Model Distillation

Introduction Real‑time inference has become the linchpin of modern AI‑driven applications—from autonomous vehicles and industrial robotics to augmented reality and smart‑city monitoring. As these workloads scale, a single data‑center GPU can no longer satisfy the stringent latency, bandwidth, and privacy requirements of every use case. The answer lies in distributed AI systems that blend powerful cloud resources with edge computing nodes located close to the data source. However, edge devices are typically resource‑constrained, making it essential to shrink model size and computational complexity without sacrificing accuracy. This is where model distillation—the process of transferring knowledge from a large “teacher” model to a compact “student” model—plays a pivotal role. ...