---
title: "Who cites Accelerating Distributed MoE — 91 citing papers"
description: "91 papers of the Elamur Research library cite «Accelerating Distributed MoE Training and Inference with Lina», found in their bibliographies…"
canonical: "https://research.elamur.ai/free/en/papers/li-2022-accelerating-distributed-moe-training-inference/cited-by/"
---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Accelerating Distributed MoE Training and…](https://md.research.elamur.ai/free/en/papers/li-2022-accelerating-distributed-moe-training-inference/) / cited by

# Who cites «Accelerating Distributed MoE Training and Inference with Lina»

91 papers of our library cite it (of the 149,439 whose bibliographies we read) · 7 with a page here, the most cited first · [← back to «Accelerating Distributed MoE Training and Inference with Lina»](https://md.research.elamur.ai/free/en/papers/li-2022-accelerating-distributed-moe-training-inference/)

1. [**Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Serve OSDI 2024**Amey Agrawal, Nitin Kedia, Ashish Panwar, et al.*2024 · 369 citations*](https://md.research.elamur.ai/free/en/papers/agrawal-2024-taming-throughput-latency-tradeoff-llm/)
2. [**SARATHI: Efficient LLM Inference by Piggybacking Decodes with Chunked Prefills arXiv 2023**Amey Agrawal, Ashish Panwar, Jayashree Mohan, et al.*2023 · 144 citations*](https://md.research.elamur.ai/free/en/papers/agrawal-2023-sarathi-efficient-llm-inference-piggybacking/)
3. [**Efficient Large Language Models: A Survey arXiv:2312.03863**Zhongwei Wan, Xin Wang, Che Liu, et al.*2023 · 110 citations*](https://md.research.elamur.ai/free/en/papers/wan-2023-efficient-large-language-models-survey/)
4. **[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*
5. [**Cost-Efficient Large Language Model Serving for Multi-turn Conversations with CachedAttention USENIX ATC 2024**Bin Gao, Zhuomin He, Puru Sharma, et al.*2024 · 105 citations*](https://md.research.elamur.ai/free/en/papers/gao-2024-cost-efficient-large-language-model/)
6. [**Step-3 is Large yet Affordable: Model-system Co-design for Cost-effective Decoding arXiv:2507.19427**StepFun, Bin Wang, Bojun Wang, et al.*2025 · 26 citations*](https://md.research.elamur.ai/free/en/papers/stepfun-2025-step-3-large-yet/)
7. **[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*

84 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/li-2022-accelerating-distributed-moe-training-inference/) — Accelerating Distributed MoE Training and Inference with Lina