NVIDIA H200 GPU is a graphics processing unit (GPU) launched by NVIDIA, specifically designed for generative artificial intelligence and high-performance computing (HPC) workloads. As the first GPU to adopt HBM3e memory, the H200 boasts 141GB of memory and a memory bandwidth of 4.8TB/s, which is nearly double the memory capacity of the NVIDIA H100 Tensor Core GPU, and the memory bandwidth has increased by 1.4 times. This larger memory and faster memory speed not only accelerate generative AI and large language models (LLMs) but also advance scientific computing in HPC workloads with higher energy efficiency and lower total cost of ownership.
The NVIDIA H200 GPU has a wide range of applications, including but not limited to:
Deep Learning Training and Inference: The high performance and large memory of the H200 GPU make it an ideal choice for deep learning training and inference, especially in scenarios that require processing large-scale datasets and complex models.
Natural Language Processing: The H200 GPU can accelerate natural language processing tasks, support the training and inference of language models, and promote the development of language technology.
High-Performance Computing (HPC): The high memory bandwidth of the H200 GPU is crucial for HPC applications as it can transfer data more quickly, reducing complex processing bottlenecks. For HPC applications that require a large amount of memory, such as simulations, scientific research, and artificial intelligence, the high memory bandwidth of the H200 ensures that data can be accessed and operated efficiently, with results up to 110 times faster than CPUs.
The NVIDIA H200 GPU has the following main features:
Enhanced Tensor Cores: The H200 GPU introduces next-generation Tensor Cores, designed specifically for AI workloads, enabling faster matrix calculations and improving efficiency in training and inference tasks.
DPX Instructions: The new DPX (Data Processing Extension) instructions allow the H200 to excel in handling complex, irregular data patterns common in scientific computing and complex machine learning models.
Memory Capacity and Bandwidth: The H200 has up to 141GB of HBM3e memory, with a memory bandwidth exceeding 4.8TB/s, ensuring faster data retrieval and lower latency, which is crucial for high-speed, data-intensive AI applications.
NVLink 5.0 Connectivity: The H200 supports NVLink 5.0, providing faster and more efficient inter-GPU data transfer, significantly reducing bottlenecks in multi-GPU systems and making scaling applications easier.
Multi-Instance GPU (MIG) Technology: NVIDIA's MIG technology allows a single H200 GPU to be divided into smaller instances, optimizing workload distribution and improving efficiency.
Compared to the H100 GPU, the NVIDIA H200 GPU has made significant performance improvements in several aspects:
Memory Capacity and Bandwidth: The H200 is the first GPU to use HBM3e memory, offering 141GB of HBM3e memory with a memory bandwidth of 4.8TB/s, which is nearly double the 80GB HBM3 memory of the H100, and the memory bandwidth has increased by 1.4 times. This larger memory and faster memory speed not only accelerate generative AI and large language models (LLMs) but also advance scientific computing in HPC workloads with higher energy efficiency and lower total cost of ownership.
AI Inference Performance: The H200 is up to 2 times faster than the H100 in processing large language models (LLMs) such as Llama2. This means the H200 can provide higher throughput and faster processing speeds in AI inference tasks.
HPC Performance Improvement: The high memory bandwidth of the H200 is crucial for HPC applications as it can transfer data more quickly, reducing complex processing bottlenecks. The high memory bandwidth of the H200 ensures that data can be accessed and operated efficiently, with results up to 110 times faster than CPUs.
Energy Efficiency and Total Cost of Ownership (TCO): While maintaining the same power consumption level as the H100, the H200 effectively reduces the total cost of ownership by 50% by reducing the energy consumption of LLM tasks by 50% and doubling the memory bandwidth.
Performance Improvement: In specific generative AI and HPC benchmarks, the H200's performance has increased by up to 45%, mainly due to the increased HBM3e memory capacity, increased memory bandwidth, and thermal management optimization of the H200.
P2P Bandwidth: The H200's P2P bandwidth is 900GB/s, achieving a 43% increase in memory bandwidth compared to the H100.
In summary, the NVIDIA H200 GPU has made significant improvements over the H100 GPU in terms of memory capacity, memory bandwidth, AI inference performance, HPC performance, energy efficiency, and total cost of ownership. These improvements make the H200 an ideal choice for processing large-scale data and complex computational tasks.
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