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GPU

NVIDIA A100 Tensor Core GPU instances to accelerate your application computations on a wide variety of artificial intelligence and high-performance computing tasks

Available Q2 2024

Speeding up treatment

Putting pre-trained neural networks into production plays an essential role in developing responses and recommendations for AI services. Our GPUs can deliver up to 27 times the inference performance of a single-socket server, significantly reducing operating costs.

Innovating with AI and Machine Learning

To boost the productivity of data scientists and implement new AI services more quickly, you need to train increasingly complex models faster and faster. Our NVIDIA A100 GPUs reduce Deep Learning training times to just a few hours.

Improving efficiency

Fully compatible with the Kubernetes platform, the container systems and the virtual machines environment of the trusted cloud, the multi-instance technology of A100 GPUs simplifies access to computing resources for all users, regardless of the type of workload.

Benefit from an agile solution

Adjust the number of GPU instances to suit your needs: multi-instance (MI) GPU technology enables a GPU to be partitioned into seven separate secure instances, each with 5GB or 10GB of dedicated memory. Your users can access all the benefits of GPU acceleration.

Technical
specifications

FP64
9.7 TFLops

FP64 Tensor Core
19.5 TFlops

FP32
19.5 TFlops

Tensor Float 32 (TF32)
312 TFlops

INT8 Tensor Core
1248 TFlops

FP16 Tensor Core
624 TFlops

BFLOAT 16 Tensor Core
624 Tflops

Memory
80 GB HDBM2e at 1935 GB/S

Multi instance
Up to 7 MIG instances at 10 GB

Pricing

Want to know the prices of our GPU products?

engineering
Use cases

Engineering and simulation

GPU instances are used in the most powerful modelling systems, offering the power required to run simulation tools in real time and accelerate innovation.

research AI machine learning
Use cases

Artificial intelligence and machine learning

Thanks to the high inference performance of NVIDIA A100 GPUs, create and train artificial intelligence neural networks and process massive volumes of data.

scientific research
Use cases

Scientific research

Fundamental and applied research institutes can harness the power of the GPU to model complex scenarios and reduce the time needed to analyse the data.

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