| Gptq: Accurate post-training quantization for generative pre-trained transformers E Frantar, S Ashkboos, T Hoefler, D Alistarh arXiv preprint arXiv:2210.17323, 2022 | 2167 | 2022 |
| Sparsegpt: Massive language models can be accurately pruned in one-shot E Frantar, D Alistarh International conference on machine learning, 10323-10337, 2023 | 1198 | 2023 |
| Optimal brain compression: A framework for accurate post-training quantization and pruning E Frantar, D Alistarh Advances in Neural Information Processing Systems 35, 4475-4488, 2022 | 396 | 2022 |
| Spqr: A sparse-quantized representation for near-lossless llm weight compression T Dettmers, R Svirschevski, V Egiazarian, D Kuznedelev, E Frantar, ... arXiv preprint arXiv:2306.03078, 2023 | 372 | 2023 |
| The optimal bert surgeon: Scalable and accurate second-order pruning for large language models E Kurtic, D Campos, T Nguyen, E Frantar, M Kurtz, B Fineran, M Goin, ... arXiv preprint arXiv:2203.07259, 2022 | 189 | 2022 |
| Extreme compression of large language models via additive quantization V Egiazarian, A Panferov, D Kuznedelev, E Frantar, A Babenko, D Alistarh arXiv preprint arXiv:2401.06118, 2024 | 156 | 2024 |
| Ziplm: Hardware-aware structured pruning of language models E Kurtic, E Frantar, D Alistarh arXiv preprint arXiv:2302.04089 3 (7), 2023 | 100* | 2023 |
| Marlin: a fast 4-bit inference kernel for medium batchsizes E Frantar, D Alistarh | 79* | 2024 |
| M-FAC: Efficient matrix-free approximations of second-order information E Frantar, E Kurtic, D Alistarh Advances in Neural Information Processing Systems 34, 14873-14886, 2021 | 79 | 2021 |
| Quik: Towards end-to-end 4-bit inference on generative large language models S Ashkboos, I Markov, E Frantar, T Zhong, X Wang, J Ren, T Hoefler, ... Proceedings of the 2024 Conference on Empirical Methods in Natural Language …, 2024 | 74 | 2024 |
| SPDY: Accurate pruning with speedup guarantees E Frantar, D Alistarh International conference on machine learning, 6726-6743, 2022 | 57 | 2022 |
| Qmoe: Sub-1-bit compression of trillion parameter models E Frantar, D Alistarh Proceedings of Machine Learning and Systems 6, 439-451, 2024 | 46* | 2024 |
| Scaling laws for sparsely-connected foundation models E Frantar, C Riquelme, N Houlsby, D Alistarh, U Evci arXiv preprint arXiv:2309.08520, 2023 | 38 | 2023 |
| Sparse fine-tuning for inference acceleration of large language models E Kurtic, D Kuznedelev, E Frantar, M Goinv, S Pandit, A Agarwalla, ... Enhancing LLM Performance: Efficacy, Fine-Tuning, and Inference Techniques 7, 83, 2025 | 32 | 2025 |
| On the sample complexity of adversarial multi-source pac learning N Konstantinov, E Frantar, D Alistarh, C Lampert International Conference on Machine Learning, 5416-5425, 2020 | 32 | 2020 |
| Cap: Correlation-aware pruning for highly-accurate sparse vision models D Kuznedelev, E Kurtić, E Frantar, D Alistarh Advances in Neural Information Processing Systems 36, 28805-28831, 2023 | 28* | 2023 |
| L-GreCo: Layerwise-adaptive Gradient Compression For Efficient Data-parallel Deep Learning I Markov, K Alimohammadi, E Frantar, D Alistarh Proceedings of Machine Learning and Systems 6, 312-324, 2024 | 17* | 2024 |
| Jaxpruner: A concise library for sparsity research JH Lee, W Park, NE Mitchell, J Pilault, JSO Ceron, HB Kim, N Lee, ... Conference on Parsimony and Learning, 515-528, 2024 | 16 | 2024 |
| Accurate neural network pruning requires rethinking sparse optimization D Kuznedelev, E Kurtic, E Iofinova, E Frantar, A Peste, D Alistarh arXiv preprint arXiv:2308.02060, 2023 | 14 | 2023 |
| Qigen: Generating efficient kernels for quantized inference on large language models T Pegolotti, E Frantar, D Alistarh, M Püschel arXiv preprint arXiv:2307.03738, 2023 | 10* | 2023 |
