VQV Signal
CEMA-Router addresses the challenge of routing LLM inference requests across heterogeneous cloud-edge resources with varying computational capacity, energy efficiency, and network conditions. It balances trade-offs among response quality, cost, energy use, and batch completion time to improve infer...
CEMA-Router addresses the challenge of routing LLM inference requests across heterogeneous cloud-edge resources with varying computational capacity, energy efficiency, and network conditions. It balances trade-offs among response quality, cost, energy use, and batch completion time to improve infer...
AI-assisted summary based on the listed source.
Large language models (LLM) are increasingly integrated into pervasive applications spanning heterogeneous cloud-edge resources. These heterogeneous resources differ in computational capacity, energy efficiency, network conditions, and deployed model quality, creating trade-offs among response quality, monetary...
As LLMs become pervasive in applications spanning cloud and edge environments, efficient routing that considers infrastructure differences is key to optimizing performance and resource use. CEMA-Router's approach helps manage these trade-offs for better overall system efficiency.
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VQV surfaced this signal because it is recent, relevant to LLM Inference, connected to arXiv.
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