NVIDIA's A100 and H100 are both high-performance GPUs with a wide range of applications in scientific computing, deep Xi, and data analytics. However, the two products differ in performance and use cases, mainly because of the different hardware architectures they use.
First, let's take a look at the key parameters of the A100:
Architecture: Ampere
Number of CUDA cores: 6,912.
Video memory: 40GB of high-speed HBM2 video memory.
Tensor Core's new Transformer engine enables AI training of large language models to be accelerated by 9 times, and AI inference is accelerated by 30 times.
Next, let's take a look at the key parameters of the H100:
Architecture: hopper
Number of CUDA cores: 14,592.
Video memory: 80GB of HBM2E video memory.
Tensor Core's new Transformer engine enables AI training of large language models up to 30 times faster.
Looking at these parameters, the H100 outperforms the A100 in terms of CUDA cores and video memory, which makes the H100 more performant when handling large-scale data processing and machine Xi tasks. The A100 excels in deep learning Xi and scientific computing tasks, with its new Transformer engine for Tensor Core speeding up AI training for large language models by 9x and AI inference by 30x.
Overall, NVIDIA's A100 and H100 are very powerful GPU products, and they have their own strengths in different application scenarios. Which product you choose depends on your specific needs and application scenario.
Hopefully, this article will help you better understand the difference between Nvidia's A100 and H100. If you have any other questions, feel free to ask me!