What GPU hardware does Aurora use for AI infrastructure?

Aurora operates NVIDIA B200 and B300 GPU clusters, supporting deployments from 100kW to MW+ scale, managed end-to-end with a 99.9% uptime SLA.

How much cheaper is Aurora GPU infrastructure compared to AWS or Azure?

Aurora GPU infrastructure is priced 2–7x lower than equivalent hyperscaler GPU services, with no egress fees and no lock-in.

Can Aurora run private AI inference without data leaving my jurisdiction?

Yes. Aurora deploys private AI inference infrastructure in-region by default. Data does not leave the target jurisdiction without explicit configuration.

Rent GPU Clusters | Managed GPU Cloud

When running your own AI infrastructure is the hard part…

Whether you are training models, serving inference, or adding an AI layer to a product you already sell, the same wall shows up. Getting GPUs, and running them well, is a project of its own.

Quotas and waitlists.

Current-gen accelerators come in fractions, in a region you did not pick, weeks late.

Contention.

On oversubscribed or fractional instances, interconnect and storage bandwidth swing under load, so your runs are not predictable.

Economics that punish scale.

On-demand pricing is built for spikes. Run steady for a quarter and the bill is a multiple of what that compute should cost.

Residency you cannot honor.

Current-gen GPUs, in your jurisdiction, with sovereign or air-gapped options are rarely on the menu.

A team you did not plan to hire.

Running the platform takes GPU and ops engineers who are not building your product.

CAPEX you do not want.

Owning the metal means a purchase order, a supply chain, a data-center contract, and an ops team before a job runs.

Aurora clears all of this. You get dedicated GPU capacity, built and operated for you, so your team ships AI instead of running infrastructure.

What is a managed GPU cluster from Aurora?

A dedicated GPU cluster that Aurora designs, builds, and operates for you, deployed into ready capacity and handed to your team through a Kubernetes API. It is GPU as a service (GPUaaS) with the hardware dedicated to you, not shared.

Dedicated, Single-Tenant Hardware

Your nodes are yours. NVIDIA B200, B300, and GB300 NVL72, with 800G InfiniBand XDR and non-blocking spine-leaf fabric so bandwidth does not move under load.

Managed Operations

Aurora runs it: GPU health monitoring, incident response, and break/fix. You get GPU capacity that stays up, not a rack you have to babysit.

No CAPEX

The cluster is an OPEX line. No purchase orders for accelerators, no supply chain, no depreciation schedule.

Integrated Storage

NVMe hot tier and HDD capacity tier, S3-compatible, with RDMA and GPUDirect so data reaches the GPUs at line rate.

In-Region & Sovereign

Deploy in North America, Western Europe, the Nordics, the GCC, or APAC. Air-gap available for qualifying workloads.

Serve What You Train

Hand your training checkpoints straight to Aurora Inference to serve models in the same region.

Turnkey cluster or bespoke buildout? Pick the right path.

Aurora runs two Hard Infra pathways.

	GPU Clusters	Data Centers
What it is	A managed GPU cluster deployed into ready or managed capacity	A new data-center facility engineered and built to your spec
You get	Dedicated GPU nodes, turnkey, operated by Aurora	Custom facility and large dedicated capacity
Speed	Live in weeks	Longer, engineered buildout
Best when	You need GPUs running soon, into existing capacity	You need new capacity purpose-built at scale
Get started	Reserve GPUs →	Scope a build →

GPU Clusters deploy into ready or managed capacity. Data Centers build new capacity to spec. If you are unsure, start with Reserve GPUs.

Hardware & GPU Rental

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

	B200	B300	GB300 NVL72
GPU Memory	192 GB HBM3e	288 GB HBM3e	288 GB HBM3e (~20 TB/rack)
Node / rack config	8x HGX per node	8x HGX per node	72 Blackwell Ultra GPUs + 36 Grace CPUs per rack
Scale-out fabric	800G InfiniBand XDR	800G InfiniBand XDR	800G InfiniBand XDR
In-rack interconnect	NVLink	NVLink	5th-gen NVLink, 130 TB/s in-rack
Power	~40 kW/rack	~45 kW/rack	~132 to 140 kW/rack
Deploy unit	16 to 10,000+ nodes	16 to 10,000+ nodes	1 to 16+ NVL72 racks
Best for	Large-scale training and inference	Memory-heavy training, long context	Frontier-scale training, largest models

Cluster and Fabric

Cluster size: 16 to 10,000+ nodes. GB300 deploys as NVL72 racks, 1 to 16+.
Non-blocking spine-leaf network. 800G InfiniBand XDR scale-out.
GPU topology-aware scheduling so jobs land on the right interconnect neighbors.

Orchestration

Managed Kubernetes with NVIDIA GPU Operator.
Autoscaling and a clean Kubernetes API handoff to your team.
Per-tenant namespaces.

Isolation and Security

Hardware TEE isolation.
Encryption in transit and at rest.
Bring your own KMS (HashiCorp Vault).
Air-gap available for qualifying workloads.

Storage

NVMe hot tier plus HDD capacity tier.
S3-compatible.
RDMA and GPUDirect for direct-to-GPU data paths.

Explore Aurora Storage →

Deployment

Aurora-hosted or customer-hosted, hybrid supported.
Regions: North America, Western Europe, Nordics, GCC, APAC.
Lead time: based on scope

Operations and Commercial

99.9% uptime SLA.
Managed by Aurora: GPU health monitoring, incident response, break/fix.
No CAPEX, OPEX structure. Pricing is quote-based.
Direct NVIDIA OEM procurement, owned data centers.
Far below hyperscaler GPU rates

Who is an Aurora GPU cluster for?

AI labs training foundation models

who need thousands of B300 or GB300 GPUs on a non-blocking fabric, in weeks, without buying the hardware.

Enterprises with sovereign data

who must keep training and inference inside a jurisdiction, with air-gap for the regulated slice.

Teams already on Kubernetes

who want a topology-aware managed cluster handed over through the K8s API, not a pile of raw instances.

Product teams shipping inference at scale

who train on Aurora GPUs and serve on Inference in the same region.

Groups escaping hyperscaler GPU rates

who run clusters 24/7 for months and need OPEX economics that hold at scale.

SaaS and product teams adding AI

to what they sell, who need capacity to fine-tune and serve their models without hiring a platform team.

Why rent your GPU cloud from Aurora?

We operate, not just provision.

Aurora engineers deploy and run GPU clusters at scale. Managed means we own uptime, incidents, and break/fix.

Dedicated, sovereign, in-region.

Private capacity in the region you require, with air-gap for qualifying workloads.

Direct NVIDIA OEM procurement.

Current-generation Blackwell and Blackwell Ultra, in owned data centers, without a hyperscaler waitlist.

Turnkey speed.

Designed, built, and live in weeks, deployed into ready or managed capacity.

Priced for real workloads.

Quote-based, structured as OPEX, far below hyperscaler GPU rates.

One stack.

GPUs, Storage that feeds them, and Inference to serve models, on a single console.

Frequently-Asked Questions

Can I rent GPUs from Aurora instead of buying them? Yes. Aurora GPU rental is a managed GPUaaS model. You rent dedicated NVIDIA B200, B300, or GB300 nodes as an OPEX line, with no CAPEX and no hardware to buy or rack.

How fast can a GPU cluster be deployed? Depending on your needs, it can be in just weeks, because Aurora deploys into ready or managed capacity rather than building a new facility.

What is the difference between GPU Clusters and Data Centers? GPU Clusters deploy a managed cluster into ready or managed capacity, turnkey. Data Centers build new capacity engineered to your spec. Start with Reserve GPUs if you need GPUs running soon. See Data Centers.

Do I get managed Kubernetes? Yes. Managed Kubernetes with NVIDIA GPU Operator, GPU topology-aware scheduling, autoscaling, and a clean Kubernetes API handoff to your team.

Where can Aurora deploy my GPU cloud? Aurora-hosted in our global network of data centers, or in your own capacity, hybrid supported. Regions: North America, Western Europe, Nordics, GCC, APAC. Air-gap is available for qualifying workloads.

How is my data isolated? Hardware TEE isolation, per-tenant namespaces, encryption in transit and at rest, and bring your own KMS with HashiCorp Vault.

How much does it cost? Pricing is quote-based and structured as OPEX, positioned a vast amount below hyperscaler GPU rates. Reserve GPUs for a configuration quote.

Rent and deploy dedicated GPU clusters,
built and operated for you