Today, we are announcing our $13 million Series A round to accelerate our mission to unlock the world’s idle compute. The round was led by Matrix Partners, who also led our Seed round earlier this year, with participation from Y Combinator and CEAS Investments. We are deploying this capital to permanently solve the GPU capacity shortage by eliminating the $200 billion of wasted compute sitting idle today, building towards a future where every GPU is virtualized.
Today, enterprises are severely underutilizing their GPUs, with an average of five percent utilization according to Cast AI's 2026 State of Kubernetes Optimization Report. This is because GPUs run one workload at a time and stay allocated whether or not any work is running. CPUs, storage, and memory operate differently; they are all allocated through virtual abstractions that let many workloads share the same physical hardware. Thunder Compute’s mission is to free up GPU capacity by building and scaling these virtual abstractions for GPUs.
“There is a massive efficiency problem with over $200 billion in data center capacity sitting idle because GPUs are the only hardware that aren’t virtualized,” said Carl Peterson, co-founder of Thunder Compute. “Four years ago, we saw this gap and set out to build out the VMware for GPUs. Since then, we have invented cutting-edge virtualization technology for GPUs and have hardened it through our self-serve cloud offering, where over 10,000 users have run their workloads on our virtualized GPUs. This funding will help us virtualize GPUs at scale by partnering with enterprises seeking to create more capacity within their existing GPU fleets.”
Thunder Compute's proprietary software treats GPUs as network resources which are accessible to any workload in the data center. This flexibility enables efficiency, as GPUs can be allocated exactly when and where they’re needed rather than sitting idle. Thunder Compute works beneath the workload layer, changing how machine learning code interacts with the physical GPU. This has a distinct advantage; the virtualization is completely invisible to the developer, so it can drop into existing workflows.
"Lots of startups focus on optimizing specific workloads but Thunder Compute stood out by figuring out how to optimize data centers in a generalized and transparent way,” said Ilya Sukhar, General Partner at Matrix Partners. “Carl, Brian and the rest of the team had a lot of foresight to approach the problem this way and we believe the timing is now perfect to deploy their solution toward addressing today’s GPU crunch.”
You can read more about Thunder Compute’s Series A in SiliconANGLE’s coverage.