Declare the job, not the machine
Hand Nimbus a resource envelope and a deadline. It reads the shape of the workload and places it on the nearest healthy node with real headroom, then rebalances as demand shifts.
AI cloud infrastructure
Point at the work. Nimbus routes the compute.
Rented boxes make you the scheduler. Nimbus is a self-scheduling mesh of GPU nodes: declare a training run or an inference endpoint, and it places you on live capacity in under a second.
No cluster to size. 100 GPU-hours on the house.
740ms
Median cold start
1.2M
GPU-hours / day scheduled
38
Regions in the mesh
scheduler: 1,204 nodes online
How Nimbus is different
You describe intent. The mesh does the logistics.
Three decisions we made so you never open a capacity ticket, resize a node pool, or babysit a spot fleet again.
Sub-second cold starts
Snapshotted runtimes and a pool of pre-warmed nodes mean your endpoint answers the first request, not the fiftieth. Median cold start is 740 milliseconds, tail p99 under two seconds.
Spot-priced, never spot-fragile
When a cloud reclaims hardware, Nimbus migrates your running workload to a fresh node before it drains. You pay interruptible rates and keep uninterrupted jobs, up to 71 percent off list.
Pricing that starts at zero
Ship a model before your coffee is cold.
Create an account, pipe in a container or a model ID, and Nimbus schedules the rest. Your first 100 GPU-hours are free, no card, no sales call.