Elias Frantar

17 Publications

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[17]
2025 | Published | Conference Paper | IST-REx-ID: 19877 | OA
Frantar, Elias, et al. “MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on Large Language Models.” Proceedings of the 30th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, Association for Computing Machinery, 2025, pp. 239–51, doi:10.1145/3710848.3710871.
[Published Version] View | Files available | DOI | arXiv
 
[16]
2024 | Published | Conference Paper | IST-REx-ID: 18113 | OA
Egiazarian, Vage, et al. “Extreme Compression of Large Language Models via Additive Quantization.” Proceedings of the 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 12284–303.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[15]
2024 | Published | Conference Paper | IST-REx-ID: 18975 | OA
Modoranu, Ionut-Vlad, et al. “Error Feedback Can Accurately Compress Preconditioners.” 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 35910–33.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[14]
2024 | Published | Conference Paper | IST-REx-ID: 18977 | OA
Dettmers, Tim, et al. “SpQR: A Sparse-Quantized Representation for near-Lossless LLM Weight Compression.” 12th International Conference on Learning Representations, OpenReview, 2024.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[13]
2024 | Published | Thesis | IST-REx-ID: 17485 | OA
Frantar, Elias. Compressing Large Neural Networks : Algorithms, Systems and Scaling Laws. Institute of Science and Technology Austria, 2024, doi:10.15479/at:ista:17485.
[Published Version] View | Files available | DOI
 
[12]
2024 | Published | Conference Paper | IST-REx-ID: 18061 | OA
Frantar, Elias, and Dan-Adrian Alistarh. “QMoE: Sub-1-Bit Compression of Trillion Parameter Models.” Proceedings of Machine Learning and Systems, edited by P. Gibbons et al., vol. 6, 2024.
[Published Version] View | Files available | Download Published Version (ext.)
 
[11]
2024 | Published | Conference Paper | IST-REx-ID: 18062 | OA
Frantar, Elias, et al. “Scaling Laws for Sparsely-Connected Foundation Models.” The Twelfth International Conference on Learning Representations, 2024.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[10]
2024 | Published | Conference Paper | IST-REx-ID: 18121 | OA
Moakhar, Arshia Soltani, et al. “SPADE: Sparsity-Guided Debugging for Deep Neural Networks.” Proceedings of the 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 45955–87.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[9]
2024 | Published | Conference Paper | IST-REx-ID: 17456 | OA
Markov, Ilia, et al. “L-GreCo: Layerwise-Adaptive Gradient Compression for Efficient Data-Parallel Deep Learning.” Proceedings of Machine Learning and Systems , edited by P. Gibbons et al., vol. 6, Association for Computing Machinery, 2024.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[8]
2024 | Research Data Reference | IST-REx-ID: 19884 | OA
Frantar, Elias, et al. MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on Large Language Models. Zenodo, 2024, doi:10.5281/ZENODO.14213091.
[Published Version] View | Files available | DOI | Download Published Version (ext.)
 
[7]
2023 | Published | Conference Paper | IST-REx-ID: 17378 | OA
Frantar, Elias, et al. “OPTQ: Accurate Post-Training Quantization for Generative Pre-Trained Transformers.” 11th International Conference on Learning Representations , International Conference on Learning Representations, 2023.
[Published Version] View | Files available
 
[6]
2023 | Published | Conference Paper | IST-REx-ID: 14458 | OA
Frantar, Elias, and Dan-Adrian Alistarh. “SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot.” Proceedings of the 40th International Conference on Machine Learning, vol. 202, ML Research Press, 2023, pp. 10323–37.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[5]
2022 | Published | Conference Paper | IST-REx-ID: 17088 | OA
Kurtic, Eldar, et al. “The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.” Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, 2022, pp. 4163–81, doi:10.18653/v1/2022.emnlp-main.279.
[Published Version] View | Files available | DOI | arXiv
 
[4]
2022 | Published | Conference Paper | IST-REx-ID: 17087 | OA
Frantar, Elias, et al. “Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning.” 36th Conference on Neural Information Processing Systems, vol. 35, ML Research Press, 2022.
[Submitted Version] View | Files available | arXiv
 
[3]
2022 | Published | Conference Paper | IST-REx-ID: 17059 | OA
Frantar, Elias, and Dan-Adrian Alistarh. “SPDY: Accurate Pruning with Speedup Guarantees.” 39th International Conference on Machine Learning, vol. 162, ML Research Press, 2022, pp. 6726–43.
[Published Version] View | Files available | WoS
 
[2]
2021 | Published | Conference Paper | IST-REx-ID: 11463 | OA
Frantar, Elias, et al. “M-FAC: Efficient Matrix-Free Approximations of Second-Order Information.” 35th Conference on Neural Information Processing Systems, vol. 34, Neural Information Processing Systems Foundation, 2021, pp. 14873–86.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[1]
2020 | Published | Conference Paper | IST-REx-ID: 8724 | OA
Konstantinov, Nikola H., et al. “On the Sample Complexity of Adversarial Multi-Source PAC Learning.” Proceedings of the 37th International Conference on Machine Learning, vol. 119, ML Research Press, 2020, pp. 5416–25.
[Published Version] View | Files available | arXiv
 
