arXiv AI By Jannatun Noor, Athar Noor Mohammad Rafee, Md. Sadman Haque, Sreezon Das Gupta, Atiar Osman, Md. Abu Obaida Zishan

LLMs in Cloud: A Discussion on Architecture, Orchestration, and Inference

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arXiv AI
Jun 29

End-to-End Dynamic Sparsity for Resource-Adaptive LLM Inference

arXiv:2606. 27743v1 Announce Type: cross Abstract: Large Language Models (LLMs) inference is typically deployed under a static resource assumption, where models execute a fixed computational graph regardless of the runtime environment.

By Yuhang Chen, Jinhao Duan, Ruichen Zhang, Mingfu Liang, Xiaohan Wei, Yunchen Pu, Fei Tian, Chonglin Sun, Parish Aggarwal, Frank Shyu, Luke Simon, Sandeep Pandey, Tianlong Chen, Xi Liu
arXiv AI
3d ago

InferSlice: A Reference Architecture for SLO-driven LLM Inference over a Telco Wide-Area Network

InferSlice is a reference architecture that enables Service-Level Objective (SLO)-driven large language model (LLM) inference across geographically distributed telco infrastructure. It integrates network slicing, GPU partitioning, elastic serving, and observability to address sovereignty, compliance, latency, and resource‑locality constraints that limit centralized cloud deployments. The architecture was evaluated on the JOINER UK national‑scale wide‑area network, demonstrating how different parallelism strategies and resource placements affect latency‑throughput regimes and scale‑out times.

By Juan Parra-Ullauri, Anthony Chiu, Hari Madhukumar, Zhaozhou Wu, Anderson Bravalheri, Alex Healing, Andy Corston-Petrie, Dimitra Simeonidou, Nektaria Efthymiou
arXiv Machine Learning
Sep 11

Optimizing AI Inference Across the Deployment Stack

The paper argues that AI deployment performance depends on interactions among compression, compiler transformations, and serving policies rather than just model architecture. It introduces a three‑layer taxonomy—model‑level techniques, compiler transformations, and system policies—and frames deployment as a constrained multi‑objective optimization problem over accuracy, latency, throughput, memory footprint, and energy. The authors propose an evidence protocol for comparable benchmarking and synthesize data from edge and data‑center platforms to show that cross‑layer interactions drive deployment outcomes, concluding with a constraint‑aware selection procedure and open research problems.

By Tejinder Singh, John Pflueger, Jeebak Mitra, Robert Lincourt, Mitchell Markow, Bhavesh A. Patel
arXiv AI
Jul 7

Data Driven Optimization of GPU efficiency for Distributed LLM-Adapter Serving

arXiv:2602. 24044v2 Announce Type: replace-cross Abstract: Large Language Model (LLM) adapters enable low-cost model specialization, but introduce complex caching and scheduling challenges in distributed serving systems where hundreds of adapters must be hosted concurrently.

By Ferran Agullo, Joan Oliveras, Chen Wang, Alberto Gutierrez-Torre, Olivier Tardieu, Alaa Youssef, Jordi Torres, Josep Ll. Berral