mlsanityData to 14 Oct 2026

PTSBench: A Comprehensive Post-Training Sparsity Benchmark Towards Algorithms and Models

arXiv 2412.07268 ↗First posted 10 Dec 2024cs.LG cs.MMcited by 5

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Abstract (arXiv)

With the increased attention to model efficiency, post-training sparsity (PTS) has become more and more prevalent because of its effectiveness and efficiency. However, there remain questions on better practice of PTS algorithms and the sparsification ability of models, which hinders the further development of this area. Therefore, a benchmark to comprehensively investigate the issues above is urgently needed. In this paper, we propose the first comprehensive post-training sparsity benchmark called PTSBench towards algorithms and models. We benchmark 10+ PTS general-pluggable fine-grained techniques on 3 typical tasks using over 40 off-the-shelf model architectures. Through extensive experiments and analyses, we obtain valuable conclusions and provide several insights from both algorithms and model aspects. Our PTSBench can provide (1) new observations for a better understanding of the PTS algorithms, (2) in-depth and comprehensive evaluations for the sparsification ability of models, and (3) a well-structured and easy-integrate open-source framework. We hope this work will provide illuminating conclusions and advice for future studies of post-training sparsity methods and sparsification-friendly model design. The code for our PTSBench is released at hrefhttps://github.com/ModelTC/msbenchhttps://github.com/ModelTC/msbench.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 34 of its 57 references that are in the map. First posted on arXiv 10 Dec 2024.

Builds on

34 of 57 references.

References · 34

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22 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 1.

5 papers.

  1. 2025SlimInfer: Accelerating Long-Context LLM Inference via Dynamic Token Pruningcited by 13
  2. 2025SalQ-VLM: Fine-Grained Saliency-Guided Quantization for Vision-Language Modelscited by 8
  3. 2025LLMC+: Benchmarking Vision-Language Model Compression with a Plug-and-play Toolkitcited by 7
  4. 2026Focus-dLLM: Accelerating Long-Context Diffusion LLM Inference via Confidence-Guided Context Focusingcited by 7
  5. 2025SLMQuant:Benchmarking Small Language Model Quantization for Practical Deploymentcited by 2