Assess students' ability to apply model compression techniques—magnitude/structured pruning, post‑training and quantization‑aware quantization, and knowledge distillation—to transformer-based NLP models to achieve real-time inference on edge devices; include designing hardware-aware compression pipelines, selecting hyperparameters, evaluating trade-offs among latency, memory, energy, and accuracy, and justifying deployment choices using benchmark metrics and toolchains.