@inproceedings{jimenez2024swebench,
  author    = {Carlos E. Jimenez and John Yang and Alexander Wettig and
               Shunyu Yao and Kexin Pei and Ofir Press and Karthik Narasimhan},
  title     = {{SWE}-bench: Can Language Models Resolve Real-world
               {G}it{H}ub Issues?},
  booktitle = {Proc. ICLR},
  year      = {2024},
}

@misc{bfcl2024,
  author       = {Fanjia Yan and Huanzhi Mao and Charlie Cheng-Jie Ji and
                  Tianjun Zhang and Shishir G. Patil and Ion Stoica and
                  Joseph E. Gonzalez},
  title        = {{Berkeley} Function-Calling Leaderboard},
  howpublished = {\url{https://gorilla.cs.berkeley.edu/blogs/8_berkeley_function_calling_leaderboard.html}},
  year         = {2024},
}

@misc{vllmsemanticRouter2026,
  author       = {{vLLM Project Contributors}},
  title        = {{vLLM} Semantic Router},
  howpublished = {\url{https://github.com/vllm-project/semantic-router}},
  year         = {2026},
  note         = {Open-source LLM request routing framework with
                  per-category {ModernBERT/mmBERT-32K} classification,
                  pool-based dispatch, and {Envoy} ext\_proc integration},
}


@inproceedings{orca2022,
  author    = {Gyeong-In Yu and Joo Seong Jeong and Geon-Woo Kim and
               Soojeong Kim and Byung-Gon Chun},
  title     = {{Orca}: A Distributed Serving System for Transformer-Based
               Generative Models},
  booktitle = {Proc. OSDI},
  year      = {2022},
}

@article{chen2026poolrouting,
  author    = {Huamin Chen and Xunzhuo Liu and Junchen Jiang and
               Bowei He and Xue Liu},
  title     = {Token-Budget-Aware Pool Routing for Cost-Efficient
               {LLM} Inference},
  journal   = {arXiv preprint},
  year      = {2026},
  note      = {Manuscript under review},
}

@inproceedings{agrawal2024sarathi,
  author    = {Amey Agrawal and Nikhil Kedia and Ashish Panwar and
               Jayashree Mohan and Nipun Kwatra and Bhargav S.\ Gulavani and
               Alexey Tumanov and Ramachandran Ramjee},
  title     = {Taming Throughput-Latency Tradeoff in {LLM} Inference with
               {Sarathi-Serve}},
  booktitle = {Proc. OSDI},
  year      = {2024},
}

@inproceedings{li2023selective,
  author    = {Yucheng Li and Bo Dong and Chenghua Lin and Frank Guerin},
  title     = {Compressing Context to Enhance Inference Efficiency of Large
               Language Models},
  booktitle = {Proc. EMNLP},
  year      = {2023},
}

@inproceedings{jiang2023llmlingua,
  author    = {Huiqiang Jiang and Qianhui Wu and Chin-Yew Lin and
               Yuqing Yang and Linyun Yu},
  title     = {{LLMLingua}: Compressing Prompts for Accelerated Inference
               of {LLMs}},
  booktitle = {Proc. EMNLP},
  year      = {2023},
}

@inproceedings{liu2024lost,
  author    = {Nelson F. Liu and Kevin Lin and John Hewitt and
               Ashwin Paranjape and Michele Bevilacqua and Fabio Petroni and
               Percy Liang},
  title     = {Lost in the Middle: How Language Models Use Long Contexts},
  booktitle = {Trans. ACL (TACL)},
  year      = {2024},
}

@inproceedings{mihalcea2004textrank,
  author    = {Rada Mihalcea and Paul Tarau},
  title     = {{TextRank}: Bringing Order into Texts},
  booktitle = {Proc. EMNLP},
  year      = {2004},
}

@inproceedings{carbonell1998mmr,
  author    = {Jaime Carbonell and Jade Goldstein},
  title     = {The Use of {MMR}, Diversity-Based Reranking for Reordering
               Documents and Producing Summaries},
  booktitle = {Proc. SIGIR},
  year      = {1998},
}

@misc{azure-llm-traces,
  author       = {{Microsoft Azure}},
  title        = {{Azure LLM} Inference Trace 2023},
  howpublished = {\url{https://github.com/Azure/AzurePublicDataset}},
  year         = {2023},
}

