Characterizing neural responses and behavior require large scale simulation of brain circuits. Spatio-temporal information processing in large scale neural simulations often require compromises between computing resources and realistic details to be represented. In this work, we compared the implementations of point neuron models and biophysically detailed neuron models on serial and parallel hardware. GPGPU like architectures provide improved run time performance for multi compartmental Hodgkin-Huxley (HH) type neurons in a computationally cost effective manner. Single compartmental Adaptive Exponential Integrate and Fire (AdEx) model implementations, both in CPU and GPU outperformed embarrassingly parallel implementation of multi compartmental HH neurons. Run time gain of CPU implementation of AdEx cluster was approximately 10 fold compared to the GPU implementation of 10-compartmental HH neurons. GPU run time gain for Adex against GPU run time gain for HH was around 35 fold. The results suggested that careful selection of the neural model, capable enough to represent the level of details expected, is a significant parameter for large scale neural simulations.