Thunder Compute today announced it has raised $13 million in early funding to help graphics processing unit cloud providers squeeze out the last erg of compute capacity that sits idle and wasted at the long end of workload cycles.

Because GPUs are traditionally allocated as bare-metal resources, dedicated to individual workloads, these expensive chips can spend much of their reserved time sitting idle, waiting for work.

In an exclusive interview with SiliconANGLE, co-founder and Chief Executive Carl Peterson said the objective is to build the “VMware for GPUs.” The idea is to virtualize them so the underlying hardware disappears, much like storage or central processing units. Instead of treating individual GPUs like horses hired out by the minute or hour, Thunder puts them into a pool that can do work whenever capacity becomes available.

“The historical precedent here is that every other type of computer hardware is virtualized,” Peterson said. “For GPUs, this isn’t the case.”

According to the CastAI 2026 State of Kubernetes Optimization Report, enterprise GPUs sit idle, averaging around 5% to 20% utilization. This waste leaves a tremendous amount of capacity at the long end, which Thunder Compute says amounts to almost $200 billion left on the table waiting to be used.

Peterson said much of that underutilization comes from how GPUs are reserved: They are allocated continuously to workloads regardless of whether they are actually being used. Thunder’s software separates a workload’s access to a GPU from the specific hardware serving it, allowing the underlying fleet to be scheduled much more efficiently.

Thunder Compute created proprietary software to treat GPUs as network resources, making them accessible to workloads across the data center. This makes them more flexible and efficient, allowing capacity to be allocated much like storage and central processing unit resources, exactly when it is needed. The company sits between the developer and the cloud provider, abstracting away the GPU as a physical chip so it can drop directly into the existing workflow.

Turning GPUs into another invisible layer

“What’s critical here is that the virtualization is invisible to the developer, and really our goal is for them to not care,” Peterson said.

Importantly, Thunder isn’t really selling a developer productivity story. Peterson said the economic benefit goes primarily to the cloud provider or enterprise firm that bought the GPUs in the first place. Developers should barely notice that Thunder is there; they simply ask for a GPU and get one.

The payoff happens behind the scenes: Higher utilization lets operators squeeze more work out of the hardware they already paid for and potentially pass some of those savings back through lower cloud prices.

To date, Thunder Compute has supplied compute to more than 10,000 users on its own cloud of virtualized GPUs. Peterson couldn’t name any customers, but said two enterprises are piloting the software and the company is scaling it out to many more.

Until now, he explained, Thunder has effectively been “selling to ourselves,” running its own cloud service as a testbed for GPU use cases and becoming its own GPU cloud provider before scaling the software outward.

“Until this point, we have effectively been selling this to ourselves and acting as the cloud provider,” Peterson said. “The fastest way to do that was to launch our own cloud.”

Peterson said Thunder has seen customers achieve gains of four times or more, though he cautioned that the company cannot promise that level of improvement for everyone. The gains depend heavily on the workload and existing utilization.

Now that Thunder has the funding under its belt and has been developing the software for four years, Peterson said the company is confident it is ready to scale across huge fleets of GPUs. The Series A marks a shift from proving the technology on its own cloud toward putting it into the hands of cloud providers and enterprises that already operate GPU infrastructure at scale.

“[The cloud] was a logical place to go, but the most benefit is to work with others who already have huge fleets of GPUs,” Peterson said.

The funding will also help Thunder build out the organization needed to support that shift. Peterson said the company plans to hire systems researchers to continue pushing its virtualization technology, engineers to support enterprise GPU deployments and a sales team as it expands its go-to-market effort. Longer-term, Thunder sees its current virtualization layer as the foundation for a broader suite of GPU infrastructure technology, much as VMware expanded from individual virtualization products into a larger platform.

Image: SiliconANGLE/Microsoft Designer