Memory Requirements for Running LLM

Are there any rule of thumb calculations for determining memory requirement (as a function of number of model parameters) for an LLM model. I’m referring to a base model (no quantization) with full fine-tuning vs no fine-tuning that I would like to run inference on. Lets use Llama2 7B as an example. Below is what I’ve seen for pre-training, but not sure how this exactly translates over to loading in the pre-trained base model and working with it. The disconnect here for me is I’m unsure what components of the model exactly are getting stored in memory. Any help appreciated!
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https://huggingface.co/spaces/hf-accelerate/model-memory-usage is a tool.