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Supercomputing GPU-Servers
Nvidia A100 & Nvidia H100

In this case study, we focus on the deployment and comparison of two leading supercomputing GPUs in a data center environment: the Nvidia A100 80GB and the Nvidia H100.

Dedicated GPU server

8x NVIDIA A100 80GB Tensor Cores

✓ GPU Memory 640GB

Dual AMD Rome 7742 – 128 cores – 2.25 GHz (base) – 3.4 GHz (max boost)

✓ Memory Potion 2 TB

Ethernet

✓ Storage Chambers:

OS: 2x 1.92TB M.2 NVME

Internal: 30TB (8x 3.84 TB) U.2 NVMe

Dedicated GPU server

8x NVIDIA H100 Tensor Core SXM5

GPU Memory 640GB

 Dual 56-core 4th Gen Intel® Xeon® Scalable processors

 2TB of system memory

✓ Storage Chambers:

2x 1.9TB NVMe M.2 for OS

8x 3.84TB NVMe U.2 for internal storage

Overview

Nvidia A100

The Nvidia A100 80GB GPU is designed to accelerate AI and HPC at every scale. It’s highly suited for modern data centers, capable of handling varied workloads efficiently.

Key Features

Enterprise PCIe 40GB/80GB

The A100 comes in two variants, 40GB and 80GB, and is optimized for AI, HPC, and data analytics workloads. Its versatility and power make it a cornerstone in contemporary high-performance computing environments.

Comparison - Nvidia A100

Architecture

The A100 uses the earlier Ampere architecture, which is highly effective for deep learning and AI tasks.

Performance

Though slightly older than the H100, the A100 remains one of the best GPUs for deep learning and various high-performance tasks.

nvidia-a100-gpu

Memory

Comes with different VRAM options (e.g., 40GB or 80GB), making it versatile for various applications.

Use Cases

Suited for data centers, AI research, and HPC tasks, offering a balance of performance and power efficiency.

Overview

Nvidia H100

The H100, part of Nvidia’s HGX AI Supercomputing Platform, is a more recent and advanced GPU primarily for executing data center and edge compute workloads, particularly in AI and HPC.

Key Features

Enterprise PCIe-4

The H100 offers a significant performance boost, with up to 7x higher efficiency compared to its predecessors. It’s especially powerful when deployed at scale in data centers.

Comparison - Nvidia H100

Architecture

The H100 is based on Nvidia’s new Hopper GPU architecture, designed for high-performance computing and AI workloads.

Performance

It excels in deep learning and AI applications, offering significant performance improvements over its predecessors.

Memory

The H100 typically features higher VRAM, enhancing its capability for more demanding tasks.

Use Cases

Ideal for data centers and professional applications requiring high computational power and efficiency.

Installation and Deployment in a Data Center

Nvidia H100 & A100 GPUs

Scenario

Installing these GPUs in a data center involves planning for scalability, workload management, and ensuring optimal interconnectivity for maximum performance.

Nvidia H100 & A100 GPUs

Example

In our data center, we installed 8 Nvidia H100 GPUs connected with NVLink and NVSwitch, optimizing them for TensorFlow and PyTorch workloads. Similarly, we deployed A100 80GB GPUs in different server clusters, balancing the workload based on the computational requirements.

Nvidia H100 & A100 GPUs

Outcome

The H100 servers showed remarkable efficiency in handling deep learning and AI-centric tasks, while the A100 servers provided robust support for a variety of AI, HPC, and data analytics applications.

Conclusion

Both GPUs are top-tier choices in their respective fields. The H100, with its newer architecture and higher VRAM, is more suited for extremely demanding and cutting-edge tasks. The A100, meanwhile, provides robust performance for a wide range of high-performance applications, especially where power efficiency is a concern.

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