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GPU Infrastructure, Deployed and Managed.

Aurora deploys, manages, and operates GPU clusters for neoclouds, enterprises, and AI service providers.
From 16 nodes to 1000+. Your site or ours. Bare metal to fully managed — including storage, orchestration, and inference.

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Public GPU Infrastructure Is Not Built for Enterprise AI.

Acquiring GPUs is the easy part. The hard part is everything after: networking, InfiniBand, Kubernetes with GPU scheduling, shared storage that keeps your GPUs fed, multi-tenant isolation, health monitoring, and keeping utilization above 80%.

Aurora solves this by deploying and operating the complete GPU infrastructure stack — from power and cooling to Kubernetes and inference endpoints — so you can focus on serving your customers.

What Aurora delivers:

  • Full-stack GPU cluster deployment — rack to Kubernetes handoff
  • AI-optimized tiered storage integrated into every cluster
  • Multi-tenant isolation with per-tenant GPU quotas and monitoring
  • Private inference endpoints with per-token metering
  • Your site or ours — Aurora-hosted, customer-hosted, or hybrid
  • White-label ready — offer GPUaaS under your brand
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Three Capabilities. One Private AI Stack.

Managed GPU Clusters

Aurora deploys and operates GPU clusters on your infrastructure or ours. Kubernetes with GPU-aware scheduling — delivered as a managed service.

  • 16 to 1000+ node deployments
  • Kubernetes-native with NVIDIA GPU Operator
  • GPU health monitoring, alerting, and lifecycle management
  • Multi-tenant namespace isolation with per-tenant GPU quotas
  • Deploy at Aurora sites or your data center
  • White-label available — offer GPUaaS under your brand
AI-Optimized Storage

Aurora AI Storage provides shared, GPU-aware tiered storage — NVMe for active workloads, HDD for capacity — integrated directly into your GPU cluster via RDMA.

  • S3-compatible API + NFS mount for training frameworks
  • NVMe hot tier for model weights and active training data
  • HDD capacity tier for checkpoints, datasets, and archives
  • RDMA and GPUDirect Storage integration for Tier 0
  • Per-tenant storage isolation with quota enforcement
  • 60–70% cheaper than all-flash at equivalent GPU throughput
Private Inference Endpoints

Turn GPU capacity into metered inference. OpenAI-compatible endpoints for open-weight models, billed per token.



  • OpenAI-compatible API
  • Curated model catalog
  • Model optimization for max tokens per dollar
  • Dedicated or shared endpoints
  • Per-token metering and billing
  • Private deployment — your infrastructure, your data

Choose the Right GPU for Your Workload.

Aurora deploys and configures the right GPU hardware for your requirements.

  B200 B300
GPU Memory 192 GB HBM3e 288 GB HBM3e
Best For Large model training & inference High-memory LLM training & inference
Nodes Available 8x HGX B200 8x HGX B300
Interconnect 800G InfiniBand XDR 800G InfiniBand XDR
Network Topology Non-blocking Spine-Leaf Non-blocking Spine-Leaf
Power/Rack ~40 kW/rack ~45 kW/rack
Pricing Quote-based Quote-based

What Every GPU & AI Deployment Includes.

Managed Inference Endpoints

Deploy open-weight models as private API endpoints. Aurora handles model optimization, serving, scaling, and per-token metering. OpenAI-compatible API.

Isolated AI Execution

Execution environments isolated at the hardware level. Your data and model weights do not share GPU memory or compute resources with other tenants.

Managed Kubernetes with GPU Scheduling

Managed Kubernetes with GPU topology awareness. Workloads are scheduled to the right hardware automatically — B200, or B300.

Deploy Anywhere

Aurora-hosted sites in Norway and the US. Customer-hosted in your data center. Hybrid across both. Same platform, same management, same SLA.

In-Region Deployment

GPU infrastructure deployed in-region. Data does not leave your jurisdiction. Supports air-gapped and data residency requirements.

99.9% Uptime SLA

Aurora operates what it builds. Monitoring, GPU health checks, incident response, and SLA management are part of every engagement.

What Enterprises Run on Aurora GPU & AI.

Neocloud
Operators

GPU hardware and data center capacity without a platform to monetize it. Aurora provides managed Kubernetes, storage, and inference — white-labeled and ready for downstream customers.

AI-as-a-Service Providers

Companies building AI products that need reliable inference infrastructure without managing GPUs. Aurora delivers private inference endpoints with per-token billing.

Enterprise
Private AI

Organizations requiring GPU infrastructure on-prem or in private colo for data residency, regulatory compliance, or cost control. Aurora deploys and manages the full stack on-site.

Research & Scientific Compute

Universities and R&D teams running large-scale simulations, genomics, robotics, or neuroscience. High-memory GPU clusters with shared storage for dataset-intensive workloads.

Sovereign &
Regional AI

Governments and regulated industries requiring AI infrastructure within national borders. Aurora deploys GPU clusters with full data sovereignty, air-gapped options, and in-region operations.

GPU
Co-Lo Clients

Data center tenants requiring GPU infrastructure deployed and operational. Aurora handles hardware procurement, rack-and-stack, networking, and Kubernetes handoff.

Offer Private AI Under Your Brand.
Aurora GPU & AI is available as a fully white-labeled service.
Your customers get private GPU instances and AI inference endpoints — with your logo, your domain, and your pricing. Aurora operates the infrastructure.
Learn more about the Aurora Partner Program
Digital rendering of Aurora Infra Private AI containerized data center

Managed GPU Infrastructure. Ready to Deploy.

LLM training, production inference, regulated AI, sovereign deployments — every engagement starts with understanding the infrastructure requirements.
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Share your requirements and Aurora's team will scope the infrastructure.
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