A new physics-inspired method prunes large language models by framing block removal as an Ising optimization problem. The approach uses techniques like simulated annealing to select which blocks to drop, aiming to cut model size and inference cost while keeping accuracy high. This offers developers a principled way to shrink models for deployment without extensive retraining.
Opening Kapyn…