Recent works have shown that modelling raw waveform directly from text in an end-to-end (E2E) fashion produces morenatural-sounding speech than traditional neural text-to-speech(TTS) systems based on a cascade or two-stage approach. However, current E2E state-of-the-art models are computationallycomplex and memory-consuming, making them unsuitable forreal-time offline on-device applications in low-resource scenarios. To address this issue, we propose a Lightweight E2E-TTS(LE2E) model that generates high-quality speech requiring minimal computational resources. We evaluate the proposed modelon the LJSpeech dataset and show that it achieves state-of-the-art performance while being up to 90% smaller in terms ofmodel parameters and 10× faster in real-time-factor. Furthermore, we demonstrate that the proposed E2E training paradigmachieves better quality compared to an equivalent architecturetrained in a two-stage approach. Our results suggest that LE2Eis a promising approach for developing real-time, high quality,low-resource TTS applications for on-device applications.