What is a cloud GPU?
A cloud GPU is a virtualised or dedicated graphics processor rented from a cloud provider. It can be used flexibly for a few hours, for days or as part of a long-term plan. Thanks to APIs and container stacks, it integrates quickly into existing workflows.
The technology at a glance
- vGPU & SR-IOV: Several virtual machines share one physical GPU with guaranteed isolation.
- MIG (Multi-Instance GPU): Modern GPUs such as the A100 or H100 can be split into up to seven isolated instances – ideal for inference and multi-tenant scenarios.
- NVLink & NVSwitch: High-speed interconnects for multi-GPU jobs that bypass the limits of PCIe.
- NGC containers: Ready-made, optimised containers for AI, ML and HPC speed up deployment.
Typical use cases
- Artificial intelligence: Training and inference of large language models, image and speech processing.
- Rendering & visualisation: 3D graphics, CAD/CAE simulations, digital twins.
- HPC & simulation: Scientific computing, big data analytics.
- Video transcoding: Real-time streaming and media processing.
- Chatbots & real-time APIs: AI-driven communication with low latency.
Pricing models
Cloud GPUs are offered under several billing models:
- On demand: Maximum flexibility, but a higher hourly rate.
- Reserved/committed: Committing for months or years secures discounts and capacity.
- Spot instances: Up to 90 % cheaper, but revocable at any time – suitable only for tolerant workloads.
Alongside the GPU itself, companies should also budget for network, storage and egress.
Benefits of cloud GPUs
- Scalability: Resources grow flexibly with demand.
- No hardware costs: No purchase or maintenance of your own GPU servers.
- Fast integration: Container stacks and APIs shorten time to deployment.
- Flexibility: Different GPU generations and performance classes are available.
Risks and challenges
- Provider dependency: Capacity shortages or price changes take effect immediately.
- Spot interruptions: Only sensible with robust checkpointing.
- Drivers & frameworks: Incompatibilities arise when curated containers are not used.
- Data residency & compliance: Where the data sits is decisive for GDPR and sector-specific requirements.
Cloud GPUs and centron
centron provides cloud GPUs in German data centres certified to ISO 27001. You get maximum performance and the highest level of data security at the same time. Typical building blocks:
| centron component | Role for cloud GPU |
|---|---|
| Cloud GPU | High-performance GPUs for AI, rendering and HPC |
| ccloud³ VM | Flexible orchestration layer for GPU workloads |
| Managed Firewall | Protection for endpoints and APIs in GPU applications |
| Backup & Recovery | Safeguarding training data, models and logs |
| CI/CD Pipelines | Automated deployments for AI and GPU workflows |
FAQ on cloud GPUs
What is a cloud GPU?
A cloud GPU is a graphics processor made available through the cloud. It lets you run compute-intensive tasks such as AI training or rendering without owning any hardware.
What are the benefits of using cloud GPUs?
Companies benefit from scalability, cost savings, flexible usage and fast integration via containers and APIs.
What are the risks?
The main challenges are provider dependency, spot interruptions, compatibility problems and data protection and compliance requirements.
Which scenarios suit cloud GPUs?
They are ideal for AI training, inference, scientific simulations, 3D rendering, video processing and real-time chatbots.
High-performance cloud GPUs from Germany
With centron you use state-of-the-art GPUs such as the NVIDIA A100 or H100 – securely hosted in a data centre certified to ISO 27001 on the basis of IT-Grundschutz. Ideal for AI, rendering and HPC workloads.
Cloud GPU – start here ccloud³ VMs