Post-training pruning cuts the inference cost of large language models, yet it faces a trade-off: simple heuristics that zero weights are fast but degrade accuracy, while principled joint optimization methods recover accuracy but are computationally infeasible at modern scale. One-shot methods such as SparseGPT sit in between and apply efficient but approximate weight updates. To close this gap, we introduce OPTIMA, a practical one-shot post-training pruning method that balances accuracy and scalability. OPTIMA casts layer-wise weight reconstruction after mask selection as independent, column-wise quadratic programs (QPs) that share one Hessian per weight matrix. Solving these QPs yields, at solver convergence, the per-column minimizer of the layer-wise reconstruction objective under the shared Hessian, and the shared Hessian makes the QPs easy to batch on accelerators. We implement an accelerator-friendly QP solver that accumulates one Hessian per weight matrix and solves many small QPs in parallel with a convergence certificate per column, enabling one-shot post-training pruning at scale on a single accelerator without fine-tuning. OPTIMA integrates with existing mask selectors and improves average zero-shot accuracy in 40 of 49 (model, mask selector, sparsity) settings across the LLaMA and Gemma families, with mean gains of +0.61, +0.56, and +1.01 points at 50% unstructured, 2:4, and 60% unstructured sparsity, and up to +3.97 points at 60% sparsity, while matching ADMM-based exact reconstruction on its own masks. On an NVIDIA H100, OPTIMA prunes an 8B-parameter transformer end-to-end in 40 hours and 60 GB of peak GPU memory. This shows that principled column-wise QP reconstruction is a practical alternative to heuristic weight updates in one-shot post-training pruning.

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