The ongoing competition between AMD's MI300X and NVIDIA's H100 GPUs highlights the evolving landscape of AI inference hardware, with each offering distinct advantages. The MI300X excels in memory capacity and bandwidth, making it suitable for memory-intensive tasks like large language models (LLMs) that require substantial resources and can benefit from single-GPU setups to reduce latency and boost throughput. In contrast, the H100 offers lower latency and a mature CUDA software ecosystem, making it ideal for compute-bound tasks and medium batch sizes. The article also emphasizes the importance of software maturity, with NVIDIA's CUDA leading in stability, while AMD's ROCm continues to develop. Platforms like Clarifai provide a unified API to abstract hardware differences, enabling seamless deployment across various GPUs. As the market prepares for next-generation GPUs like AMD's MI350/MI355X and NVIDIA's Blackwell, organizations are advised to consider their specific workload requirements, potential energy efficiency, and future-proofing strategies to make informed decisions.