Study Benchmarks Real-World Performance of LLM Pruning Methods Using GEMM-Centric Framework
Researchers have introduced a GEMM-centric taxonomy and unified benchmarking framework to systematically compare how different large language model (LLM) pruning methods perform in real hardware conditions. The study addresses a gap in the field where theoretical FLOPs reductions from pruning often fail to translate into actual inference speedups due to hardware and kernel implementation differences. The findings provide the first unified view of practical pruning limits and offer concrete guidance for choosing pruning strategies based on acceptable quality trade-offs.
A new preprint from arXiv reorganizes LLM pruning methods—which remove computation across tokens, layers, heads, dimensions, and attention patterns—according to the M, N, and K dimensions of general matrix multiplication (GEMM), enabling consistent cross-method comparisons. The researchers built a unified benchmarking framework on top of this taxonomy to characterize the acceleration-quality Pareto frontier under real hardware conditions rather than relying solely on theoretical FLOPs counts. Their results show that static depth pruning is the strongest Pareto-optimal baseline overall and comes closest to its theoretical acceleration upper bound in memory-bounded scenarios. During the prefill phase, the optimal pruning strategy shifts depending on how much quality loss is tolerable: static depth pruning dominates at low loss (0–4%), dynamic depth pruning takes over at moderate loss (5–16%), and static width pruning becomes preferable at higher loss levels (17–26%). The authors release their code publicly and argue these findings establish the first systematic, implementation-consistent map of practical LLM pruning acceleration limits.
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The paper has not yet undergone peer review.
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- arXiv cs.LGCenter
Beyond FLOPs: Benchmarking Real Inference Acceleration of LLM Pruning under a GEMM-Centric Taxonomy
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