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.
✓ 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
✓ 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
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.
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.
The A100 uses the earlier Ampere architecture, which is highly effective for deep learning and AI tasks.
Though slightly older than the H100, the A100 remains one of the best GPUs for deep learning and various high-performance tasks.
Comes with different VRAM options (e.g., 40GB or 80GB), making it versatile for various applications.
Suited for data centers, AI research, and HPC tasks, offering a balance of performance and power efficiency.
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.
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.
The H100 is based on Nvidia’s new Hopper GPU architecture, designed for high-performance computing and AI workloads.
It excels in deep learning and AI applications, offering significant performance improvements over its predecessors.
The H100 typically features higher VRAM, enhancing its capability for more demanding tasks.
Ideal for data centers and professional applications requiring high computational power and efficiency.
Installing these GPUs in a data center involves planning for scalability, workload management, and ensuring optimal interconnectivity for maximum performance.
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.
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.
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.
