Enterprise GPU capacity

Need more GPU capacity?
Use the fleet you already have.

Thunder Compute transparently adds usable GPU capacity inside the fleet you already control: Slurm, Kubernetes, cloud, on-prem, VM, or mixed GPU clusters. We've seen up to a 3x capacity gain from implementing Thunder Compute.

Uneven utilization

Capacity is stranded inside your cluster.

Most GPU clusters are capacity constrained, but parts of the fleet are still idle or fragmented across teams, queues, jobs, and environments.

Thunder Compute uses proprietary GPU virtualization software to transparently slot workloads into these gaps, improving utilization beyond what scheduler-only GPU orchestration can see.
Supported options
01
Kubernetes
02
Slurm
03
VMs
04
Bare metal GPU hosts
05
Cloud environments
Drop-in

Add capacity across Slurm, Kubernetes, VMs, and bare metal

Thunder Compute plugs into your existing environment at the CUDA level. It complements the GPU schedulers, operators, and management tools you already use, and is compatible with any hypervisor and all NVIDIA GPUs.

Fully CUDA-compatible

Invisible to ML developers and researchers

We virtualize at the CUDA layer, so GPU functionality is largely identical and the developer experience is unchanged. Developers keep using the same ML frameworks, containers, machines, schedulers, and workflows while more capacity is added to the fleet.

Example environments

More usable GPU capacity for real enterprise deployments

01
Large AWS deployment

You have a large committed GPU footprint in AWS. Some workloads run constantly, but others are bursty. Thunder Compute transparently creates more capacity for additional workloads inside your existing deployment.

02
University GPU cluster

You run an on-prem Slurm GPU cluster shared across labs, departments, and research groups. Demand is uneven, users queue for access, and capacity is fragmented across projects. Thunder Compute lets more students and researchers share the same cluster, and offers a more flexible alternative to Slurm interactive nodes.

03
AI company with mixed workloads

Your team runs training, inference, evaluation, and developer experiments across a large fleet. Some jobs need dedicated capacity; others need intermittent access. Thunder Compute lets you run more workloads on the GPUs you already have.

04
Hedge fund or research organization

You run GPU-heavy research, simulation, model training, and production inference, and researcher demand is outpacing your ability to deploy hardware. Run more workloads within your existing fleet, and track each desk's GPU usage instead of VM runtime to see who actually consumes compute.

05
GPU cloud or infrastructure provider

You operate GPU capacity for end customers and are capacity constrained, with demand from more customers than your fleet can handle. Customers keep seeing the same environment you expose to them, while Thunder Compute lets you serve more of them from the same data centers and partnerships.

The core idea

You have a capacity problem. We have the solution.

Thunder Compute unlocks more usable GPU capacity from the fleet you already operate, across teams, workloads, schedulers, and environments. If you run 1,000+ GPUs and demand is outpacing supply, we should talk.