NVIDIA's GPUs, particularly the A100 and H100 models, have become essential components in meeting the growing complexity of AI and high-performance computing (HPC) workloads. The A100, launched in 2020, introduced the Ampere architecture with features like the third-generation Tensor Cores and Multi-Instance GPU technology, which improved flexibility and performance for AI tasks. In 2022, the H100 with the Hopper architecture further advanced these capabilities, offering significant enhancements in performance, particularly for transformer-based AI workloads, through its fourth-generation Tensor Cores and Transformer Engine with FP8 precision. The H100 also includes features like DPX instructions, Distributed Shared Memory, and Thread Block Clusters, delivering up to six times the performance of the A100 in certain applications. While the A100 remains a cost-effective choice for tasks where latency is not a priority, the H100 is designed for large-scale AI and HPC applications that require high performance and low latency, making it suitable for real-time applications and large-scale model training. Users can deploy these GPUs across various cloud providers, enabling flexibility and avoiding vendor lock-in, with resources available for pricing comparisons and expert support.