@inproceedings{zheng2024lmsyschat1m,
  author    = {Lianmin Zheng and Wei-Lin Chiang and Ying Sheng and
               Tianle Li and Siyuan Zhuang and Zhanghao Wu and
               Yonghao Zhuang and Zhuohan Li and Zi Lin and Eric P. Xing
               and Joseph E. Gonzalez and Ion Stoica and Hao Zhang},
  title     = {{LMSYS-Chat-1M}: A Large-Scale Real-World {LLM} Conversation
               Dataset},
  booktitle = {Proc. ICLR},
  year      = {2024},
}

@article{servegen2025,
  author    = {{Alibaba}},
  title     = {{ServeGen}: Workload Characterization and Generation of
               Large Language Model Serving in Production},
  journal   = {arXiv:2505.09999},
  year      = {2025},
}

@article{burstgpt2024,
  author    = {Yuxin Wang and others},
  title     = {{BurstGPT}: A Real-World Workload Dataset to Optimize
               {LLM} Serving Systems},
  journal   = {arXiv:2401.17644},
  year      = {2024},
}

@article{laps2026,
  author    = {Jianshu She and Zonghang Li and Hongchao Du and
               Shangyu Wu and Wenhao Zheng and Eric Xing and
               Zhengzhong Liu and Huaxiu Yao and Jason Xue and Qirong Ho},
  title     = {{LAPS}: A Length-Aware-Prefill {LLM} Serving System},
  journal   = {arXiv:2601.11589},
  year      = {2026},
}

@article{continuum2025,
  author    = {Hanchen Li and Qiuyang Mang and Runyuan He and
               Qizheng Zhang and Huanzhi Mao and Xiaokun Chen and
               Hangrui Zhou and Alvin Cheung and Joseph Gonzalez and
               Ion Stoica},
  title     = {Continuum: Efficient and Robust Multi-Turn {LLM} Agent
               Scheduling with {KV} Cache Time-to-Live},
  journal   = {arXiv:2511.02230},
  year      = {2025},
}

@article{ewsjf2026,
  author    = {Bronislav Sidik and Chaya Levi and Joseph Kampeas},
  title     = {{EWSJF}: An Adaptive Scheduler with Hybrid Partitioning
               for Mixed-Workload {LLM} Inference},
  journal   = {arXiv:2601.21758},
  year      = {2026},
}

@article{lmcache2025,
  author    = {Yuhan Liu and Yihua Cheng and Jiayi Yao and Yuwei An and
               Xiaokun Chen and Shaoting Feng and Yuyang Huang and
               Samuel Shen and Rui Zhang and Kuntai Du and Junchen Jiang},
  title     = {{LMCache}: An Efficient {KV} Cache Layer for
               Enterprise-Scale {LLM} Inference},
  journal   = {arXiv:2510.09665},
  year      = {2025},
}

@article{vllm-optimization,
  author    = {{vLLM Project}},
  title     = {Optimization and Tuning},
  journal   = {\url{https://docs.vllm.ai/en/stable/configuration/optimization/}},
  year      = {2025},
}

@inproceedings{sennrich2016bpe,
  author    = {Rico Sennrich and Barry Haddow and Alexandra Birch},
  title     = {Neural Machine Translation of Rare Words with Subword Units},
  booktitle = {Proc. ACL},
  year      = {2016},
}

@article{specprefill2025,
  author    = {Jingyu Liu and Beidi Chen and Ce Zhang},
  title     = {Speculative Prefill: Turbocharging {TTFT} with Lightweight
               and Training-Free Token Importance Estimation},
  journal   = {arXiv:2502.02789},
  year      = {2025},
}

@article{tokenflow2025,
  author    = {Junyi Chen and Chuheng Du and Renyuan Liu and Shuochao Yao and
               Dingtian Yan and Jiang Liao and Shengzhong Liu and Fan Wu and
               Guihai Chen},
  title     = {{TokenFlow}: Responsive {LLM} Text Streaming Serving under
               Request Burst via Preemptive Scheduling},
  journal   = {arXiv:2510.02758},
  year      = {2025},
}

@article{multiturn2025,
  author    = {Others},
  title     = {More with Less: An Empirical Study of Turn-Control
               Strategies for Efficient Coding Agents},
  journal   = {arXiv:2510.16786},
  year      = {2025},
}

