The emergence of Edge AI has opened new possibilities for deploying machine learning models on power-constrained devices, enabling real-time, private, and efficient inference directly on embedded platforms. Among these tasks, image captioning remains particularly challenging due to the computational demands of vision-language (VL) models, which typically rely on large-scale transformer architectures. This thesis addresses the gap between high-performing generative captioning and efficient edge deployment by adapting Microsoft's GIT-Base model for execution on the Hailo-8 Neural Processing Unit (NPU). We present a full pipeline for real-time image-to-text generation on an embedded system based on the i.MX8M Plus SoC and Hailo-8 NPU. To make the GIT-Base model compatible with the stringent constraints of edge hardware, we optimized both the encoder and decoder components and thoroughly reengineered the decoder architecture.