--- title: "Who cites Atom: Low-bit Quantization for — 132 citing papers" description: "132 papers of the Elamur Research library cite «Atom: Low-bit Quantization for Efficient and Accurate LLM Serving», found in their…" canonical: "https://research.elamur.ai/free/en/papers/zhao-2023-atom-low-bit-quantization-efficient/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/zhao-2023-atom-low-bit-quantization-efficient/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Atom: Low-bit Quantization for Efficient and…](https://md.research.elamur.ai/free/en/papers/zhao-2023-atom-low-bit-quantization-efficient/) / cited by # Who cites «Atom: Low-bit Quantization for Efficient and Accurate LLM Serving» 132 papers of our library cite it (of the 149,439 whose bibliographies we read) · 16 with a page here, the most cited first · [← back to «Atom: Low-bit Quantization for Efficient and Accurate LLM Serving»](https://md.research.elamur.ai/free/en/papers/zhao-2023-atom-low-bit-quantization-efficient/) 1. **[KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization](https://md.research.elamur.ai/free/en/papers/hooper-2024-kvquant-towards-10-million-context/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Coleman Hooper, Sehoon Kim, Hiva Mohammadzadeh, et al.*2024 · 294 citations* 2. **[QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs](https://md.research.elamur.ai/free/en/papers/ashkboos-2024-quarot-outlier-free-4-bit/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Saleh Ashkboos, Amirkeivan Mohtashami, Maximilian L. Croci, et al.*2024 · 254 citations* 3. [**KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache arXiv:2402.02750**Zirui Liu, Jiayi Yuan, Hongye Jin, et al.*2024 · 244 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-kivi-tuning-free-asymmetric-2bit/) 4. [**Quest: Query-Aware Sparsity for Efficient Long-Context LLM Inference arXiv:2406.10774**Jiaming Tang, Yilong Zhao, Kan Zhu, et al.*2024 · 203 citations*](https://md.research.elamur.ai/free/en/papers/tang-2024-quest-query-aware-sparsity-efficient/) 5. **[LLM in a flash: Efficient Large Language Model Inference with Limited Memory](https://md.research.elamur.ai/free/en/papers/alizadeh-2024-llm-flash-efficient-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Keivan Alizadeh, Seyed Iman Mirzadeh, Dmitry Belenko, et al.*2024 · 193 citations* 6. [**QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving MLSys 2025**Yujun Lin, Haotian Tang, Shang Yang, et al.*2024 · 120 citations*](https://md.research.elamur.ai/free/en/papers/lin-2024-qserve-w4a8kv4-quantization-system-co/) 7. [**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/) 8. **[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* 9. **[EfficientQAT: Efficient Quantization-Aware Training for Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-efficientqat-efficient-quantization-aware-training/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mengzhao Chen, Wenqi Shao, Peng Xu, et al.*2025 · 82 citations* 10. [**A Survey of Resource-efficient LLM and Multimodal Foundation Models arXiv:2401.08092**Mengwei Xu, Wangsong Yin, Dongqi Cai, et al.*2024 · 54 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-survey-resource-efficient-llm-multimodal/) 11. [**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/) 12. **[ARCQuant: Boosting NVFP4 Quantization with Augmented Residual Channels for LLMs](https://md.research.elamur.ai/free/en/papers/meng-2026-arcquant-boosting-nvfp4-quantization-augmented/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haoqian Meng, Yilun Luo, Yafei Zhao, et al.*2026 · 5 citations* 13. **[PagedEviction: Structured Block-wise KV Cache Pruning for Efficient Large Language Model Inference](https://md.research.elamur.ai/free/en/papers/venkata-2026-pagedeviction-structured-block-wise-kv/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Krishna Teja Chitty-Venkata, Jie Ye, Siddhisanket Raskar, et al.*2026 · 4 citations* 14. **[Balancing Fidelity and Plasticity: Aligning Mixed-Precision Fine-Tuning with Linguistic Hierarchies](https://md.research.elamur.ai/free/en/papers/zhou-2026-balancing-fidelity-plasticity-aligning-mixed/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Changhai Zhou, Shiyang Zhang, Yuhua Zhou, et al.*2026 · 3 citations* 15. **[HqeKV: Towards Hybrid Quantization and Eviction for KV Cache in Long-Context LLM Inference](https://md.research.elamur.ai/free/en/papers/wang-2026-hqekv-hybrid-quantization-eviction-kv/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**He Wang, Yu Gu, Fangfang Li, et al.*2026 · 1 citation* 16. **[RRAtention: Dynamic Block Sparse Attention via Per-Head Round-Robin Shifts for Long-Context Inference](https://md.research.elamur.ai/free/en/papers/liu-2026-rratention-dynamic-block-sparse-attention/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Siran Liu, Guoxia Wang, Sa Wang, et al.*2026* 116 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/zhao-2023-atom-low-bit-quantization-efficient/) — Atom: Low-bit Quantization for Efficient and Accurate LLM Serving