@inproceedings{patel2024splitwise,
  author    = {Pratyush Patel and Esha Choukse and Chaojie Zhang and
               {\'I}\~{n}igo Goiri and Brijesh Warrier and Nithish Mahalingam and
               Ricardo Bianchini},
  title     = {Splitwise: Efficient Generative {LLM} Inference Using
               Phase Splitting},
  booktitle = {Proc. ISCA},
  year      = {2024},
}

@inproceedings{nallapati2017summarunner,
  author    = {Ramesh Nallapati and Feifei Zhai and Bowen Zhou},
  title     = {{SummaRuNNer}: A Recurrent Neural Network Based Sequence
               Model for Extractive Summarization of Documents},
  booktitle = {Proc. AAAI},
  year      = {2017},
}

@misc{liu2019bertsumm,
  author       = {Yang Liu},
  title        = {Fine-tune {BERT} for Extractive Summarization},
  howpublished = {arXiv:1903.10318},
  year         = {2019},
}


@inproceedings{yao2025cacheblend,
  author    = {Jiayi Yao and Hanchen Li and Yuhan Liu and Siddhant Ray and
               Yihua Cheng and Qizheng Zhang and Kuntai Du and Shan Lu and
               Junchen Jiang},
  title     = {{CacheBlend}: Fast Large Language Model Serving for {RAG}
               with Cached Knowledge Fusion},
  booktitle = {Proc. EuroSys},
  year      = {2025},
  note      = {Best Paper Award},
  doi       = {10.1145/3689031.3696098},
}

@inproceedings{bonwick1994slab,
  author    = {Jeff Bonwick},
  title     = {The Slab Allocator: An Object-Caching Kernel Memory Allocator},
  booktitle = {Proc. USENIX Summer Technical Conference},
  year      = {1994},
}

@article{agrawal2024medha,
  author    = {Amey Agrawal and Haoran Qiu and Junda Chen and
               {\'I}\~{n}igo Goiri and Chaojie Zhang and Rayyan Shahid and
               Ramachandran Ramjee and Alexey Tumanov and Esha Choukse},
  title     = {No Request Left Behind: Tackling Heterogeneity in
               Long-Context {LLM} Inference with {Medha}},
  journal   = {arXiv:2409.17264},
  year      = {2024},
}

@article{zhang2024pecsched,
  author    = {Zeyu Zhang and Haiying Shen},
  title     = {{PecSched}: Preemptive and Efficient Cluster Scheduling
               for {LLM} Inference},
  journal   = {arXiv:2409.15104},
  year      = {2024},
}

@article{yuan2025cascadeinfer,
  author    = {Yitao Yuan and Chenqi Zhao and Bohan Zhao and Zane Cao and
               Yongchao He and Wenfei Wu},
  title     = {{CascadeInfer}: Low-Latency and Load-Balanced {LLM}
               Serving via Length-Aware Scheduling},
  journal   = {arXiv:2512.19179},
  year      = {2025},
}

@inproceedings{kwon2023vllm,
  author    = {Woosuk Kwon and Zhuohan Li and Siyuan Zhuang and Ying Sheng and
               Lianmin Zheng and Cody Hao Yu and Joseph E. Gonzalez and
               Hao Zhang and Ion Stoica},
  title     = {Efficient Memory Management for Large Language Model Serving
               with {PagedAttention}},
  booktitle = {Proc. SOSP},
  year      = {2023},
}

@misc{jha2024characterizing,
  author       = {Siddharth Jha and Lutfi Eren Erdogan and Sehoon Kim and
                  Kurt Keutzer and Amir Gholami},
  title        = {Characterizing Prompt Compression Methods for Long Context Inference},
  howpublished = {arXiv:2407.08892; ICML ES-FoMo~II Workshop 2024},
  year         = {2024},
}

@inproceedings{zhang2020bertscore,
  author    = {Tianyi Zhang and Varsha Kishore and Felix Wu and
               Kilian Q. Weinberger and Yoav Artzi},
  title     = {{BERTScore}: Evaluating Text Generation with {BERT}},
  booktitle = {Proc. ICLR},
  year      = {2020},
}

@inproceedings{lin2004rouge,
  author    = {Chin-Yew Lin},
  title     = {{ROUGE}: A Package for Automatic Evaluation of Summaries},
  booktitle = {Proc. ACL Workshop on Text Summarization Branches Out},
  year      = {2004},
}

