The NVIDIA H100 GPU and H200 GPU are powerful accelerators designed for AI, deep learning, and high-performance computing workloads. While the H100 set a new standard for Transformer-based models with its exceptional FP16 and FP8 throughput, the H200 builds on that foundation with architectural enhancements, higher efficiency, and expanded AI capabilities. The H200 gpu introduces improved tensor cores, faster memory bandwidth, and optimized inference performance, making it more suitable for evolving large language models and mixed-precision workloads. Both GPUs leverage NVIDIA’s Hopper architecture, but the H200 targets broader generative AI tasks with better scaling across data centers and enhanced support for complex model training and real-time inference. Choosing between them depends on your specific use case: the H100 gpu remains strong for established AI pipelines, while the H200 gpu offers future-ready performance gains for next-gen AI and deep learning projects. For a detailed comparison of specs, features, and performance differences, read the full analysis at the link.