NVIDIA has traditionally dominated the GPU market for AI applications, but AMD is emerging as a strong competitor, particularly in high-performance computing (HPC) and enterprise AI with its MI-series GPU accelerators. Although NVIDIA's CUDA platform is the industry standard for GPU programming, AMD's ROCm software stack is gaining traction, especially with the introduction of the MI300X and other CDNA-based GPUs. AMD's GPU lineup is categorized into Radeon for gaming, Radeon Pro for professional creators, and the Instinct MI-series for data centers, with the latter focusing on raw compute performance and memory bandwidth. The MI-series, including the MI300X and MI350, are positioned as cost-effective alternatives to NVIDIA's offerings, providing more memory at a lower cost, which is beneficial for memory-intensive AI workloads. While AMD's software ecosystem is still developing compared to NVIDIA's, AMD's GPUs are increasingly considered viable options for AI infrastructure, especially when supply or pricing of NVIDIA products is a concern. The decision between AMD and NVIDIA ultimately depends on specific workload requirements and the ability to adapt to evolving models and frameworks.