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cs.LG2026
SpenseGPT: Practical One-shot Pruning Enabling Sparse and Dense GEMMs for LLM Inference
Jaeseong Lee, Seung-won Hwang, Samyam Rajbhandari
Semi-structured 2:4 sparsity is widely supported by modern accelerators, providing up to a 2x theoretical speedup. However, its strict 50% sparsity constraint often causes non-negl…
cs.LG2024
STUN: Structured-Then-Unstructured Pruning for Scalable MoE Pruning
Jaeseong Lee, seung-won hwang, Aurick Qiao +3
Mixture-of-experts (MoEs) have been adopted for reducing inference costs by sparsely activating experts in Large language models (LLMs). Despite this reduction, the massive number…