2026, CoreWeave has 49 data centers, over 1 GW of operating power, and more than 3.5 GW of contracted power, with Q1 revenues of $2.078 billion and a revenue backlog near $100 billion. In contrast, Lambda expects annual revenues to exceed $1.5 billion, a significantly smaller scale, but dedicated clusters and contracts with investment-grade clients like Microsoft Corporation bring it closer to being a "focused entity cluster-type AI computing factory operator." Lambda plans to raise up to $3 billion and achieve a valuation of $12 billion or even higher, highlighting the formation of a complete capital cycle for AI computing infrastructure resources: "long-term customer contractssecured loans and project financingequity financingIPO." The company has previously secured $1 billion in a syndicate secured credit facility and completed $926 million in term loan financing rated Baa2 by Moody's Corporation. At the industrial chain level, this indicates strong order visibility for NVIDIA Corporation GPUs, HBM, NVLink/InfiniBand interconnects, optical communications, liquid cooling, and data center power. However, an estimated valuation of around $12 billion amounts to roughly 8 times its anticipated annual revenue, meaning that the capital markets will no longer only reward GPU quantities but will also scrutinize computing power utilization rates, the quality of long-term contracts, customer concentration, GPU depreciation and renewal speed, financing costs, and free cash flow. This funding proves that AI infrastructure can still attract substantial capital, but it does not alone demonstrate that the monetization of end AI or Neocloud's profit model has matured and grown.