What is a Hopper GPU?

The Hopper GPUs are the latest generation of NVIDIA high performance GPU. Specially designed for AI and HPC, these GPUs make it possible to develop a wide variety of workloads. They are based on innovative architecture with powerful Tensor Cores and combine several advanced technologies to maximize performance. NVIDIA HOPPER GPUPPER is suitable, among other things, to IA inference, Deep Learning and generative AI.

NVIDIA HOPPER HOPPER architecture

The name « GPU Hopper » comes from Hopper architecture which, as a GPU microarchitecture, constitutes the basis of high performance GPUs, optimized for IA workloads and HPC applications. Hopper GPUs are made by TSMC in 4 nanometers technology And have eighty billion transistors, making it the most advanced graphics cards available on the market at present.

With Hopper architecture, Nvidia combines the latest generation of Tensor Cores with five advanced innovations: transforming Engine, NVLink/NVSWSWITCH/NVLINK switching systems, Confidential Computing, Multi-Instances (MIG) of second generation and DPX instructions. Thanks to these technologies, HOPPER GPUs accelerate IA inferenceup to thirty times compared to the previous generation. This is based on the results of the Megatron 530B chatbot in Nvidia, the largest generative language model in the world.

The innovative features of Hopper GPUs

Hopper GPUs have several unpublished features that help improve their performance, efficiency and scalability. Here are the main news:

• Transform Engine : Transforming it Engine allows HOPPER GPUs to cause AI models up to nine times faster. For inference tasks in the field of linguistic models, GPUs reach acceleration up to thirty times higher than that of the previous generation.
• NVLink switching system : The fourth generation of NVLink provides a bidirectional GPU band from 900 GB/S, while NVSWitch ensures better scalability of H200 clusters. This guarantees effective treatment of AI models with parameter billions.
• Confidential Computing : Hopper architecture guarantees the protection of your data, AI models and algorithms, even during processing.
• Multi-instance GPU (MIG) 2.0 : The second generation of MIG technology makes it possible to divide a single HOPPER GPU into maximum isolated instances. This allows several people to simultaneously treat different workloads without mutually harming.
• DPX instructions : DPX instructions make it possible to calculate dynamically programmed algorithms up to seven times faster than with the GPUs of the Ampere architecture.

What are the use cases of GPUs

NVIDIA GPUs based on Hopper architecture are designed for high performance workloads of different types. The main areas of application of Hopper GPUs are:

• Inference tasks : These GPUs are among the leading industry solutions for the productive use of IA inference. Whether it is recommendation in electronic commerce, medical diagnostics or real -time predictions for autonomous driving, Hopper GPUs can quickly and effectively process enormous amounts of data.
• Generative : High -end GPUs provide the computing power necessary for the training and execution of generative AI tools. Parallel treatment allows more effective calculations for creative tasks such as generation of text, images and videos.
• Deep Learning training : Thanks to their great computing power, the Hopper GPUs are perfectly suitable for training large neural networks. Hopper architecture considerably reduces training times for AI models.
• Conversational : Being optimized for natural language treatment (NLP), HOPPER GPUs are ideal for VOCAL Systems based on AI such as virtual assistants and AI chatbots. They accelerate the treatment of large AI models and guarantee reactive interactions that integrate transparently into business processes, such as assistance.
• Data analysis and Big Data : HOPPER GPUs manage enormous amounts of high -speed data and accelerate complex calculations thanks to a massive parallel treatment. This allows companies to evaluate the Big datato establish forecasts and take the appropriate measures.
• Science and research : As these GPUs are designed for HPC applications, they are perfectly suited to very complex simulations and calculations. Hopper GPUs are thus used in astrophysics, for climate modeling and computer chemistry.

Current Nvidia models

With the NVIDIA H100 and the NVIDIA H200, the American company has already launched two HOPPER GPUPs on the market. The Nvidia A30 is still based on Ampere architecture. Note that the H200 is not really an independent model, but rather an evolution of the H100. The differences between the two GPUs are as follows:

• Memory and bandwidth : While the NVIDIA H100 is equipped with an 80 GB HBM3 memory, the H200 GPU has an HBM3E memory with a capacity of 141 GB. In terms of memory bandwidth, the H200 is clearly in advance with 4.8 TB/S against 2 TB/S for the H100.
• Performances for IA inference : in comparison, the NVIDIA H200 provides an inference power twice as high for LLAMA 2-70 models compared to its previous version. This not only allows faster treatment, but also effective scalability.
• HPC applications and scientific calculations : The H100 already offers a first -rate level of performance for complex calculations, which the H200 still surpasses. The speed of inference is up to twice as high and HPC performance is around 20 % higher.