arXiv:2607.07557v1 Announce Type: new Abstract: One-shot pruning methods like Wanda and SparseGPT apply the same sparsity ratio to every layer of a transformer, ignoring known variation in layer importance. We propose PALS (Percentile-Aware Layerwise Sparsity), which adjusts per-layer sparsity based on the 99th percentile of activation magnitudes, bounded to $\pm 5\%$ around the target ratio. On LLaMA-2-7B at 50\% sparsity, PALS achieves 10.96 WikiText-2 perplexity versus 12.92 for uniform Wanda (mean over 9 runs, $p < 0.001$). The benefit is architecture-dependent: LLaMA-3-8B shows marginal g
The continuous push for more efficient and performant large language models (LLMs) drives innovation in pruning techniques, addressing computational and energy constraints.
Improved pruning methods like PALS can significantly reduce the computational resources and memory required to run LLMs, making them more accessible and deployable for a wider range of applications and reducing associated costs.
The ability to achieve higher sparsity in LLMs with minimal performance degradation offers a path to more efficient AI inference, potentially impacting the cost and reach of advanced AI.
- · AI developers and researchers
- · Cloud computing providers (through increased efficiency)
- · Organizations deploying LLMs at scale
- · Hardware manufacturers (for specialized sparse compute)
- · Less efficient pruning methods
More powerful and complex LLMs can be deployed on existing or less powerful hardware due to reduced computational demands.
The cost-effectiveness of AI inference improves, broadening the adoption of sophisticated AI models across various industries.
Increased accessibility might accelerate AI development and deployment in regions or organizations with limited computational resources, potentially contributing to sovereign AI initiatives.
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Read at arXiv cs.CL