@misc{openorca2023,
  author    = {Lian, Wing and Goodson, Bleys and Pentland, Eugene and
               Cook, Austin and Vong, Chan and Hunt, "Teknium"},
  title     = {{Open-Orca}: An Open Dataset of {GPT} Augmentations},
  year      = {2023},
  howpublished = {\url{https://huggingface.co/datasets/Open-Orca/OpenOrca}},
  note      = {MIT licence},
}
% ── Core pool routing and C&R papers ────────────────────────────────

@article{chen2026car,
  author    = {Huamin Chen and Xunzhuo Liu and Junchen Jiang and
               Bowei He and Xue Liu},
  title     = {Compress-and-Route: Routing-Layer Prompt Compression
               Against the Long-Context Cost Cliff in {LLM} Inference
               Fleets},
  journal   = {arXiv preprint},
  year      = {2026},
  note      = {Manuscript under review},
}

% ── LLM serving and scheduling ──────────────────────────────────────

@book{harchol2013performance,
  author    = {Mor Harchol-Balter},
  title     = {Performance Modeling and Design of Computer Systems:
               Queueing Theory in Action},
  publisher = {Cambridge University Press},
  year      = {2013},
}

@inproceedings{romero2021infaas,
  author    = {Francisco Romero and Qian Li and Neeraja J.\ Yadwadkar and
               Christos Kozyrakis},
  title     = {{INFaaS}: Automated Model-less Inference Serving},
  booktitle = {Proc. USENIX ATC},
  year      = {2021},
}

@inproceedings{gujarati2020serving,
  author    = {Arpan Gujarati and Reza Karimi and Safya Alzayat and
               Wei Hao and Antoine Kaufmann and Ymir Vigfusson and
               Jonathan Mace},
  title     = {Serving {DNNs} like Clockwork: Performance Predictability
               from the Bottom Up},
  booktitle = {Proc. OSDI},
  year      = {2020},
}

@inproceedings{shahrad2020serverless,
  author    = {Mohammad Shahrad and Rodrigo Fonseca and {\'I}nigo Goiri and
               Gohar Chaudhry and Paul Batum and Jason Cooke and
               Eduardo Laureano and Colby Tresness and Mark Russinovich and
               Ricardo Bianchini},
  title     = {Serverless in the Wild: Characterizing and Optimizing the
               Serverless Workload at a Large Cloud Provider},
  booktitle = {Proc. USENIX ATC},
  year      = {2020},
}

@inproceedings{li2023alpaserve,
  author    = {Zhuohan Li and Lianmin Zheng and Yinmin Zhong and
               Vincent Liu and Ying Sheng and Xin Jin and Yanping Huang and
               Zhifeng Chen and Hao Zhang and Joseph E. Gonzalez and
               Ion Stoica},
  title     = {{AlpaServe}: Statistical Multiplexing with Model
               Parallelism for Deep Learning Serving},
  booktitle = {Proc. OSDI},
  year      = {2023},
}

% ── Prompt compression ──────────────────────────────────────────────

@inproceedings{zheng2024lmsys,
  author    = {Lianmin Zheng and Wei-Lin Chiang and Ying Sheng and
               Tianle Li and Siyuan Zhuang and Zhanghao Wu and
               Yonghao Zhuang and Zhuohan Li and Zi Lin and Eric P. Xing
               and Joseph E. Gonzalez and Ion Stoica and Hao Zhang},
  title     = {{LMSYS-Chat-1M}: A Large-Scale Real-World {LLM} Conversation
               Dataset},
  booktitle = {Proc. ICLR},
  year      = {2024},
}

@inproceedings{jimenez2024swe,
  author    = {Carlos E. Jimenez and John Yang and Alexander Wettig and
               Shunyu Yao and Kexin Pei and Ofir Press and Karthik Narasimhan},
  title     = {{SWE}-bench: Can Language Models Resolve Real-world
               {G}it{H}ub Issues?},
  booktitle = {Proc. ICLR},
  year      = {2024},
}

@misc{yan2024bfcl,
  author       = {Fanjia Yan and Huanzhi Mao and Charlie Cheng-Jie Ji and
                  Tianjun Zhang and Shishir G. Patil and Ion Stoica and
                  Joseph E. Gonzalez},
  title        = {{BFCL}: Berkeley Function-Calling Leaderboard},
  howpublished = {\url{https://gorilla.cs.berkeley.edu/blogs/8_berkeley_function_calling_leaderboard.html}},
  year         = {2024},
}

