--- title: "Who cites Alpa: Automating Inter- and — 208 citing papers" description: "208 papers of the Elamur Research library cite «Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning», found…" canonical: "https://research.elamur.ai/free/en/papers/zheng-2022-alpa-automating-inter-intra-operator/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/zheng-2022-alpa-automating-inter-intra-operator/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Alpa: Automating Inter- and Intra-Operator…](https://md.research.elamur.ai/free/en/papers/zheng-2022-alpa-automating-inter-intra-operator/) / cited by # Who cites «Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning» 208 papers of our library cite it (of the 149,439 whose bibliographies we read) · 17 with a page here, the most cited first · [← back to «Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning»](https://md.research.elamur.ai/free/en/papers/zheng-2022-alpa-automating-inter-intra-operator/) 1. **[A Survey of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/) [Frontiers of Computer Science](https://md.research.elamur.ai/free/en/venues/frontiers-of-computer-science/)**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,875 citations* 2. **[Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes](https://md.research.elamur.ai/free/en/papers/hsieh-2023-distilling-step-step-outperforming-larger/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Cheng-Yu Hsieh, Chun-Liang Li, Chih-kuan Yeh, et al.*2023 · 690 citations* 3. [**HybridFlow: A Flexible and Efficient RLHF Framework arXiv:2409.19256**Guangming Sheng, Chi Zhang, Zilingfeng Ye, et al.*2024 · 624 citations*](https://md.research.elamur.ai/free/en/papers/sheng-2024-hybridflow-flexible-efficient-rlhf-framework/) 4. [**DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving OSDI 2024**Yinmin Zhong, Shengyu Liu, Junda Chen, et al.*2024 · 409 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2024-distserve-disaggregating-prefill-decoding-goodput/) 5. [**FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU arXiv:2303.06865**Ying Sheng, Lianmin Zheng, Binhang Yuan, et al.*2023 · 402 citations*](https://md.research.elamur.ai/free/en/papers/sheng-2023-flexgen-high-throughput-generative-inference/) 6. [**Challenges and Applications of Large Language Models arXiv:2307.10169**Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.*2023 · 301 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/) 7. [**AlpaServe: Statistical Multiplexing with Model Parallelism for Deep Learning Serving OSDI 2023**Zhuohan Li, Lianmin Zheng, Yinmin Zhong, et al.*2023 · 170 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-alpaserve-statistical-multiplexing-model-parallelism/) 8. [**Tutel: Adaptive Mixture-of-Experts at Scale MLSys 2023**Changho Hwang, Wei Cui, Yifan Xiong, et al.*2022 · 168 citations*](https://md.research.elamur.ai/free/en/papers/hwang-2022-tutel-adaptive-mixture-experts-scale/) 9. **[Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems](https://md.research.elamur.ai/free/en/papers/miao-2023-towards-efficient-generative-large-language/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Xupeng Miao, Gabriele Oliaro, Zhihao Zhang, et al.*2023 · 110 citations* 10. [**Accelerating Distributed MoE Training and Inference with Lina arXiv:2210.17223**Jiamin Li, Yimin Jiang, Yibo Zhu, et al.*2022 · 92 citations*](https://md.research.elamur.ai/free/en/papers/li-2022-accelerating-distributed-moe-training-inference/) 11. **[DOC: Improving Long Story Coherence With Detailed Outline Control](https://md.research.elamur.ai/free/en/papers/yang-2023-doc-improving-long-story-coherence/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kevin Yang, Dan Klein, Nanyun Peng, et al.*2023 · 83 citations* 12. [**S-LoRA: Serving Thousands of Concurrent LoRA Adapters MLSys 2024**Ying Sheng, Shiyi Cao, Dacheng Li, et al.*2023 · 80 citations*](https://md.research.elamur.ai/free/en/papers/sheng-2023-s-lora-serving-thousands-concurrent/) 13. [**Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation arXiv:2307.15337**Xuefei Ning, Zinan Lin, Zixuan Zhou, et al.*2023 · 72 citations*](https://md.research.elamur.ai/free/en/papers/ning-2023-skeleton-thought-prompting-llms-efficient/) 14. [**Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learning arXiv:2504.13914**ByteDance Seed, Jiaze Chen, Tiantian Fan, et al.*2025 · 62 citations*](https://md.research.elamur.ai/free/en/papers/bytedance-seed-2025-seed1-5-thinking-advancing/) 15. [**Mixed Distillation Helps Smaller Language Model Better Reasoning arXiv:2312.10730**Chenglin Li, Qianglong Chen, Liangyue Li, et al.*2023 · 11 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-mixed-distillation-helps-smaller-language/) 16. **[High-Throughput and Memory-Efficient Zeroth-Order Fine-tuning LLMs with Distributed Parallel Computing](https://md.research.elamur.ai/free/en/papers/wang-2026-high-throughput-memory-efficient-zeroth/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Liangyu Wang, Huanyi Xie, Di Wang*2026* 17. **[Survey on Key Technologies for Large Language Model Pre-training Systems (大语言模型预训练系统关键技术综述)](https://md.research.elamur.ai/free/en/papers/gao-2025-key-technologies-large/) [Journal of Software](https://md.research.elamur.ai/free/en/venues/journal-of-software/)**Yanjie Gao (高彦杰), Yueguo Chen (陈跃国)*2025* 191 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/zheng-2022-alpa-automating-inter-intra-operator/) — Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning