This paper examines how low-level programming, particularly in C/CUDA, can significantly impact kernel activity on GPU/TPU architectures, resulting in more efficient and environmentally conscious AI inference. With modern AI workloads consuming extensive computational and environmental resources, this investigation highlights how direct kernel control using C can improve memory access, computation throughput, and energy efficiency. We bridge the complex hardware-level theory and practical CUDA examples to present an accessible roadmap for optimizing AI workloads.