The future is filled with endless AI possibilities. In this fast-paced tech world, choosing the most effective graphics processing unit (GPU) is crucial for staying ahead. Two top contenders in this space are the NVIDIA H100 and H200. If you're curious about the main features, performance, and suitability of these GPUs for different applications, read on.

NVIDIA H100

The NVIDIA H100 is the latest and most advanced GPU in NVIDIA's Hopper architecture lineup. For those of you who have no clue about Hopper architecture GPUs, it is NVIDIA's next-generation GPU architecture, designed to provide significant performance improvements and enhanced efficiency for AI and high-performance computing (HPC) workloads.
It boasts impressive specifications, including:

• 80 billion transistors

• 16896 CUDA cores

• 456 Tensor Cores

• 80GB of HBM3 memory with a bandwidth of 3 TB/s

• Support for multi-instance GPU (MIG) technology

The H100 is designed to support a wide range of AI workloads, from natural language processing (NLP) and computer vision to recommender systems and more. Its sophisticated Tensor Cores are at the core of this performance boost, enabling efficient matrix multiplication and accumulation, which are key for deep learning tasks. These advancements implies that the H100 can handle complex AI models with greater speed and efficiency, making it an invaluable asset for developers and researchers working on cutting-edge AI applications. Whether it's speeding up training times or enhancing the accuracy of AI computations, the H100 is designed to meet the demands of the most challenging AI environments, supporting the development of more advanced and powerful AI solutions.

NVIDIA H200

The NVIDIA H200, also known as the "Grace Hopper Superchip," is a powerful combination of the NVIDIA Grace CPU and the NVIDIA Hopper GPU. Briefing about these terms, NVIDIA Grace CPU is designed for data centres and is NVIDIA's first CPU. It's built to excel in handling AI and HPC (High Performance Computing) workloads efficiently. NVIDIA Hopper GPUs are the next generation of graphics processing units from NVIDIA. They are anticipated to offer significant advancements in performance and efficiency compared to previous GPU architectures.
H200 GPU features include 141GB of HBM3e memory making it perfect for generative AI and HPC workloads. It integrates with the NVIDIA Grace CPU for optimal performance and supports multi-instance GPU (MIG) technology for flexible resource allocation. Real-time ray tracing enhances graphics and visualizations, while Tensor Cores accelerate AI and deep learning tasks. Improved power efficiency and thermal management ensure high performance without significant power increases.

The NVIDIA H200 will be available from global system manufacturers and cloud service providers in the second quarter of 2024. The H200 is particularly well-suited for large-scale AI models that require both high-performance computing (HPC) and AI acceleration. Its unique architecture allows for seamless integration of CPU and GPU resources, making it an attractive choice for complex AI workloads.

Performance Comparison

In the Showdown of H100 vs H200, both the GPUs are impressive. However, their performance may vary depending on the specific workload and application. For tasks that heavily rely on GPU acceleration, such as image recognition or language modelling, the H100 may have a slight edge due to its higher number of CUDA and Tensor Cores. However, for workloads that require a balance of CPU and GPU resources, the H200 may be the better choice, thanks to its CPU-GPU hybrid design.

Ideal Applications

Both the H100 and H200 are well-suited for a wide range of AI applications, but their strengths may differ:

• H100: Ideal for large-scale AI models, NLP tasks, computer vision, and recommender systems that require high-performance GPU acceleration.

• H200: Suitable for complex AI workloads that demand a balance of CPU and GPU resources, such as large-scale data processing, HPC simulations, and AI-powered scientific computing.

The H200 is particularly well-suited for machine learning tasks due to its increased memory and bandwidth. It supports the training of larger models and the processing of larger datasets, making it ideal for deep learning and AI applications. The H200's memory bandwidth of 4.8 TB/s is 1.4x faster than the H100's, which significantly enhances its performance in AI inference and model training.

The NVIDIA H100 and H200 GPUs deliver exceptional performance for AI applications, each suited to different workload needs. The H100 is versatile for various AI tasks, while the H200 stands out with advanced memory and seamless integration with the Grace CPU. Both GPUs are set to drive AI innovation and provide a future-proof solution for computing infrastructure. Selecting the GPU that aligns with your specific requirements and budget will keep your systems at the forefront of technology.