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17 Publications

Mark all

[17]
2025 | Published | Conference Paper | IST-REx-ID: 19877 | OA
Frantar, Elias, et al. “MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on Large Language Models.” Proceedings of the 30th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, Association for Computing Machinery, 2025, pp. 239–51, doi:10.1145/3710848.3710871.
[Published Version] View | Files available | DOI | arXiv
 
[16]
2024 | Published | Conference Paper | IST-REx-ID: 18113 | OA
Egiazarian, Vage, et al. “Extreme Compression of Large Language Models via Additive Quantization.” Proceedings of the 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 12284–303.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[15]
2024 | Published | Conference Paper | IST-REx-ID: 18975 | OA
Modoranu, Ionut-Vlad, et al. “Error Feedback Can Accurately Compress Preconditioners.” 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 35910–33.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[14]
2024 | Published | Conference Paper | IST-REx-ID: 18977 | OA
Dettmers, Tim, et al. “SpQR: A Sparse-Quantized Representation for near-Lossless LLM Weight Compression.” 12th International Conference on Learning Representations, OpenReview, 2024.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[13]
2024 | Published | Thesis | IST-REx-ID: 17485 | OA
Frantar, Elias. Compressing Large Neural Networks : Algorithms, Systems and Scaling Laws. Institute of Science and Technology Austria, 2024, doi:10.15479/at:ista:17485.
[Published Version] View | Files available | DOI
 
[12]
2024 | Published | Conference Paper | IST-REx-ID: 18061 | OA
Frantar, Elias, and Dan-Adrian Alistarh. “QMoE: Sub-1-Bit Compression of Trillion Parameter Models.” Proceedings of Machine Learning and Systems, edited by P. Gibbons et al., vol. 6, 2024.
[Published Version] View | Files available | Download Published Version (ext.)
 
[11]
2024 | Published | Conference Paper | IST-REx-ID: 18062 | OA
Frantar, Elias, et al. “Scaling Laws for Sparsely-Connected Foundation Models.” The Twelfth International Conference on Learning Representations, 2024.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[10]
2024 | Published | Conference Paper | IST-REx-ID: 18121 | OA
Moakhar, Arshia Soltani, et al. “SPADE: Sparsity-Guided Debugging for Deep Neural Networks.” Proceedings of the 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 45955–87.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[9]
2024 | Published | Conference Paper | IST-REx-ID: 17456 | OA
Markov, Ilia, et al. “L-GreCo: Layerwise-Adaptive Gradient Compression for Efficient Data-Parallel Deep Learning.” Proceedings of Machine Learning and Systems , edited by P. Gibbons et al., vol. 6, Association for Computing Machinery, 2024.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[8]
2024 | Research Data Reference | IST-REx-ID: 19884 | OA
Frantar, Elias, et al. MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on Large Language Models. Zenodo, 2024, doi:10.5281/ZENODO.14213091.
[Published Version] View | Files available | DOI | Download Published Version (ext.)
 
[7]
2023 | Published | Conference Paper | IST-REx-ID: 17378 | OA
Frantar, Elias, et al. “OPTQ: Accurate Post-Training Quantization for Generative Pre-Trained Transformers.” 11th International Conference on Learning Representations , International Conference on Learning Representations, 2023.
[Published Version] View | Files available
 
[6]
2023 | Published | Conference Paper | IST-REx-ID: 14458 | OA
Frantar, Elias, and Dan-Adrian Alistarh. “SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot.” Proceedings of the 40th International Conference on Machine Learning, vol. 202, ML Research Press, 2023, pp. 10323–37.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[5]
2022 | Published | Conference Paper | IST-REx-ID: 17088 | OA
Kurtic, Eldar, et al. “The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.” Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, 2022, pp. 4163–81, doi:10.18653/v1/2022.emnlp-main.279.
[Published Version] View | Files available | DOI | arXiv
 
[4]
2022 | Published | Conference Paper | IST-REx-ID: 17087 | OA
Frantar, Elias, et al. “Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning.” 36th Conference on Neural Information Processing Systems, vol. 35, ML Research Press, 2022.
[Submitted Version] View | Files available | arXiv
 
[3]
2022 | Published | Conference Paper | IST-REx-ID: 17059 | OA
Frantar, Elias, and Dan-Adrian Alistarh. “SPDY: Accurate Pruning with Speedup Guarantees.” 39th International Conference on Machine Learning, vol. 162, ML Research Press, 2022, pp. 6726–43.
[Published Version] View | Files available | WoS
 
[2]
2021 | Published | Conference Paper | IST-REx-ID: 11463 | OA
Frantar, Elias, et al. “M-FAC: Efficient Matrix-Free Approximations of Second-Order Information.” 35th Conference on Neural Information Processing Systems, vol. 34, Neural Information Processing Systems Foundation, 2021, pp. 14873–86.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[1]
2020 | Published | Conference Paper | IST-REx-ID: 8724 | OA
Konstantinov, Nikola H., et al. “On the Sample Complexity of Adversarial Multi-Source PAC Learning.” Proceedings of the 37th International Conference on Machine Learning, vol. 119, ML Research Press, 2020, pp. 5416–25.
[Published Version] View | Files available | arXiv
 
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Citation Style: MLA

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