Xgboost Parameter Tuning Are there methods to tune and train an xgboost model in an optimized time - when I tune paramaters and train the model it takes around 12 hours to execute?

I would like to run the solution 100 times with 100 seeds; my machine has 8 GB RAM and I can't buy a cloud solution.
Typically XGBoost does not require many parameters to be tuned to get good performances. 

I would start with optimizing the n_estimators, max_depth and min_child_weight parameters only. This should already bring you close enough.

Another thing you can do to speed-up the process is to prefer Random Search over Grid Search, since in most cases is as or more effective.

You could also have a look at the LightGBM implementation, which is faster (and needs less memory) than XGBoost.