What GPU hardware does Aurora deploy?

Aurora deploys NVIDIA B200 and B300 GPU configurations. The B200 provides 192 GB HBM3e memory per GPU and is suited for large model training and inference. The B300 provides 288 GB HBM3e memory per GPU and is suited for high-memory LLM training and inference workloads. Both configurations use 8x HGX nodes, 800G InfiniBand XDR interconnect, and non-blocking Spine-Leaf network topology. All pricing is quote-based — contact Aurora sales for configuration details.

How does Aurora handle GPU cluster management?

Aurora provides fully managed Kubernetes with GPU topology awareness using the NVIDIA GPU Operator. Workloads are scheduled to the correct hardware automatically. Aurora handles GPU health monitoring, alerting, driver and firmware updates, and lifecycle management. Multi-tenant namespace isolation provides per-tenant GPU quotas and monitoring. Cluster scale ranges from 16 to 1000+ nodes.

What storage does Aurora provide for GPU workloads?

Aurora provides AI-optimized tiered storage integrated directly into GPU clusters via RDMA. The NVMe hot tier serves model weights and active training data. The HDD capacity tier stores checkpoints, datasets, and archives. Storage is accessible via S3-compatible API and NFS mount for training frameworks. GPUDirect Storage integration is available for Tier 0 workloads. Per-tenant storage isolation with quota enforcement is included. Aurora's tiered storage approach is 60 to 70 percent cheaper than all-flash storage at equivalent GPU throughput.

How do Aurora's private inference endpoints work?

Aurora's private inference endpoints are OpenAI-compatible and support open-weight model deployment. Aurora handles model optimization, serving, scaling, and per-token metering. Endpoints are private — model weights and inference requests do not touch shared infrastructure. Dedicated or shared endpoint configurations are available. Billing is per token consumed. The endpoints run on the customer's infrastructure or Aurora-hosted sites — no public routing, no shared compute.

What does isolated AI execution mean in Aurora's platform?

Isolated AI execution means that customer data and model weights do not share GPU memory or compute resources with other tenants at the hardware level. Each execution environment is private by design. Air-gapped deployment is available for workloads with the highest data sensitivity requirements. This makes Aurora suitable for regulated industries — financial services, healthcare, government — where shared public infrastructure cannot satisfy data isolation requirements.

Can Aurora's GPU and AI platform be white-labeled?

Yes. Aurora GPU and AI is available as a fully white-labeled service. Partners present GPU instances and AI inference endpoints under their own brand — their logo, their domain, and their pricing. Aurora operates the infrastructure and remains invisible to the end customer. This model is used by neocloud operators, telcos, and AI-as-a-service providers who want to offer GPUaaS under their brand without building or operating the underlying infrastructure.

What use cases does Aurora GPU and AI support?

Aurora GPU and AI supports LLM training and fine-tuning on high-memory B300 clusters, production private inference for enterprise AI applications, regulated industry AI for financial services and healthcare and government organizations, sovereign and regional AI deployments with in-country data requirements, AI-as-a-service products requiring reliable inference infrastructure, research and scientific compute for genomics and climate modelling and autonomous systems, and neocloud operators monetizing GPU hardware under their own brand.

Managed GPU Infrastructure & AI Platform

Q: What is the difference between Aurora-hosted and customer-hosted GPU deployments?

Aurora-hosted deployments run on Aurora infrastructure at sites in Norway and the United States. Customer-hosted deployments run on the customer's own data center infrastructure — Aurora handles hardware procurement, rack-and-stack, networking, Kubernetes, and ongoing operations. Hybrid deployments across both are supported. All three options use the same Aurora platform, the same management layer, and the same 99.9% uptime SLA.

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

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