@inproceedings{lewis2020rag,
  author    = {Patrick Lewis and Ethan Perez and Aleksandra Piktus and
               Fabio Petroni and Vladimir Karpukhin and Naman Goyal and
               Heinrich K{\"u}ttler and Mike Lewis and Wen-tau Yih and
               Tim Rockt{\"a}schel and Sebastian Riedel and Douwe Kiela},
  title     = {Retrieval-Augmented Generation for Knowledge-Intensive {NLP} Tasks},
  booktitle = {Proc. NeurIPS},
  year      = {2020},
}

@article{kimura1994mgc,
  author  = {Toshikazu Kimura},
  title   = {Two-Moment Approximations for the Mean Waiting Time in
             the {M/G/c} Queue},
  journal = {J. Oper. Res. Soc. Japan},
  volume  = {37},
  number  = {3},
  pages   = {238--256},
  year    = {1994},
}

% ── Fleet configuration, capacity planning, and heterogeneous GPU serving ─────

@article{xu2025aiconfigurator,
  author    = {Xu, Tianhao and Liu, Yiming and Lu, Xianglong and Zhao, Yijia
               and Zhou, Xuting and Feng, Aichen and Chen, Yiyi and others},
  title     = {{AIConfigurator}: Lightning-Fast Configuration Optimization
               for Multi-Framework {LLM} Serving},
  journal   = {arXiv:2601.06288},
  year      = {2025},
  url       = {https://arxiv.org/abs/2601.06288},
  note      = {Kernel-level performance DB (GEMM/attn/MoE/NCCL) across
               Ampere/Hopper/Blackwell; source of calibration constants
               in \texttt{fleet\_sim.hardware}},
}

@article{griggs2024melange,
  author    = {Tyler Griggs and Xiaoxuan Liu and Jiaxiang Yu and Doyoung Kim and
               Wei-Lin Chiang and Alvin Cheung and Ion Stoica},
  title     = {M\'{e}lange: Cost Efficient Large Language Model Serving by
               Exploiting {GPU} Heterogeneity},
  journal   = {arXiv:2404.14527},
  year      = {2024},
  url       = {https://arxiv.org/abs/2404.14527},
  note      = {Formulates heterogeneous GPU allocation as cost-aware bin packing;
               up to 77\% cost reduction vs.\ single GPU-type deployments},
}

@article{jia2025sageserve,
  author    = {Jia, Ningxin and others},
  title     = {{SageServe}: Optimizing {LLM} Serving on Cloud Data Centers
               with Forecast Aware Auto-Scaling},
  journal   = {arXiv:2502.14617},
  year      = {2025},
  url       = {https://arxiv.org/abs/2502.14617},
  note      = {ILP-based routing + VM auto-scaling for 10M+ daily requests;
               25\% GPU-hour savings at Microsoft Office 365},
}

% ── Disaggregated prefill/decode serving ─────────────────────────────────────

@inproceedings{zhong2024distserve,
  author    = {Yinmin Zhong and Shengyu Liu and Junda Chen and Jianbo Hu and
               Yibo Zhu and Xuanzhe Liu and Xin Jin and Hao Zhang},
  title     = {{DistServe}: Disaggregating Prefill and Decoding for
               Goodput-Optimized Large Language Model Serving},
  booktitle = {Proc. OSDI},
  year      = {2024},
  url       = {https://arxiv.org/abs/2401.09670},
  note      = {Founding paper of PD disaggregation; 7.4$\times$ more requests
               or 12.6$\times$ tighter SLO vs.\ colocated serving},
}

@article{tokenscale2024,
  author    = {Dong, Chenhe and others},
  title     = {{TokenScale}: Timely and Accurate Autoscaling for Disaggregated
               {LLM} Serving with Token Velocity},
  journal   = {arXiv:2512.03416},
  year      = {2024},
  url       = {https://arxiv.org/abs/2512.03416},
  note      = {Token Velocity as a leading indicator for proactive PD scaling;
               improves SLO attainment from 50--88\% to 80--96\%},
}

% ── LLM inference simulation ──────────────────────────────────────────────────

@misc{vidur2024,
  author  = {Agrawal, Amey and Kedia, Nitin and Panwar, Ashish and
             Mohan, Jayashree and Kwatra, Nipun and Gulavani, Bhargav S and
             Ramjee, Ramachandran and Tumanov, Alexey},
  title   = {{Vidur}: A Large-Scale Simulation Framework for {LLM} Inference},
  year    = {2024},
  journal = {arXiv:2405.05465},
  url     = {https://arxiv.org/abs/2405.05465},
  note    = {Single-instance high-fidelity DES covering KV-cache, scheduling,
             and preemption; complementary scope to this fleet-level simulator},
}

