In this work, the authors propose a theoretical hybrid microservices architecture with on-device processing and cloud-based computation for real-time assistance in smart visually impaired glasses. The architecture integrates various functionalities, each containerized and deployed either locally or in the cloud, according to their computational requirements. Specifically, the pipeline includes a transformer-based Large Language Model (LLM) service, a barcode-scanning module, an expiration-date detection microservice, and an advanced performance optimization layer. The design references our previous four works, which explored domain-specific LLM architectures in the cloud, LLM integration in barcode scanning, LLM integration in expiration-date scanning, and performance trade-offs for LLMs on different hardware systems. By unifying these components into a hybrid model, our approach aims to deliver low-latency responses for core tasks on the smart glasses and robust, large-scale computations in the cloud. We highlight container orchestration, service-level optimizations, and security concerns, proposing an architecture adaptable to future IoT developments.