{"id":8225,"date":"2023-11-30T06:19:17","date_gmt":"2023-11-30T14:19:17","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?p=8225"},"modified":"2025-11-17T20:46:51","modified_gmt":"2025-11-17T20:46:51","slug":"comparison-of-nvidia-a100-h100-and-h200-gpus","status":"publish","type":"post","link":"https:\/\/www.comet.com\/site\/blog\/comparison-of-nvidia-a100-h100-and-h200-gpus\/","title":{"rendered":"Comparison of NVIDIA A100, H100 + H200 GPUs"},"content":{"rendered":"\n
\n

A significant player is pushing the boundaries and enabling data-intensive work like HPC and AI: NVIDIA!<\/span><\/h2>\n
\n
\n

This blog will briefly introduce and compare the A100, H100, and H200 GPUs. Consider it a short guide on which GPU should be preferred for which work. You will also be able to find important information, such as how many GPUs companies need for their Large Language Models (LLMs) as well as their energy consumption. Although Nvidia is the choice of many companies and researchers when developing and selling the technology, I will also touch on the supply-demand imbalance.<\/p>\n

\n<\/figure><\/div><\/div><\/section>\n\n\n\n
\"Nvidia
Image Source: NVIDIA<\/a><\/figcaption><\/figure>\n\n\n\n

A100\u200a\u2014\u200aThe Revolution in High-Performance Computing<\/h4>\n\n\n\n

The A100 is the pioneer of NVIDIA\u2019s Ampere architecture and emerged as a GPU that redefined computing capability when it was introduced in the first half of 2020. The A100 has significantly improved, especially compared to its previous series, the Volta. These improvements have enabled it to quickly become the hardware of choice for researchers working on artificial intelligence (AI) projects, such as LLMs.<\/p>\n\n\n\n

\n<\/figure>\n\n\n\n
\"Nvidia
Up to 6X Higher Out-of-the-Box Performance \u200bwith TF32 for AI Training<\/a><\/figcaption><\/figure>\n\n\n\n

A100 has 6,912 CUDA cores, 432 tensor cores, and 40\u201380 GB of high bandwidth (HBM2). Third-generation Tensor Cores have accelerated AI tasks, leading to breakthroughs in image recognition, natural language processing, and speech recognition.<\/p>\n\n\n\n

H100\u200a\u2014\u200aPerformance and Optimization for Generative AI<\/h4>\n\n\n\n

The fourth-generation Tensor Core H100 of the Hopper architecture family introduced NVIDIA\u2019s commitment to innovation.<\/p>\n\n\n\n

\n<\/figure>\n\n\n\n
\"Nvidia
Up to 4X Higher AI Training on GPT-3<\/a><\/figcaption><\/figure>\n\n\n\n

The H100 is equipped with 18,432 CUDA cores, 640 Tensor Cores, 128 RT Cores, and 80 Streaming Multiprocessors (SMs), representing a new level in optimizing AI tasks. Thanks to NVLink interconnect technology, the H100 provides seamless and optimized integration from GPU to GPU. The H100 pioneered AI computing with its capability of machine learning and deep learning workloads. It offers single-precision performance up to 10.6 teraflops and dual-precision performance up to 5.3 teraflops.<\/p>\n\n\n\n

While discussing H100, it is also essential to mention TensorRT-LLM<\/a>. This open-source library was developed to improve and optimize the inference performance of the latest LLMs on the AI platform. It allows developers to try new LLMs without needing C++ or CUDA knowledge, enabling them to make high-performance and fast customizations.<\/p>\n\n\n\n

\n
\n
\n<\/div>\n
\n
\n

Want to learn how to build modern software with LLMs using the newest tools and techniques in the field? Check out this free LLMOps course<\/a> from industry expert Elvis Saravia of DAIR.AI!<\/p><\/blockquote>\n<\/div>\n<\/div>\n<\/section>\n\n\n\n

\n
\n
\n<\/div>\n
\n
\n

H200\u200a\u2014\u200aBet Up<\/h4>\n

NVIDIA is relentless in gaming and has continued this pace by introducing its latest gem, the H200, in November 2023<\/a>. Developed based on Hopper architecture, the H200 stands out with its groundbreaking HBM3e memory. With a mind-blowing 141GB memory capacity at 4.8 terabytes per second, it will set a new standard for processing massive datasets in generative AI and High-Performance Computing (HPC) workloads. H200, which is planned to be available for sale in the second quarter of 2024, promises a performance increase exceeding the A100.<\/p>\n

