NIRVANA: Structured Pruning Reimagined for Large Language Model Compression

While structured pruning presents a highly effective pathway for accelerating Large Language Model (LLM) inference, existing methods frequently suffer from significant performance degradation and demand computationally retraining to recover capabilities. To overcome these barriers, we present NIRVANA, a novel, hardware-aware structured pruning framework designed to preserve both zero-shot performance and the optimization landscape for downstream fine-tuning. Departing from traditional loss-based heuristics, our approach evaluates structural importance through a first-order function-space saliency inspired by the Neural Tangent Kernel (NTK), effectively safeguarding the model's critical training dynamics. To prevent structural collapse at high compression rates, we introduce a global unit-ranking strategy coupled with an analytically derived allocation mechanism, which optimally balances the pruning aggressiveness between attention heads and MLP neurons. Furthermore, we eliminate the instability typically associated with random data sampling by employing a lightweight, KL-divergence-driven calibration data selection process. Extensive evaluations across Llama3, Qwen, and T5 architectures demonstrate that NIRVANA consistently establishes new state-of-the-art results on different benchmarks, providing a theoretically sound and practical approach to LLM compression.