Compression Technique Interactions in Small Language Models: A Factorial Ablation Study

arXiv cs.AINovember 3, 2026en

arXiv cs.AI

AI Global Wire

arXiv:2611.00464v1 Announce Type: new Abstract: Deploying small language models on resource-constrained hardware requires compression, yet practitioners lack systematic guidance on how quantization, pruning, and post-compression recovery interact when combined. We present a factorial ablation study evaluating 72 compression configurations across four SLM architectures (2.7B-7B parameters), extended with a 48-configuration ordering analysis, benchmark accuracy validation on 30 compressed models, and robustness checks including multi-seed validation and full-corpus perplexity evaluation. INT8 quantization is effectively lossless (<0.5% accuracy degradation), while NF4 quantization combined wit

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