NVIDIA H100 GPU

With 80GB of HBM2e memory, the NVIDIA H100 GPU provides sufficient capacity to handle large datasets and complex AI models.

For example, in the financial sector, extensive datasets with transaction data can be stored in memory, enabling real-time fraud detection and risk analysis. This is also crucial for scientific research, such as climate modeling, where enormous amounts of data need to be continuously processed and analyzed. This allows researchers to make more accurate predictions and gain insights faster without constantly waiting for data transfers to and from external storage.

This extensive memory configuration ensures that the GPU can perform multiple intensive tasks simultaneously without performance drops, which is essential for companies working with intensive AI and data analysis applications.

Memory bandwidth refers to the speed at which data can be transferred between the memory and the GPU. The H100 has a bandwidth of 3.35 TB/s.

For example, when simulating a large climate model, the high speed of 3.35 TB/s means that data can be quickly sent to the GPU's processing cores, resulting in faster simulations and quicker results. This prevents bottlenecks that would otherwise occur with slower data transfer.

The H100 offers up to 34 teraflops of FP64 calculations. FP64 performance refers to the speed at which the GPU can perform double-precision calculations, expressed in teraflops.

For example, in scientific applications such as climate modeling, highly accurate calculations are needed. The high FP64 performance of the H100 enables these complex calculations to be performed quickly and accurately, which is essential for reliable results.

The speed of data transfer between multiple GPUs is called NVLink bandwidth. The H100 supports NVLink bandwidth of 900 GB/s.

For example, during AI training where multiple GPUs collaborate, they can communicate quickly and efficiently via NVLink, reducing latency and increasing the speed of data transfer between GPUs. This shortens the training time for AI models, resulting in faster and more effective AI implementations.

Tensor Cores are specialized processing cores within the GPU designed to accelerate the performance of machine learning and AI computations.

For example, in deep learning tasks such as image recognition, Tensor Cores can perform calculations much faster than traditional GPU cores. This enables AI models to be trained and deployed more quickly, helping companies gain insights and results faster.

The H100 is based on the advanced Hopper architecture. This offers improved performance and efficiency compared to previous generations. This means that companies can benefit from the latest technological advancements for their AI and HPC applications, such as faster computing power and improved energy efficiency.