Nvidia, CoreWeave, and Nebius: Inside the Circular Financing of the GPU Boom

Nvidia, CoreWeave, and Nebius: Inside the Circular Financing of the GPU Boom

June 12, 2026

Beth Kindig

Lead Tech Analyst

• Neoclouds are seeing massive hyperscaler demand as companies race to scale AI infrastructure, resulting in rapid revenue and backlog growth.
• Leaders like CoreWeave and Nebius enable this through access to the latest Nvidia GPU’s while also optimizing compute utilization.
• However, the bearish argument behind hyperscaler demand lies in their desire to offload their capex spending and shift costs to the operating expense line.
• CoreWeave’s and Nebius’ growth is far from profitable, as they seek to capture AI demand with limited cash flow and soaring debt loads in an increasingly tough macro backdrop.
• Circular financing, demonstrated by Nvidia’s investments and financial backstopping, is another key item to monitor closely

Neoclouds are one of the more hotly debated AI business models, with CoreWeave and Nebius being the two most widely recognized names. These companies have seen their sales, backlog, and share prices soar, differentiating themselves through quick access to the latest GPU compute and GPU utilization advantages that allow hyperscalers to rapidly add efficient compute capacity.

Notably, CoreWeave and Nebius have each secured 3.5 GWs of contracted power capacity; while these power footprints are key considering power is a hindrance to data center expansion, the vast majority of their contracted power capacity has yet to come online. CoreWeave is targeting 1.7 GW of active power by the end of 2026, while Nebius is targeting 800 MW to 1 GW of connected power.

In turn, they are quickly working to convert their contracted power to active power, and thus convert large backlogs into revenue. Yet doing so is extremely expensive, and neoclouds do not have the same cash nor operating cash flow profiles of Big Tech. This is leading neoclouds to employ unique and circular financing structures, raising some red flags.

In this analysis, I dive into the two public neoclouds that are riding Nvidia equity, hyperscaler contracts, and GPU-backed debt to fund the buildout, and what it means for the durability of the surge.

The size of hyperscaler-neocloud partnerships compared to their current revenue is astounding. Microsoft has struck the most neocloud deals, with approximately $60 billion worth of commitments between CoreWeave, Nebius, and other private players such as Nscale. Meanwhile, Meta has committed $35.2 billion to CoreWeave in total after its recent $21 billion expansion, and an up to $27 billion deal with Nebius for a total commitment of up to $62.2 billion. Along with Meta, OpenAI is one of CoreWeave’s two largest customers, while CoreWeave also has a multi-year compute agreement with Anthropic.

Alone, Microsoft and Meta’s total commitments extend up to $122.2 billion – for perspective, that is ~90% of the TTM revenue of AWS being allocated towards neoclouds over long-term capacity deals. When factoring in hyperscaler-backed deals from OpenAI and Anthropic (although exact deal value is unknown), total potential commitments surpass $145 billion.

Keep in mind, CoreWeave’s FY2026 estimated revenue is $12.6B and Nebius FY26 revenue is expected to be $3.4B - therefore, these partnerships are leading to commitments that are an order of magnitude higher than current sales.

The reason hyperscalers are willing to allocate this capital to a relatively new business model in the neoclouds is three-fold – quick access to leading GPU generations, optimized compute utilization, and the added benefit of not having to recognize capex on the balance sheet – we look at each of these drivers below.

At its root, neocloud demand is a product of hyperscalers' insatiable demand for compute capacity. However, neoclouds can often add compute capacity much faster than hyperscalers can through internal builds, offering a key value proposition for Big Tech. As hyperscalers spend hundreds of billions a year on AI compute, minimizing the lag between data center expenses and revenue generation is critical to maximizing their return on investment.

Supporting the argument around neocloud’s advantage lying within time to deployment, commercial real estate giant JLL notes, “Neoclouds can deploy high-density GPU infrastructure within months compared to multi-year builds for hyperscale data centers, providing crucial time-to-market advantages for businesses needing rapid AI development.”

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In CoreWeave’s S-1 Registration filing, it lists “Faster access to the latest AI infrastructure advancements” as one of its key benefits to customers. Specifically, CoreWeave says “we were among the first to deliver NVIDIA H100, H200, and GH200 clusters into production at AI scale, and the first cloud provider to make NVIDIA GB200 NVL72-based instances generally available. We are able to deploy the newest chips in our infrastructure and provide the compute capacity to customers in as little as two weeks from receipt.”

Nebius makes a similar statement in its Annual Report, noting its “consistent track record of being one of the first to deploy the latest generation of NVIDIA GPU chips.”

CoreWeave and Nebius' relationship with Nvidia is key to acquiring the latest GPUs ahead of others. Nvidia recently invested $2 billion in both CoreWeave and Nebius. Under these partnerships, CoreWeave and Nebius will each look to deploy more than 5 GW of data center capacity by 2030.

CoreWeave recently demonstrated its ability to offer quick access to the latest chips and newest architectures to hit the market once again, being the first to have a Vera Rubin system up and running at the start of June. This provides evidence that partnering with CoreWeave and Nebius can help hyperscalers access as much of the latest GPU compute as possible in short order.

Aside from raw compute access, CoreWeave and other neoclouds layer on software and additional capabilities that improve GPU utilization – a key value add for hyperscalers.

For example, CoreWeave Kubernetes Service (CKS) helps coordinate the allocation of workloads across thousands of GPUs, while its SUNK service helps optimize GPU utilization by allowing training and inference workloads to run on the same cluster. CoreWeave Tensorizer enables high-speed model loading, reducing GPU idle time.

Combining these software and optimization capabilities with rapid fault detection and remediation services, CoreWeave believes it can offer higher GPU utilization rates than hyperscalers, based on the model FLOPs utilization (MFU) metric. The “MFU gap” is a metric that describes the gap between compute capacity and usage, which today often ranges between 30% to 40%.

The MFU gap can become quite costly as it represents a more realistic way to measure the performance of GPUs -- rather than only taking into account if a GPU is sitting idle or not. According to Trainy AI: “GPU Utilization is only measuring whether a kernel is executing at a given time. It has no indication of whether your kernel is using all cores available, or parallelizing the workload to the GPU’s maximum capability.”

*Chart comparing theoretical model FLOPS utilization (100%) with observed performance (35%–45%), illustrating a significant efficiency gap in AI workloads. Source: CoreWeave *

When going public, CoreWeave published its MFU rate at 35% to 45%, stating it is 20% higher than competitors, which means other AI data centers had MFU rates more in the 30% range. However, in a March 2025 blog post, CoreWeave noted that it was achieving an MFU of >50% on Hopper GPUs. This ability to stand up next-generation GPU hardware in short fashion combined with improved utilization rates is where the neoclouds’ advantage lies.

By leasing compute capacity from neoclouds, hyperscalers shift their cost timeline from being a large upfront capex outflow to an operational expense outflow spread over long-term contracts. The need to spread costs is becoming