When the input values are roughly in the range (-1, 1) the gradients are more meaningful and training goes much faster.
In vague theory almost any model should be able to adjust to the data and make decent predictions, but it often doesn’t work that well. The building blocks of neural nets are designed to work best for inputs roughly in the range (-1, 1). Most building blocks work best when the weights are initialised carefully. Now if your inputs are in the range (80, 120), then your model will first have to learn to adjust to the data, and when it has finished adjusting then there is no guarantee that the model weights are still in a state conducive to further learning.
More info on data normalisation

https://www.coursera.org/learn/deep-neural-network/lecture/lXv6U/normalizing-inputs 6
https://becominghuman.ai/image-data-pre-processing-for-neural-networks-498289068258 3
https://ahmedhanibrahim.wordpress.com/2014/10/10/data-normalization-and-standardization-for-neural-networks-output-classification/ 8
and much more just a few google searches away. You will need to be fairly specific in your queries, for example “data normalization” can mean many things in many contexts, “data normalisation for neural networks” is a much better query.