State-of-the-art deep learning models require significant GPU memory, which is a limiting factor for many GPUs. The RTX 8000 and RTX 6000 are the top options for training state-of-the-art networks due to their high VRAM capacity. However, language models tend to be more memory-intensive than image models, with the Transformer Big model requiring significantly more VRAM. GPUs with higher VRAM, such as the RTX 2080 Ti and Quadro RTX 8000, offer better performance by allowing larger batch sizes. The RTX 2060 is not suitable for training state-of-the-art models due to its limited VRAM capacity. Language models benefit from larger GPU memory than image models, with language models requiring proportionally larger batch sizes on GPUs with higher VRAM. The recommended GPUs for deep learning depend on budget and requirements, ranging from the RTX 2070 or 2080 for serious deep learning enthusiasts to the Quadro RTX 8000 for future-proofing and large-scale research.