arXiv (All)
open access
Learning-Based Collaborative MEC for LLM Inference with Soft-Deadline Awareness via Transformer-Enhanced PPO
This paper investigates collaborative mobile edge computing (MEC) servers for large language model (LLM) inference under soft deadline constraints. In this system, to improve the quality of service, computations are expected to be completed within their deadlines. However, due to dependencies among
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- The Battery Price of edge AI: A study of the Environmental Impact of LLM Inference on Mobile Devices#999925
- AceSpec: An Asymmetric Edge-Cloud Collaborative Framework for Communication-Efficient LLM Inference#924677
- AceSpec: An Asymmetric Edge-Cloud Collaborative Framework for Communication-Efficient LLM Inference#920443
- Denoising Surface: Modeling and Predicting Inference Cost for Diffusion LLM Serving#1155434
- TokenWeave: Efficient Compute-Communication Overlap for Distributed LLM Inference#189269
Record · ID 926145
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive