European GPU infrastructure for AI teams.
Compare accelerators, configure containerized GPU instances in Paris or Frankfurt, and manage storage, clusters and crypto-funded usage from one workspace.
Capacity board
Deploy a GPU
Region
Auto · 22ms
Image
PyTorch 2.6
Mode
On-demand
NVIDIA H100 SXM
80 GB HBM3 · EU-WEST
$2.69/hr
Capacity request
NVIDIA RTX 5090
32 GB GDDR7 · EU-CENTRAL
$0.69/hr
Capacity request
NVIDIA L40S
48 GB GDDR6 · EU-CENTRAL
$0.79/hr
Capacity request
Persistent
account data
Isolated
workspaces
Protected
server sessions
Auditable
resource changes
Start with the constraint
Find the GPU rental page that answers your job.
One page owns each search intent: the catalog compares cards, pricing explains the full bill, use-case pages size real workloads and the selector exposes every assumption.
01
Compare cloud GPUs
19 priced NVIDIA and AMD accelerators, from 16 to 288 GB.
02
Estimate GPU cost
Compare hourly rates, storage costs, prepaid deposits and monthly rental invoices.
03
Choose by workload
Filter the catalog with peak VRAM, headroom and duration.
04
Rent GPUs in Europe
Paris and Frankfurt request regions with scoped compliance guidance.
One control plane
Every stage of AI compute, connected.
Develop, train, serve and scale without moving credentials, storage or billing between clouds. Choose the execution model that fits each workload.
GPU Cloud
Dedicated GPU instances
SSH, Jupyter, custom containers and persistent storage for development, training and long-running jobs.
19 priced GPU configurations
Clusters
Distributed training
Multi-node GPU clusters, high-speed fabric, shared storage and Slurm for serious model and HPC workloads.
Up to 64 GPU self-serve
Storage
Persistent network volumes
Keep datasets, checkpoints and model artifacts independently from compute, then attach them by region.
From $0.07/GB/month
Templates
Reusable Docker environments
Save images, commands, disks, ports and non-secret environment settings for repeatable launches.
Pod · Cluster · Serverless
One lifecycle
From notebook to production endpoint.
The same identity, secrets, storage and cost controls follow every stage.
Spin up
Choose any available GPU and a trusted template.
Build
Train, fine-tune or process data in your own environment.
Deploy
Turn the image or repository into a versioned endpoint.
Scale
Route requests, watch workers and let capacity return to zero.
Endpoint observability
One measured view
Measured
throughput
P50–P99
latency
Autoscaled
workers
Attributed
costs
Production inference
Scale fast. See everything.
Queues, workers, releases, latency percentiles, cold starts and costs live in the same view. No observability stack required just to understand an endpoint.
- Model-aware warm starts
- Per-request logs and retry states
- GPU and region priority routing
Built for trust
European by default. Enterprise by design.
Residency, operator tier and deployment boundary should be visible—not buried in a sales deck. Compliance claims remain explicitly scoped to the contracted region and provider.
Security overviewScoped compliance
A clear trust center and downloadable evidence per eligible region.
Private by default
Secrets, role-based access and isolated networking from the first resource.
Data residency
Choose where volumes and workloads live, then keep the policy visible.
Cost governance
Budgets, alerts, projects and cost centers that mirror your organization.
Questions
Infrastructure, without the mystery.
What does GPURento provide?+
GPURento brings account management, GPU resources, clusters, storage, templates, budgets and usage records into one control plane.
How is GPU usage priced?+
Compute is presented as a per-second meter with an hourly reference rate. Storage, reserved IPs and optional premium networking are estimated separately before deployment.
Can I bring my own Docker image?+
Yes. The product supports public and private OCI registries, environment variables, secrets, custom start commands, HTTP/TCP ports and reusable templates.
Which workload belongs on serverless?+
Serverless fits bursty or request-driven inference. Use a GPU instance for interactive development or long jobs, and a cluster when one node is not enough.
Where can workloads run?+
The experience is designed around EU-first regions with optional US capacity. Region, operator tier, latency and availability are visible before deployment.
Account-backed control plane
Put your next workload on the board.
Monthly GPU rental
Around 30% less for continuous use · Storage is billed separately.