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New paper reviews cloud LLM architecture, orchestration, and inference

By PulseAugur Editorial · [1 sources] ·

A new paper published on arXiv details a comprehensive review of Large Language Model (LLM) deployment in cloud data centers. It addresses challenges such as High Bandwidth Memory limitations, KV cache expansion, and serverless cold starts. The paper proposes a cross-layer taxonomy unifying hardware virtualization, cluster scheduling, and runtime inference optimizations, and evaluates resilient cloud-native service architectures. It also outlines a future research roadmap focusing on Compute Express Link memory pooling and multi-agent control planes. AI

IMPACT Provides a structured overview of challenges and future directions for deploying LLMs in cloud infrastructure.

RANK_REASON The cluster contains a single academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New paper reviews cloud LLM architecture, orchestration, and inference

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The cluster contains a single academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · 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

    arXiv:2610.33475v1 Announce Type: cross Abstract: The widespread deployment of foundational Large Language Models (LLM) across cloud datacenters marks a fundamental paradigm shift from stateless deep learning toward distributed, memory-intensive generative workloads. However, ser…

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