The fundamentals of the GPU offering
Cloud Temple's GPU offering provides access to the latest-generation NVIDIA graphics cards in a secure sovereign cloud environment. Positioned as the premium solution for accelerating AI and high-performance computing workloads with the strictest sovereignty requirements.
Our compliance procedures
Our GPU offering is HDS and ISO 27001 certified, and available on SecNumCloud-qualified and C5-compliant services.
The key benefits of Cloud Temple's GPU offering
Faster processing
Up to 27x faster processing
Run your inference workloads much faster thanks to the power of GPUs, capable of achieving up to 27 times the performance of CPUs for certain intensive processes.
Accelerating AI innovation
Drastically reduce training times
Accelerate your artificial intelligence and machine learning projects by dramatically reducing the training times for deep learning models, so you can iterate faster and get your innovations into production more quickly.
Integration into your environments
Compatible with your existing infrastructure
Easily deploy your GPU workloads in your environments thanks to compatibility with Kubernetes, containers and virtual machines, for seamless integration into your cloud and DevOps architectures.
Flexibility and sovereignty
Multi-instance GPU in a trusted cloud
Optimise the use of resources thanks to the secure partitioning of GPUs in multi-instances, while benefiting from an infrastructure hosted in a qualified, trusted cloud that guarantees the sovereignty of your data and your calculations.
The key features of our GPU
Flash storage
5 performance levels (500 to 15000 IOPS/To)
Multi-Instance GPU
Partitioning into 7 instances
Native Kubernetes
OpenShift integration and containers
Dedicated VMs
Virtual machine support
Technical specifications
Thinking about a cloud project? Let's talk about your project.
Are you planning to modernise your infrastructures, optimise the performance of your databases or secure critical applications? Our team can help you define your needs and assess the relevance of a GPU offering, tailored to your performance, security and sovereignty requirements.
Share a few details about your project via the form—we’ll get back to you quickly to discuss it.
Use cases
These instances are designed to provide massive computing power, essential for artificial intelligence, machine learning and scientific computing. They are particularly relevant for training complex models, high-performance modelling and the deployment of inference services requiring a strictly sovereign framework.
Your processing will be considerably accelerated, offering inference up to 27 times faster than with a conventional processor (CPU) and reducing Deep Learning training times to just a few hours. The multi-instance partitioning technology (up to 7 instances) will also enable you to make the most of your resources while taking advantage of the flexibility of Kubernetes environments and containers.
Our offering combines the power of the latest NVIDIA cards with the highest levels of security and sovereignty. It is hosted in France in a trusted SecNumCloud and ISO 27001-qualified cloud, ensuring complete isolation of your workloads and encryption of your data.
Access to your resources is very fast, with an estimated provisioning time of just 30 minutes to launch your GPU instances on our dedicated infrastructure.
Absolutely not: the entire service is based on Cloud Temple's Bare Metal servers, specially dedicated to NVIDIA equipment. You use our own highly optimised infrastructure, with dedicated memory per instance (from 5 to 10 GB per MIG instance), without any impact on your own internal resources.
Reversibility is fully guaranteed, in full compliance with the Data Act. We guarantee free export of your AI models in standard market formats (such as ONNX) within 15 days, and of your data in open formats within 30 days. Technical assistance to help you migrate is also available on request.
There are four key phases in setting up your project:
- Precise assessment of your needs to calibrate resources according to your IA workloads.
- Choosing the right hardware architecture.
- Provisioning on our dedicated infrastructure.
- The final configuration of your artificial intelligence environments.