PLATFORM SERVICES

Production-grade orchestration for AI

Accelerate developer velocity and improve reliability by combining production-ready Kubernetes, an HPC-grade Slurm scheduler, and managed instances with multi-tenancy and enterprise observability.

Unified workload orchestration

Orchestration, high-performance, and reliability for AI workloads

Managed Slurm

Make large GPU training runs manageable with an HPC-grade Slurm batch scheduler that runs on Kubernetes.

  • Create reliable R&D timelines with scheduled queues for large-scale training
  • Simplify management of mixed workloads across applications
  • Retain a familiar environment for HPC teams transitioning to AI

Nscale Kubernetes Service (NKS)

Run production-ready Kubernetes and lightweight, virtual Kubernetes clusters for a range of workloads and experiments.

  • Take full control of Kubernetes with fast spin-up, multitenancy isolation, and failure recovery in a production-ready orchestration environment
  • Reduce time-to-market and operational bottlenecks with virtual Kubernetes clusters — provisioned in minutes
  • Scale with ease to enterprise-grade super-clusters

Instances

Remove hardware lifecycle complexity with compute flexibility that fits your needs.

  • Maximize performance for intensive workloads with managed bare-metal nodes
  • Stay fast with Virtual Machines for experimental workloads
  • Keep data and network sovereignty with VPC isolation

Unified platform services for optimized runs

Shorten cycle times

Spin up developer test environments in minutes and move experiments to production with GPU-aware scheduling and autoscaling.

Reduce operational bottlenecks

Remove complexity and reduce outages with intelligent GPU placement, orchestration, and observability.

Provide predictable budgeting

Spin up bare metal or VMs with lifecycle-managed instances to simplify capacity planning and costs.

Power enterprise AI at scale

Telco

Scalable, AI-native infrastructure

Telcos can leverage Nscale’s GPU infrastructure to deliver AI services, optimise 5G networks, support advanced AI workflows, and drive next-generation solutions .

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Finance

Unlock AI advantage in finance

Financial service organisations that leverage GPU and Cloud technology are gaining a competitive edge through enhanced efficiency, improved decision-making, and superior customer service.

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Healthcare & Life Sciences

Enhancing efficiency in healthcare

GPU Cloud technology is revolutionising healthcare, impacting areas like bioinformatics, genomics, drug discovery, personalised medicine, and multiomic analysis.

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AI Native

Accelerated AI model deployment

AI-native companies can leverage Nscale’s scalable GPU cluster infrastructure to enhance model development, support critical operations, and drive innovation in their tech solutions.

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Introducing Nscale
fine-tuning service

Access thousands of GPUs tailored to your needs

Reserve GPUs

FAQ

Yes, the platform is designed for mixed workloads: Slurm and Kubernetes integrate so containerised services, batch training and interactive jobs can coexist with predictable scheduling and GPU-awareness.

Virtual Kubernetes is a fast, isolated environment intended for development and test scenarios with quick spin-ups and safe experimentation. NKS (production Kubernetes) is a hardened orchestration service designed for production workloads with stronger SLAs, integrated observability, networking and cluster policies.

We provide organization and project constructs with role-based access control (RBAC) so teams can be granted the exact permissions they need. Features include project scoping, logical segregation of resources, audit logging, and integration with enterprise SSO (SAML/OIDC). Multi-tenant isolation is enforced at the orchestration and network level so teams see only their resources and data.

Nscale builds platform patterns that align with NVIDIA’s reference architectures: selected GPU node types, validated CUDA/driver stacks and tuned network/storage topologies. This gives predictable, repeatable performance for large training and inference workloads.