--- title: "Who cites PowerInfer: Fast Large — 139 citing papers" description: "139 papers of the Elamur Research library cite «PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPU», found in…" canonical: "https://research.elamur.ai/free/en/papers/song-2023-powerinfer-fast-large-language-model/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/song-2023-powerinfer-fast-large-language-model/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [PowerInfer: Fast Large Language Model Serving…](https://md.research.elamur.ai/free/en/papers/song-2023-powerinfer-fast-large-language-model/) / cited by # Who cites «PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPU» 139 papers of our library cite it (of the 149,439 whose bibliographies we read) · 12 with a page here, the most cited first · [← back to «PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPU»](https://md.research.elamur.ai/free/en/papers/song-2023-powerinfer-fast-large-language-model/) 1. [**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/) 2. [**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/) 3. [**A Survey on Efficient Inference for Large Language Models arXiv:2404.14294**Zixuan Zhou, Xuefei Ning, Ke Hong, et al.*2024 · 113 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2024-survey-efficient-inference-large-language/) 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. [**Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security arXiv:2401.05459**Yuanchun Li, Hao Wen, Weijun Wang, et al.*2024 · 100 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-personal-llm-agents-insights-survey/) 7. [**Small Language Models are the Future of Agentic AI arXiv:2506.02153**Peter Belcak, Greg Heinrich, Shizhe Diao, et al.*2025 · 92 citations*](https://md.research.elamur.ai/free/en/papers/belcak-2025-small-language-models-future-agentic/) 8. [**LLM Inference Unveiled: Survey and Roofline Model Insights arXiv 2024**Zhihang Yuan, Yuzhang Shang, Yang Zhou, et al.*2024 · 91 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2024-llm-inference-unveiled-survey-roofline/) 9. [**Small Language Models: Survey, Measurements, and Insights arXiv:2409.15790**Zhenyan Lu, Xiang Li, Dongqi Cai, et al.*2024 · 88 citations*](https://md.research.elamur.ai/free/en/papers/lu-2024-small-language-models-survey-measurements/) 10. [**From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models arXiv:2406.16838**Sean Welleck, Amanda Bertsch, Matthew Finlayson, et al.*2024 · 51 citations*](https://md.research.elamur.ai/free/en/papers/welleck-2024-decoding-meta-generation-inference-time/) 11. **[Let's Focus on Neuron: Neuron-Level Supervised Fine-tuning for Large Language Model](https://md.research.elamur.ai/free/en/papers/xu-2025-let-s-focus-neuron-neuron/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Haoyun Xu, Runzhe Zhan, Yingpeng Ma, et al.*2025 · 10 citations* 12. **[Dual Activation-Weight Sparsity: A Training-Free Framework for Efficient Large Language Model Compression](https://md.research.elamur.ai/free/en/papers/sun-2026-dual-activation-weight-sparsity-training/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Luoyang Sun, Guangyan Li, Cheng Deng, et al.*2026* 127 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/song-2023-powerinfer-fast-large-language-model/) — PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPU