Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities
arXiv 2604.22906•3e8a03de51e84ee85177a08dd660603d186267549a4667193223121a78e4018b
CRDTClusterFusion++GPU-optimizationKeyhiveLLM edge inferenceMEVMatrixSDSL-SolverTACOaccess-controlatomic-simulationavailabilityblockchainclient-exclusioncommunication-compressiondistributed-trainingedge-computingfederated-learningformal-verificationkubernetesnumerical-stabilityprivacyspot-instancestransaction-ordering
Paper metadata
- arXiv ID
- 2604.22906
- 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
- 3e8a03de51e84ee85177a08dd660603d186267549a4667193223121a78e4018b
- Enrichment time
- 2026-04-28T08:52:26Z
- AI-assisted enrichment
- Yes
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