Recently deep learning based on-device automatic speech recognition (ASR) shows breakthrough progress. However, in literature, there is no concrete work about integrating on-device ASR into mobile games as a voice user interface. The difficulties to deploy ASR into mobile games is that most game users want a quick responding voice command interface with no time delay. Therefore a need to design an on-device ASR system which costs minimal memory and CPU resources rises. To this end, we propose transformer based on-device ASR named MObile Neural voIce Command Assistant (MONICA) for mobile games. With MONICA, users could conduct game actions using voice commands only, such as "enter the monster dungeon", "start the auto-quest", "open the inventory" etc.To the best of our knowledge, this is the first work trying to resolve an on-device ASR task for mobile games at the service level. MONICA reduces the number of parameters in the neural network to 10% and speeds up the inference time by more than 5 times compared to the baseline transformer model while retaining minimal recognition accuracy degradation. We perform a web-based interactive live demonstration of MONICA as a voice user interface for an online chess game. Also, a demonstration video shows MONICA integrated into A3: Still Alive, which is a major game from Netmarble serviced in South Korea. MONICA will be on the service as a voice command interface for all A3 users very soon this year. Finally, we release a mobile application so that you could download and test the efficiency of MONICA on your mobile device.