Aurora Infra Owned Data Center GPU Capacity

Owned data centers

We control the real estate.

Direct OEM procurement

No broker, no waitlist risk.

Well below hyperscaler pricing

Same hardware, lower cost

Public Cloud GPU Infrastructure Is Not Built for Your Workloads.

Hyperscaler GPU quotas are allocated to the largest customers first. When you get access, your training jobs run on shared hardware alongside other tenants. Your data transits infrastructure you don't control, in regions that may not satisfy your compliance requirements. And the economics don't hold at scale — the cost of training a frontier model on public cloud would buy dedicated hardware many times over.

AI labs and ML teams doing serious model development need infrastructure that is truly theirs: dedicated nodes, isolated execution, in-region deployment, and consistent performance that doesn't depend on what other tenants are doing.

Aurora deploys and manages dedicated GPU clusters for AI teams who need that level of control — without building or operating the infrastructure themselves.

Dedicated GPU Infrastructure,
Fully Managed.

Dedicated Hardware — No Shared Tenants

Your GPU cluster is yours. B200 or B300 nodes, or full GB300 NVL72 racks, allocated exclusively to your team. No shared GPU memory, no noisy-neighbor performance variance, no other tenants' workloads on your hardware. Hardware-level isolation via confidential compute on every deployment.

In-Region Deployment — Data Stays in Jurisdiction

Aurora deploys infrastructure in-region by default. Your data does not leave your target jurisdiction without explicit configuration. Supports GDPR, EU AI Act data residency requirements, Canada Protected B, and emerging APAC sovereign AI frameworks. Air-gapped configurations available for the most sensitive workloads.

No CAPEX Required

Aurora structures GPU infrastructure as pure operating expense. No hardware purchase, no depreciation schedule, no capital allocation process to navigate. The cluster is yours — the balance sheet impact is ours. Structures cleanly as a managed infrastructure contract.

Managed Operations — GPU Health to Kubernetes

Aurora operates what it deploys. Continuous GPU health monitoring, incident response, Kubernetes orchestration, and 99.9% uptime SLA included in every engagement. Your ML team focuses on model development. Aurora keeps the infrastructure running.

AI-Optimized Storage — Integrated

GPU-aware tiered storage integrated directly into your cluster via RDMA. NVMe hot tier for model weights and active training data. HDD capacity tier for checkpoints and archived datasets. S3-compatible API. No egress fees.

Managed Inference Endpoints

Deploy models as private API endpoints once training is complete. OpenAI-compatible inference API. Aurora handles model optimization on B200, B300, and GB300 NVL72. Per-token metering or dedicated-throughput configurations. Your data and model weights stay on your infrastructure.

Choose the Right GPU for Your Workload.

B200 and B300 ship as HGX nodes you scale out over InfiniBand. GB300 NVL72 is a rack-scale system: 72 GPUs wired into a single NVLink domain, built for the largest reasoning-inference and training jobs where the whole rack behaves as one accelerator.

Not sure which to deploy? Aurora's engineering team scopes the right hardware configuration for your training and inference requirements as part of the initial conversation.

TECHNICAL REFERENCE

	B200	B300	GB300 NVL72
GPU Memory	192 GB HBM3e	288 GB HBM3e	288 GB HBM3e per GPU (~20 TB per rack)
Best For	Large model training and inference	High-memory LLM training and inference	Reasoning and test-time-scaling inference, plus trillion-parameter training at rack scale
Configuration	8x HGX B200 per node	8x HGX B300 per node	72 Blackwell Ultra GPUs + 36 Grace CPUs per rack (one NVLink domain)
Interconnect	800G InfiniBand XDR	800G InfiniBand XDR	5th-gen NVLink, 130 TB/s in-rack + 800G InfiniBand XDR scale-out
Power per Rack	~40 kW	~45 kW	~132–140 kW
Pricing	Quote-based	Quote-based	Quote-based

Not sure which to deploy? Aurora's engineering team scopes the right hardware configuration for your training and inference requirements as part of the initial conversation.

Platform Specifications

TECHNICAL REFERENCE

GPU Hardware	NVIDIA B200 (192GB HBM3e), NVIDIA B300 (288GB HBM3e), NVIDIA GB300 NVL72 (72 Blackwell Ultra GPUs, 288GB HBM3e each)
Cluster Size	16 to 1,000+ nodes (HGX B200 / B300). GB300 deployed as NVL72 racks, 1 to 16+ racks.
Interconnect	800G InfiniBand XDR (HGX). GB300 NVL72: 5th-gen NVLink, 130 TB/s in-rack + 800G InfiniBand XDR scale-out.
Network Topology	Non-blocking Spine-Leaf
Power per Rack	~40 kW (B200), ~45 kW (B300), ~132–140 kW (GB300 NVL72)
Kubernetes	Managed, GPU topology-aware scheduling, NVIDIA GPU Operator
Multi-Tenancy	Namespace isolation, per-tenant GPU quotas and monitoring
Storage	NVMe hot tier + HDD capacity tier, S3-compatible, RDMA/GPUDirect
Inference	OpenAI-compatible API, per-token metering, open-weight model catalog
White-Label	Console, portal, domain, billing, API endpoints
Confidential Compute	Hardware TEE on B200, B300, and GB300
Deployment Lead Time	2–6 weeks (Aurora sites). Customer-hosted timelines vary.
SLA	99.9% platform uptime
Pricing	Quote-based. Contact sales for configuration and commercial terms.

Built for AI Teams Doing Serious Model Work.

AI Labs and Research Teams

Organizations training frontier models or large-scale foundation models that need consistent, dedicated GPU performance. Aurora provides the cluster; your team focuses on the research.

AI Product Companies

Companies offering AI inference APIs who need reliable, dedicated GPU infrastructure. Aurora delivers private inference endpoints with per-token metering, model optimization, and serving infrastructure.

Regulated AI Deployments

AI teams in financial services, healthcare, or public sector who require private infrastructure that satisfies data residency, air-gap, or sovereign AI requirements. Aurora deploys in-region with BYO KMS encryption and confidential compute.

Teams Moving Off Hyperscalers

Organizations who have run the economics and found that dedicated private infrastructure delivers better performance, better cost, and better control than public cloud GPU at their current scale.

Private GPU Infrastructure That Actually Delivers.

Supply Certainty

Aurora procures B200, B300, and GB300 NVL72 hardware through direct NVIDIA OEM relationships. When Aurora commits to a deployment, the hardware allocation is real — not subject to broker uncertainty or reseller queue positions. Owned data centers across five regions mean we control the real estate behind every deployment.

Economics That Work at Scale

Aurora GPU infrastructure is priced well below equivalent hyperscaler GPU rates for equivalent hardware. The economics improve as your workload scales: dedicated hardware removes the per-unit pricing premium of shared cloud, and Aurora's OPEX structure removes the capital barrier that makes CFO approval difficult.

In-Region by Default

Data sovereignty is not a feature request at Aurora — it's the default deployment model. Every Aurora cluster is deployed in-region. Data does not leave your target jurisdiction without explicit configuration. Supports the full range of enterprise and regulatory data residency requirements.

Dedicated GPU Clusters.
Private Infrastructure.

Public Cloud GPU Infrastructure Is Not Built for Your Workloads.