From Servers to Sites: Compositional Power Trace Generation of LLM Inference for Infrastructure Planning
arXiv 2603.18383•c04478994cac785b37742cb54ebfcef2f9ae6d2e984e1506dbf188dc868b3a6a
GPU-computingLLM-inferenceadversarial-machine-learningdatacenter-infrastructureedge-cloudfederated-learninglabel-flippingmachine-unlearningmodel-integrityoperational-analyticspoisoning-attackspeculative-decoding
Paper metadata
- arXiv ID
- 2603.18383
- Version
- Not specified by this published record
- Category
- Computer Science — Distributed, Parallel, and Cluster Computing (cs.DC)
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Evidence and limitations
- Source ID
- arxiv_cs_dc
- Record identifier
- c04478994cac785b37742cb54ebfcef2f9ae6d2e984e1506dbf188dc868b3a6a
- Enrichment time
- 2026-08-16T13:05:27Z
- AI-assisted enrichment
- Yes
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