Unified Static-Dynamic Pruning for Efficient LLM Inference

2026-07-24 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, AI Hardware Optimization · Depth: Expert, quick

Summary

SPDP is a unified sparse-inference framework designed to enhance large language model (LLM) inference efficiency on GPUs by addressing computational and memory bottlenecks during autoregressive decoding. It integrates unstructured static pruning (SP) with input-adaptive dynamic pruning (DP), overcoming limitations of standalone methods. SPDP co-designs a Tiled-Column-wise Bitmap Compressed (Tiled-CBC) format alongside two specialized GPU kernels: a CUDA-core spMspV kernel featuring Hybrid Activation-aware Dynamic Shared-Memory Bitmap Decoding (HAD-SMBD) for fine-grained activation skipping, and a Tensor-Core SpMM kernel optimized for prefill computation. This joint format-kernel approach harmonizes static and dynamic sparsity, ensuring bandwidth-efficient memory access and high compute intensity. Evaluations show SPDP achieves an average speedup of 1.24x-1.37x, reaching up to 2.51x, compared to sparse frameworks like SpInfer, while maintaining perplexity with up to 25% higher sparsity.

Key takeaway

For ML Engineers or AI Hardware Engineers optimizing large language model inference, if you are encountering significant computational and memory bottlenecks, SPDP demonstrates that unifying static and dynamic pruning can deliver substantial performance improvements. You should investigate co-designed sparse inference frameworks that integrate custom data formats and GPU kernels, as this approach achieved 1.24x-1.37x average speedup and up to 25% higher sparsity while maintaining model quality. This changes the decision to pursue only static or dynamic pruning in isolation.

Key insights

SPDP unifies static and dynamic pruning with co-designed GPU kernels and data formats for efficient LLM inference.

Principles

• Co-design data formats and GPU kernels for sparse inference.
• Harmonize static and dynamic sparsity for efficiency.

Method

SPDP employs a Tiled-Column-wise Bitmap Compressed (Tiled-CBC) format and two GPU kernels: a CUDA-core spMspV with HAD-SMBD for activation skipping, and a Tensor-Core SpMM for prefill.

Topics

• LLM Inference Optimization
• Sparse Inference
• Static Pruning
• Dynamic Pruning
• GPU Kernels
• Data Formats

Best for: AI Engineer, NLP Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, AI Hardware Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.