How I explored INT8 quantization, biological graphs, and CPU-only inference using PyTorch Geometric. Healthcare AI is often discussed in terms of massive cloud infrastructure and expensive GPUs. But many real-world systems do not operate inside large datacenters. Small clinics, portable medical systems, rural deployments, and edge diagnostic devices frequently depend on: low-power CPUs limited memory unstable connectivity compact hardware environments That raises an important engineering question: Can graph neural networks become smaller and more deployable without completely losing their predictive behavior? This project explores that question using biological graph data, Graph Neural Networks (GNNs), and manual INT8 quantization.…