For AI teams considering self-hosting Generative AI models, selecting the appropriate GPU is crucial, with NVIDIA being a leading choice for AI workloads due to its range of data center GPUs optimized for large-scale tasks. These GPUs, updated every few years, include notable models like the T4, L4, A100, H100, and the new B200, each designed to meet different performance and cost needs. NVIDIA's GPU architecture, named after renowned scientists, enhances performance with each generation, while its data center GPUs are distinct from the GeForce and RTX lines, which are aimed at gaming and professional visualization, respectively. When evaluating GPUs, memory capacity is often as critical as compute power, especially for long-context AI inference workloads. Although NVIDIA dominates the AI GPU market, AMD's MI-series offers competitive alternatives, albeit with less software support. Understanding these options can help teams balance performance and cost for effective AI deployments.