With the development of Big Data technology GPU cluster as a high efficiency parallel system applies into the Large-scale data computing field.Energy is a significant computation resource.So power consumption optimization control and capping in real-time becomes a challenge issue.The Model Prediction Control strategy is introduced and a Multi-Input Multi-Output controller is built by using a closed loop feedback principle from the whole cluster perspective.Power consumption status is changed by scaling frequency and adjusting active stream multi-processors.Then the feedback and the periodic optimization mechanisms can predict the control behaviors in the future control cycles.This achieves the goal that reduces redundancy energy.The results demonstrate that the proposed model has more accuracy and comsumes less energy than the others.And it has better control stability.So it has better adaptability and obvious advantage in the Large-scale data real-time computing.