As AI workloads scale, infrastructure decisions increasingly determine how quickly teams can move from experimentation to production. For organizations running AI, ML, or HPC workloads globally, GPU availability, performance consistency, and operational simplicity are often the limiting factors.

In this use case, see how Vultr provides the global GPU and bare metal infrastructure that underpins Exostellar’s intelligent GPU orchestration platform. By running on Vultr, Exostellar can quickly provision GPU resources across regions, deliver predictable performance for training and inference, and support customers operating in distributed environments.

Vultr’s broad GPU portfolio gives Exostellar the flexibility to match workloads to the appropriate hardware without vendor lock-in. Combined with transparent and predictable pricing, this enables customers to scale GPU-intensive workloads while maintaining cost control and avoiding overprovisioning.

The use case also demonstrates how Vultr’s global footprint supports intelligent workload placement and migration. Exostellar leverages Vultr regions to help customers improve GPU utilization and reduce operational complexity, without requiring them to manage underlying infrastructure or adapt to fragmented deployment models.

For organizations evaluating how to run AI workloads reliably at scale, this use case demonstrates how Vultr infrastructure can support advanced GPU orchestration while maintaining performance, flexibility, and control at the forefront.