This past week, NVIDIA introduced the H200 Tensor Core GPU. This GPU hopes to set a new benchmark as the world’s most powerful GPU designed to supercharge artificial intelligence and high-performance computing or HPC workloads.
The H200 Tensor Core is based on the NVIDIA Hopper architecture the GPU features HBM3e, providing an unprecedented 141 gigabytes of memory at an astounding 4.8 terabytes per second. This is nearly double the capacity of its predecessor, the NVIDIA H100 Tensor Core GPU, with 1.4 times more memory bandwidth.
As we can see, this is a remarkable increase in memory size and speed accelerates generative AI and large language models while elevating scientific computing for HPC workloads. For example, in Llama2 70B inference, it achieves 1.9 times faster speeds, and GPT-3 175B inference at 1.6 times faster.
Memory bandwidth plays a pivotal role in HPC applications, enabling faster data transfer and reducing processing bottlenecks. For memory-intensive tasks such as simulations, scientific research, and AI, the H200’s higher memory bandwidth translates to a remarkable 110 times faster time to results compared to traditional CPUs.
The H200 Tensor Core also boosts inference speed by up to 2 times compared to its predecessor, the H100 GPU, particularly when handling complex LLMs like Llama2. With that said, the H200 maintains energy efficiency, operating within the same power profile as the H100.
This helps companies to maintain and align with eco-friendly practices. Energy consumption has been a growing issue as more companies make the move toward generative AI and it seems that NVIDIA has kept that in mind with the H200 Tensor Core.
NVIDIA hopes that with the introduction of the H200, it is setting a new standard for GPU capabilities. This is becoming more important as the AI and scientific communities continue to depend on AI output to aid in cutting-edge research.
Over the last few years, more and more research teams have been depending on the power of models to find new protein combinations and detect Parkinson’s disease. How this will affect the overall GPU market is still unknown.