As computers and other personal devices have become increasingly prevalent, interest in conversational AI has grown due to its multitude of potential applications in a variety of situations. Each conversational AI framework is comprised of several more basic modules such as automatic speech recognition (ASR), and the models for these need to be lightweight in order to be effectively deployed on the edge, where most of the devices are smaller and have less memory and processing power. However, most state-of-the-art (SOTA) ASR models are extremely large -- they tend to have on the order of a few hundred million parameters. This makes them hard to deploy on a large scale given current limitations of devices on the edge. To tackle this problem, NVIDIA is releasing QuartzNet, a new end-to-end neural ASR model architecture based on Jasper that is smaller than all other competing models.