The engine of superintelligence

Move from experimentation to production without managing infrastructure, using serverless or dedicated inference, fine-tuning, prompt workbench, and OpenAI-compatible APIs.

Run AI workloads with less operational complexity, using Nscale Kubernetes Service (NKS) and Managed Slurm for autoscaling and predictable training queues, while Environments isolate workloads and help teams get more from reserved GPU clusters.

Reduce operational burden for intensive AI workloads with dedicated GPU nodes managed through Nscale Cloud, while reservations and placements map workloads to physical topology and NVLink domains.

Keep GPU capacity productive with a fleet-wide observability platform, automated fault detection and remediation, and resource governance that maintains healthy and schedulable capacity.

Get the right compute configuration into production quickly, with GPU and CPU infrastructure tailored to your platform, architecture and operating model.

Scale workloads without bottlenecks across low-latency InfiniBand, RoCE and NVLink interconnects that keep GPUs communicating efficiently. Keep training and inference fed with AI-optimised parallel storage for predictable throughput at scale.

Expand capacity predictably with prefabricated modules designed for rapid, repeatable deployment.

Closed-loop liquid cooling removes heat efficiently to enable reliable operation for next-generation AI infrastructure.

Reduce facility energy overhead and operating costs through efficient power and cooling design that targets a Power Usage Effectiveness (PUE) of 1.1–1.15, leaving more power capacity for productive AI compute.

Bring AI capacity online faster with on-site behind-the-meter generation that bypasses multi-year grid interconnection queues and reduces dependence on utility timelines.

Keep AI workloads running during grid disruption with microgrid infrastructure designed to operate independently of the utility supply.