@misc{chen2026fleetsim,
  author  = {{vLLM Semantic Router Team}},
  title   = {{vLLM Semantic Router Fleet Sim}: A Generic Discrete-Event Simulator
             for Heterogeneous {LLM} {GPU} Fleet Capacity Planning},
  year    = {2026},
  url     = {https://github.com/vllm-project/semantic-router/tree/main/src/fleet-sim},
  note    = {Open-source; validates fleet provisioning models via
             request-level M/G/$c$ discrete-event simulation},
}


@article{chen2026fleetopt,
  author  = {Huamin Chen and Xunzhuo Liu and Junchen Jiang and
             Bowei He and Xue Liu},
  title   = {{FleetOpt}: Workload-{CDF}-Aware {LLM} {GPU} Fleet
             Provisioning via Co-Design of Pool Boundaries
             and Compression Policy},
  journal = {arXiv},
  year    = {2026},
}

@article{kokolis2024reliability,
  author  = {Apostolos Kokolis and Michael Kuchnik and John Hoffman and
             Adithya Kumar and Parth Malani and Faye Ma and
             Zachary DeVito and Shubho Sengupta and Kalyan Saladi and
             Carole-Jean Wu},
  title   = {Revisiting Reliability in Large-Scale Machine Learning
             Research Clusters},
  journal = {arXiv},
  volume  = {2410.21680},
  year    = {2024},
  url     = {https://arxiv.org/abs/2410.21680},
  note    = {Failure-rate data: RSC-1 $r_f{=}6.50$/1000 node-days,
             RSC-2 $r_f{=}2.34$/1000 node-days; MTTF at 1024 GPUs
             ${\approx}7.9$\,h; basis for \texttt{node\_availability()}},
}

@article{cui2025twogpus,
  author  = {Shengkun Cui and Archit Patke and Hung Nguyen and
             Aditya Ranjan and Ziheng Chen and Phuong Cao and
             Gregory Bauer and Brett Bode and Catello {Di Martino} and
             Saurabh Jha and Chandra Narayanaswami and Daby Sow and
             Zbigniew T. Kalbarczyk and Ravishankar K. Iyer},
  title   = {Story of Two {GPU}s: Characterizing the Resilience of
             {Hopper H100} and {Ampere A100} {GPU}s},
  journal = {arXiv},
  volume  = {2503.11901},
  year    = {2025},
  url     = {https://arxiv.org/abs/2503.11901},
  note    = {11.7M GPU-hours on Delta (1,056 A100+H100 GPUs);
             H100 has 3.2$\times$ worse memory MTBE but better hardware
             resilience than A100; recommends ${\sim}5\%$ overprovisioning
             ($\texttt{H100\_AVAIL\_5PCT}=0.95$);
             MTTR is failure-type-driven, not GPU-model-driven},
}

% ── Grid flexibility / demand response ───────────────────────────────

@article{hassan2025g2g,
  author  = {M. Hassan and J. Lin and D. Kim and R. Bhatt and
             Y. Wang and N. Li and S. Grijalva},
  title   = {{GPU-to-Grid}: Coupling {LLM} Inference with Power System Control},
  journal = {arXiv:2602.05116},
  year    = {2025},
  url     = {https://arxiv.org/abs/2602.05116},
  note    = {Shows vLLM max\_num\_seqs is the primary GPU power knob;
             power follows a logistic curve vs.\ log2(batch size);
             data from ML.ENERGY Benchmark on H100-SXM5},
}

@inproceedings{chung2025mlenergy,
  author    = {Jae-Won Chung and Woosuk Kwon and Ion Stoica},
  title     = {{LLM-Pilot}: Characterize and Optimize Inference of {LLMs}
               on Dedicated {GPU} Servers},
  booktitle = {Proc. NeurIPS Datasets and Benchmarks Track},
  year      = {2025},
  url       = {https://ml.energy/leaderboard},
  note      = {ML.ENERGY Benchmark v3.0: measured H100-SXM5 GPU power
               vs.\ batch size (1--256) running vLLM on Llama-3.1 variants.
               Idle (batch$\approx$1) $\approx$300\,W;
               saturated (batch=128) $\approx$600\,W for 70B-class model},
}
