LLM Inference at the Edge: Mobile, NPU, and GPU Performance Efficiency Trade-offs Under Sustained Load
arXiv 2603.23640•35ba025c414c034b203b4b6d3b0e95d7ab96a15de119e78eb076765bdba35d6b
BFTCloudFormerSybil-resistanceblockchainconsensuscross-chaindosedge-inferenceerasure-codinggpuhpcidentitylayer-1llmmpimulti-gpunpunvmon-chain-slashingpeer-discoveryperformance-predictionprivacyresource-exhaustionstate-availabilitythermal-throttling
Paper metadata
- arXiv ID
- 2603.23640
- 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
- 35ba025c414c034b203b4b6d3b0e95d7ab96a15de119e78eb076765bdba35d6b
- Enrichment time
- 2026-03-26T08:52:23Z
- AI-assisted enrichment
- Yes
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