What is the difference between H100 and A100 GPUs?

The NVIDIA H100 GPU, built on the Hopper architecture, surpasses the A100 GPU (Ampere architecture) in performance, efficiency, and AI capabilities. While the A100 is well-established for high-performance AI and data center tasks, the H100 introduces advanced features like fourth-generation Tensor Cores, significantly increased CUDA cores, higher memory bandwidth with HBM3, and enhanced power efficiency, making it suitable for the most demanding AI and HPC workloads.

Introduction to GPUs in AI

Graphics Processing Units (GPUs) have become essential in modern AI and high-performance computing (HPC) due to their massively parallel processing capabilities. The evolution of GPU architectures directly impacts their ability to handle complex AI models, training data, and inference tasks efficiently and at scale.​

Architecture Comparison

The A100 GPU, launched in 2020, is based on the Ampere architecture. It features 6,912 CUDA cores, 432 third-generation Tensor Cores, and support for high-bandwidth HBM2e memory (40 GB or 80 GB), offering a high level of performance for AI, data analytics, and scientific computing.​

In contrast, the H100 GPU, introduced later, is built on the Hopper architecture. It boasts 18,432 CUDA cores, 640 fourth-generation Tensor Cores, and 80 GB of HBM3 memory with a bandwidth of up to 3.35 TB/s. The Hopper architecture emphasizes AI workloads, integrating the Transformer Engine, FP8 support, and more efficient data handling for larger models and faster training times.​

Performance Benchmarks

Performance comparisons consistently show the H100 outperforms the A100 across AI training, inference, and HPC tasks. For example:​

The H100’s fourth-generation Tensor Cores are up to 6x faster than those in the A100.

It provides over 2,000 TFLOPS of FP8 performance vs. approximately 312 TFLOPS from A100.

Tasks like large language model training, where speed and efficiency are critical, see substantial improvements with the H100.

Power efficiency also favors the H100, which delivers about 60% better efficiency per watt than the A100 in benchmarks like MLPerf.​

Power Efficiency and Scalability

Despite consuming more power (up to 700W), the H100’s increased performance per watt justifies its use in data centers requiring maximal throughput. Features like PCIe Gen5 support and improved NVLink 4.0 enable better multi-GPU scalability, critical for large-scale AI models and scientific simulations.​

Use Cases and Suitability

H100 is especially advantageous for enterprises aiming to accelerate model training, inference, and complex scientific computations, whereas the A100 remains a robust and cost-effective choice for many existing applications.

Pricing and Cost-Performance

The H100’s advanced architecture and performance lead to higher costs; however, the price gap has reduced recently, making the performance benefits more accessible for enterprise investments. When considering total cost of ownership, the efficiency gains often offset the initial expenditure.​

Why Choose Cyfuture Cloud

Cyfuture Cloud leverages the latest NVIDIA GPU technology, including H100 and A100, in optimized cloud configurations. Our solutions empower your organization to scale AI workloads effortlessly, minimize time-to-market, and maximize investment value. Partner with us for the most advanced GPU infrastructure tailored to your specific needs.

Conclusion

The NVIDIA H100 GPU represents the next leap in GPU technology, surpassing the A100 in core count, memory bandwidth, AI-specific features, and overall performance. While the A100 remains a solid choice for many applications, organizations aiming for cutting-edge AI and HPC workloads should prioritize H100 for its superior capabilities and efficiency.​