Motivating Next-Gen Accelerators with Flexible (N:M) Activation Sparsity via Benchmarking Lightweight Post-Training Sparsification Approaches

Researchers present a comprehensive analysis of post-training N:M activation pruning techniques for large language models, demonstrating that activation pruning preserves generative capabilities better than weight pruning. The study establishes hardware-friendly baselines and explores sparsity patterns beyond NVIDIA's standard 2:4, with 8:16 patterns showing superior performance while maintaining implementation feasibility.