Nscale Dedicated Inference
Deploy supported models on reserved Nscale GPU capacity and access managed endpoints for production inference.Your team keeps the application and the dedicated compute, while Nscale manages the serving layer underneath.
Get managed endpoints for production inference
When you run production inference on dedicated GPUs, you face a trade-off: operate the serving stack yourself, including Kubernetes, serving engines, routing, model storage, and GPU operations, or add a separate inference platform with its own identity, governance, and operating model.
Nscale Dedicated Inference gives you a third option: managed serving on capacity you have already reserved.
You stay in control of your application and compute, while Nscale handles deployment, monitoring, and upgrades of the serving layer.
Why choose Nscale Dedicated Inference
Move from a supported model to a production endpoint in less time, with compute, storage, networking, and serving engineered to work together.
Nscale’s visibility across infrastructure and model serving enables optimization and troubleshooting of the complete inference path.
Nscale manages deployment, monitoring, upgrades, and rollback paths, so your team spends less time on operations and more on building AI products.
How it works
Choose your model
Start with a supported model from our model catalog and the Nscale GPU capacity reserved for your workload.
Deploy with an Nscale profile
Deploy into your managed cloud environment with an Nscale hardware-aware deployment profile, pre-defined for the model and accelerator. Choose the one that fits your reserved capacity.
Connect your application
Call your managed endpoint from your application, while Nscale runs the serving stack and endpoint lifecycle underneath as part of Nscale Cloud.
Built for AI teams
Choose Nscale Dedicated Inference when your team needs dedicated inference capacity and predictable production operations, without turning model serving into another platform to build and maintain.
For AI Natives
Move from training to production on the same infrastructure, without taking on the operational burden of model serving.
For enterprises
Run mission-critical AI on single-tenant GPU nodes that support data sovereignty requirements, with defined SLA targets, without investing in bespoke infrastructure.
For developers
Deploy through tools and APIs your team already uses, with fewer context switches between building and operating.