Why it is taking more training time on one machine comparatively to similar another?
In machine learning, while your choice of inputs and hyper-parameters do matter, most of the time convergence ultimately comes down to how lucky you are. If you're lucky you might reach optimum in a very short period of time, whereas if you're unlucky you might get stuck in a local optimum with a huge error and get stuck in a very long loop. 

That's one reason; another is possibly that if you're using TensorFlow and you installed the version for non-GPU computers, and your friends installed the version for computers with GPUs.