The NVIDIA H100 Tensor Core GPU, based on the Hopper architecture, is a key player in the current generative AI surge, providing the computational power needed for training large language models and enabling real-time inference. Introduced in late 2022, it features innovations like a Transformer Engine, fourth-generation Tensor Cores, and Multi-Instance GPU (MIG) slicing, which allow multiple AI workloads to run concurrently. Despite its high cost, the H100 is favored for its significant performance improvements over its predecessor, the A100, making it essential for AI/ML engineers and infrastructure teams aiming to build cutting-edge AI systems. The guide explores the H100's specifications, including its compute efficiency, memory bandwidth, and power requirements, while also discussing its comparison with alternatives like the A100, H200, and AMD's MI300. Additionally, it highlights the importance of understanding the total cost of ownership, which includes power, cooling, networking, and software expenses, and suggests using orchestration platforms like Clarifai's Compute Orchestration to enhance uptime and cost efficiency in AI deployments.