Introduction
NVIDIA stands as a dominant force in the technological landscape, particularly in the realm of GPU technology. Renowned for their GPUs that efficiently handle demanding computing tasks such as artificial intelligence (AI), high-performance computing (HPC), and graphics workloads, NVIDIA offers a diverse array of options. Among these, the A100, H100, and H200 stand out as three of the most powerful GPUs.
Choosing the best GPU depends on individual requirements, preferences, and budget considerations. This comprehensive comparison aims to assist users in making an informed decision in their pursuit of the ideal GPU.
NVIDIA GPU Legacy: A Brief Introduction
NVIDIA’s legacy in GPU technology is marked by several generations of GPUs, each featuring distinct capabilities. Notable among these are the Tesla V100, introduced in 2017 with revolutionary Tensor Cores for deep learning and HPC workloads; the A100, powered by the Ampere architecture, setting new standards in 2020; and the H100, the latest addition launched in 2023, designed to elevate performance in AI, HPC, and graphics tasks.
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The Architecture: A100 vs H100 vs H200
A100’s Ampere Architecture
The A100 Tensor Core GPU, driven by the Ampere architecture, represents a leap forward in GPU technology. Key features include Third-Generation Tensor Cores, offering comprehensive support for deep learning and HPC, an advanced fabrication process on the TSMC 7nm N7 manufacturing process, Multi-Instance GPU (MIG) for partitioning into independent instances, and technologies like NVLink and NVSwitch for high-speed data transfer between GPUs.
H100’s Hopper Architecture
The H100 Tensor Core GPU, featuring the Hopper architecture, emerges as NVIDIA’s latest and most potent GPU. 
Boasting Fourth-Generation Tensor Cores, HBM3e Memory for advanced memory technology, Multi-GPU Fabric for optimized integration, and specialized cores like RT Cores and SMs, the H100 aims to deliver unparalleled acceleration for AI, HPC, and graphics workloads.
H200’s Hopper Architecture
The H200 Tensor Core GPU, an extension of the Hopper architecture, promises even higher performance and efficiency.
Equipped with Fifth-Generation Tensor Cores, HBM4 Memory for next-gen memory technology, Multi-GPU Fabric 2.0 for faster integration, and enhanced cores in the form of RT Cores 2.0 and SMs 2.0, the H200 stands as the ultimate GPU for AI, HPC, and graphics.
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Performance comparison: A100 vs H100 vs H200
The performance of A100, H100, and H200 GPUs is scrutinized across various tasks to provide a comprehensive overview.
1.AI Training Performance: AI training, a resource-intensive process, demands substantial computational power. The H200 GPU excels in this domain, offering 17 TFLOPS on GPT-3 and 23.6 TFLOPS on Llama 2. Comparatively, the H100 and A100 exhibit lower performances.
2.AI Inference Performance: AI inference, requiring speed and accuracy, sees the H200 GPU outshining with 7.7 TFLOPS on GPT-3 and 10.7 TFLOPS on Llama 2. Once again, the H100 and A100 trail behind.
3.HPC Performance: For HPC tasks, measuring the peak floating-point performance, the H200 GPU emerges as the leader with 62.5 TFLOPS on HPL and 4.5 TFLOPS on HPCG. The H100 and A100 lag behind in HPC performance.
4.Graphics Performance:In graphics, the H200 GPU maintains its supremacy with 118,368 in 3DMark and 720 FPS in Unigine. The H100 and A100, while formidable, fall short in comparison.
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AI Capabilities Unveiled: A100 vs H100 vs H200
The H200 GPU dominates generative AI, generating 32 images on DALL-E and 8 songs on Jukebox. The H100 and A100 exhibit lower capabilities in comparison.
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.
Power Efficiency Unveiled: A100 vs H100 vs H200
Power efficiency, a critical metric for operational costs and environmental impact, reveals the efficiency of each GPU.
The H200 GPU emerges as the most power-efficient, outperforming the A100 and H100 across various workloads, including AI training, AI inference, HPC, and graphics.
How Many Are Needed?
Similarly, the number of GPUs needed depends on the data type, size, and models used. As an example of how many of these GPU companies need, let’s look at the following examples for some major language models:
For GPT-4, OpenAI probably trained the model with an A100 GPU around 10–25k.
Meta has approximately 21k A100s, Tesla has around 7k, and Stability AI has about 5k.
Falcon-40B was trained with 384 A100s.
Inflection used 3.5k H100 for its GPT-3.5 equivalent model.
Conclusion
The A100, H100, and H200 GPUs present users with a spectrum of options, each catering to specific needs in terms of performance, AI capabilities, and power efficiency. The A100 is a versatile choice for flexible and scalable usage, while the H100 excels in handling the most demanding workloads. The H200, standing as the pinnacle, offers ultimate efficiency for the most complex tasks.
