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NVIDIA GPUS

Our GPUs are the only ones available either on demand or by reserving an instance in a sovereign public cloud. NumSpot is also the only public cloud to offer SecNumCloud-qualified GPUs. NVIDIA GPUs provide exceptional computing capabilities to handle a wide range of workloads (graphics creation, financial transactions, scientific calculations, etc.). Various NVIDIA GPU models are available on Numspot: Kepler (K2), Pascal (P6), Pascal (P100), Tesla (V100), (A100).

Available

The benefits

Reduce IA/ML costs and management effort

No need to purchase, c install and maintain expensive GPU hardware. Infrastructure maintenance and upgrades by the cloud provider.

GPU flexibility and scalability

Easily adjust the amount of GPU resources to your needs. Optimize costs according to needs, and scale resources to cope with larger tasks or workload peaks.

High performance for specialized tasks

Efficient management of intensive parallel computing tasks, such as graphics rendering, big data analysis and machine learning. Guaranteed speed of execution for significant improvements in productivity and ability to innovate.

Features

Instance independence

GPUs can be used independently of the instances to which they are attached. GPUs can be configured and managed separately from compute instances, for greater flexibility in the architecture of cloud solutions.

Multiple model support

Multiple GPUs of the same model can be attached to a single instance. Multi-GPU capability to handle parallel computing-intensive workloads (3D rendering, deep learning, scientific simulations)

Dynamic allocation

Dynamic allocation of resources, which can be attached or detached from instances as required. Flexibility to adjust graphics processing capacity to specific application requirements, optimizing costs and adapting quickly to changing workloads.

“Most” of NumSpot

Performance acceleration

Additional processing power for graphics-intensive and parallel computing tasks. Substantial performance improvements for applications such as graphics rendering, data analysis and machine learning, reducing the time needed to run complex, ressource-intensive processes.

Efficient scalability

Ability to dynamically attach and detach GPUs to specific instances for more flexible, cost-effective management of graphics resources. Simple adaptation of computing infrastructure to actual project requirements, without the need to maintain unused ressources, for optimized costs and enhanced performance.

Cost optimization

Precise control over the use of GPU ressources, in order to pay only for the computing power you need, when you need it. A “pay-as-you-go” approach to graphics-intensive ressources that helps keep costs down while maximizing capacity to respond to peak demand.