Deploying state-of-the-art vision models, such as Vision Transformers (ViT), MLP-Mixers, and ResNet-50, on resource-constrained edge devices remains a significant challenge due to their high memory footprint and computational requirements. This project aims to address this by investigating layer-wise quantization strategies for these architectures, with a specific focus on remote sensing applications using the EuroSAT dataset. By employing Quantization-Aware Training (QAT) via the Brevitas library, we will explore how mixed-precision quantization can compress these models while preserving their predictive accuracy. The outcome of this research will be highly optimized, low-bitwidth models exported in QONNX format, paving the way for efficient, hardware-accelerated Earth observation systems on edge devices.