Comparison: A100 vs. H100 vs. H200<\/h3>\n

In the architecture race, the A100\u2019s 80 GB HBM2 memory competes with the H100\u2019s 80 GB HBM2 memory, while the H200\u2019s revolutionary HBM3 draws attention.<\/p>\n<\/div>\n

\n
\n<\/figure><\/div><\/div><\/section>\n\n\n\n
\"Nvidia
COMPARISON:<\/a> Results of GPT-J-6B A100 and H100 without and with TensorRT-LLM\u200a\u2014\u200aResults of Llama 2 70B, A100 and H100 without and with TensorRT-LLM<\/figcaption><\/figure>\n\n\n\n
\n

The inference performance comparison for GPT-J 6B and Llama2<\/strong> 70 B models shows that H100 is 4 times faster than A100. In particular, the H100 TensorRT-LLM version, which ensures optimal use of the hardware for LLM projects, exhibits 8 times higher inference performance than the A100. Although a performance difference is also observed for Meta\u2019s Llama2 model, the performance increase rate between A100 and H100 decreases as the model parameters increase.<\/p>\n

Power Efficiency: Fine Balances<\/h4>\n

Keeping Total Cost of Ownership (TCO) and energy consumption as low as possible is critical, especially for AI and LLM developers. Because these costs can be much greater than other costs, such as equipment, human resources, and connectivity needed for the rest of the project.<\/p>\n<\/div>\n\n\n\n

\n
\n<\/figure><\/div>\n\n\n\n
\"\"\/
COMPARISON:<\/a> A100 vs H100 TCO and energy cost\u200a\u2014\u200aTCO and energy cost benefits of A100<\/figcaption><\/figure>\n\n\n\n
\n

With its advanced architecture, H200 aims to establish a delicate balance between performance and power consumption and is expected to open new horizons for efficient AI computing. We will observe the developments on this issue over time.<\/p>\n

Which One to Choose?<\/h4>\n

Choosing the proper GPU depends entirely on the need, and only some GPUs may be suitable for some scenarios. The A100 still delivers strong performance on intensive AI tasks and deep learning. A more budget-friendly option, the H100 can be preferred for graphics-intensive tasks. The H100\u2019s optimizations, such as TensorRT-LLM and NVLink, show that it surpasses the A100, especially in the LLM area. The newly arrived H200 has an assertive position, especially in productive AI research and product development, with the expected performance increase and efficiency.<\/p>\n

Below, 8 different A100 hardware configurations are compared for the same Natural Language Processing (NLP) inference. Although multiplying 1 H100 by 8 with linear scaling does not give the exact result, it appears to exceed the performance of other A100 experiments. This is similar to NVIDIA\u2019s official posts. Having 8 H100s results in more than double the inference queries per second (~60,000 queries\/sec) of the Netrix-X660G45L, achieving ~27,000 queries\/sec in NLP compared to the Netrix-X660G45L (8x A100-SXM4\u201380GB, TensorRT). sec) is estimated to be possible.<\/p>\n

\n<\/figure><\/div>\n\n\n\n
\"\"\/
Comparison of A100 and H100 for NLP<\/a><\/figcaption><\/figure>\n\n\n\n

This time, instead of NLP, the (online) inference results per second for the recommendation engine application were compared similarly. It has been observed that having 8 H100s will perform 2.5 million recommendation engine inference queries per second while having 8 A100s is lower than the first two configurations. Both are estimated to perform 2.6 million inference queries per second. Solution #1 is the same Nettrix-X660G45L (8x A100-SXM(4)-80GB, TensorRT) and recommendation engine inference solution #2 is Inspur-NF5688M6 (8x A100-SXM(4)-80GB, TensorRT).<\/p>\n\n\n\n

\n<\/figure>\n\n\n\n
\"\"\/
Comparison A100 and H100 for Recommendation Engine<\/a><\/figcaption><\/figure>\n\n\n\n

According to a comparison made by NVIDIA, For 16-bit inference, H100 is about 3.5 times<\/a> faster, and for 16-bit training, H100 is about 2.3 times <\/a>faster.<\/p>\n\n\n\n

\n<\/figure>\n\n\n\n
\"\"\/
COMPARISON:<\/a> A100, H100, and H100+NVLink Results on different samples for High-Performance Computing, AI Inference, and AI Training<\/figcaption><\/figure>\n\n\n\n

A100 Usage Scenarios<\/strong><\/h4>\n\n\n\n