VQV Signal
This paper addresses the challenge of low-latency inference for large language models deployed across distributed edge servers, focusing on time-varying server selection amid heterogeneous resources. It proposes methods to optimize request scheduling by balancing load and minimizing end-to-end late...
This paper addresses the challenge of low-latency inference for large language models deployed across distributed edge servers, focusing on time-varying server selection amid heterogeneous resources. It proposes methods to optimize request scheduling by balancing load and minimizing end-to-end late...
AI-assisted summary based on the listed source.
Large language model (LLM)-powered agentic AI services increasingly demand low-latency inference, motivating the deployment of LLMs across distributed edge servers. However, heterogeneous communication and computing capabilities, together with dynamically evolving inference states, make the edge server selection...
Efficiently managing inference requests on edge servers is critical for delivering responsive AI services that rely on large language models. This work contributes to improving performance in real-world deployments where communication and computing capabilities vary dynamically.
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Signal Strength reflects source quality, relevance, freshness and evidence. Public Interest helps organize discovery; it is not proof of truth.
VQV surfaced this signal because it is recent, relevant to LLM Inference, connected to arXiv.
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