The Evolution of NVIDIA AI Accelerators and Their Impact on Artificial Intelligence Model Performance: A Comparative Analysis of NVIDIA A100, H100, H200, and Blackwell Architectures

Abstract

The fast development of artificial intelligence (AI), especially in deep learning, generative AI, and large language models (LLMs), has increased the need for powerful computing hardware. GPUs and special AI accelerators have become an important part of modern AI systems. NVIDIA has been one of the main companies contributing to this development through different GPU architectures, such as Ampere, Hopper, and Blackwell. This paper studies the development of NVIDIA AI accelerators and their effect on the performance of modern AI models, focusing on the NVIDIA A100, H100, H200, and Blackwell B200 architectures. The study looks at improvements in tensor processing, memory capacity and bandwidth, numerical precision, interconnect technologies, and other technologies designed for AI workloads. The comparison shows that improvements in GPU architecture have helped reduce the time needed for model training and inference and have made it possible to work with larger and more complex AI models. The move from A100 to H100 introduced important technologies such as fourth-generation Tensor Cores and the Transformer Engine. The H200 mainly improved memory capacity and bandwidth, which makes it more suitable for large language models. The Blackwell architecture provides further improvements through fifth-generation Tensor Cores, lower-precision computing, improved memory systems, and faster communication between chips. Results from MLPerf also show clear improvements in AI training and inference performance across these generations. However, the performance of an AI model does not depend only on the processing power of the GPU. Memory bandwidth, model design, software optimization, communication between GPUs, and energy efficiency also have an important effect. The paper concludes that the development of NVIDIA AI accelerators and the development of AI models are strongly connected. Improvements in hardware have helped researchers and companies build larger and more capable AI models, while the increasing size and complexity of these models have created a need for more advanced AI hardware.