Unified Static-Dynamic Pruning for Efficient LLM Inference
The increasing deployment of large language models (LLMs) has magnified the computational and memory bottlenecks of autoregressive decoding, where low compute intensity and bandwidth-bound kernels dominate inference cost. Weight pruning offers a promising remedy, but existing methods remain confined to either static pruning (SP), which permanently removes redundant weights but lacks adaptivity, or dynamic pruning (DP), which adapts to input sparsity but introduces runtime irregularity. This paper presents SPDP, a unified sparse-inference framework that integrates unstructured SP with input-adaptive DP for efficient LLM inference on GPUs. SPDP co-designs a new Tiled-Column-wise Bitmap Comp...
| Authors | Jinhyeok Kim, Yejoon Lee, Jaeyoung Do |
|---|---|
| Primary category | cs.DC |
| Published | 24 July 2026 |
| Last updated at source | 9 August 2026 |
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