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Accurate and effective rapid detection in remote sensing images play an extremely important role in natural disasters, landslides, flooding problems and military defense. Specially in earthquake damage detection, time critical tasks such as performing the damage assessment or providing immediate delivery of relief assistance require responses for swift decisions. To minimize response time, this paper proposes the portability and acceleration of the inferences process on a deep convolution neural network (CNN) model to detect rapid earthquake damage on Very High Resolution (VHR) remote sensing data using the Intel OpenVINO toolkit. This model, based on a previous work, has been optimized for High Performance Computing techniques to minimize processing times. Along with the immediate responses, the correctness of the result is also very crucial parameter for such type of applications. For analysis and performance, we use the Geoeye-1 VHR disaster images of the Haiti earthquake occurred in year 2010. Experimental results show that the optimized model provides good accuracy for damage detection with significant execution speed on CPU+GPU using mixed precision technique compared to other previous work. Moreover, OpenVINO toolkit presents satisfactory performance in the inference stage compared to other toolkits like TensorFlow serving using asynchronous inference mode executed on an Intel Xeon processor and Intel Movidius NCS as co-processor.