arXiv AI June 24, 2026 By Jiahuan Yu, Aryan Taneja, Junfeng Lin, Minjia Zhang

VoltanaLLM: Energy-Efficient and SLO-Aware Disaggregated LLM Serving via Adaptive Frequency Control and State-Space Routing

arXiv:2509. 04827v3 Announce Type: replace-cross Abstract: The energy cost of Large Language Model (LLM) inference is rapidly becoming a barrier to sustainable and scalable deployment.

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llms

arXiv AI

Jun 17

Prefill/Decode-Aware Evaluation of LLM Inference on Emerging AI Accelerators

arXiv:2606. 17104v1 Announce Type: cross Abstract: As large language models (LLMs) are increasingly deployed in latency- and cost-sensitive settings, inference efficiency has become a central systems challenge.

By Shun Usami, Venkatram Vishwanath, E. Wes Bethel

llms efficiency

arXiv Machine Learning

Aug 31

Characterization of Request and Token Energy Costs for LLM Inference Workloads on GPU Platforms

arXiv:2608.28044v1 Announce Type: cross Abstract: Large language model (LLM) inference serving is priced by tokens, but GPU energy is consumed over inference windows. This accounting mismatch makes t...

By Prabhu Vellaisamy, Vanessa Lam, Shawn Blanton, John Paul Shen

llms

arXiv Machine Learning

Aug 21

RequestRouter: Request-Boundary Routing for Efficient Single-GPU LLM Inference

arXiv:2605. 23057v2 Announce Type: replace Abstract: RequestRouter is a lightweight request-boundary controller for reducing the latency and energy cost of single-GPU large language model inference.

By Aman Sunesh, Ali Alshehhi, Hivansh Dhakne

llms efficiency benchmarks

arXiv AI

Jun 16

Frontier: Towards Comprehensive and Accurate LLM Inference Simulation

arXiv:2605. 21312v2 Announce Type: replace-cross Abstract: Modern LLM serving is no longer homogeneous or monolithic.

By Yicheng Feng, Xin Tan, Yangtao Deng, Yimin Jiang, Yibo Zhu, Hong Xu

llms agents reinforcement-learning efficiency benchmarks

arXiv Computation and Language

Sep 10

PELM: Power Efficient On-Device LLM Inference with Speculative Decoding and Dynamic Voltage Frequency Scaling

arXiv:2609.09662v1 Announce Type: cross Abstract: Deploying Large Language Models (LLMs) directly on mobile platforms at the edge is gaining traction due to a myriad of benefits, such as increased pr...

By Weisi Yang, Stephen Xia

llms benchmarks safety

arXiv Machine Learning

Jul 8

Energy-Efficient GPU DVFS for Fine-Tuning of SLMs on Resource-constrained Embedded Devices

arXiv:2607. 05933v1 Announce Type: cross Abstract: Dynamic Voltage Frequency Scaling (DVFS) on resource-constrained embedded GPU platforms is essential for energy-efficient small language model (SLM) fine-tuning, as privacy- and personalization-driven adaptation increasingly requires local execution and involves repeated forward-backward optimization over many mini-batches, making it substantially more time- and energy-intensive than single-pass inference.

By Jurn-Gyu Park, Sanzhar Zholdybayev, Aidar Amangeldi, Ademi Zhanuzakova

llms fine-tuning benchmarks safety

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