arXiv (OAI Expanded)
open access
AceSpec: An Asymmetric Edge-Cloud Collaborative Framework for Communication-Efficient LLM Inference
Deploying Large Language Models (LLMs) on edge devices typically relies on model compression or split inference. However, compression degrades reasoning capabilities, while split inference suffers from severe Wide Area Network (WAN) communication bottlenecks. Edge-cloud speculative decoding emerges
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
- AceSpec: An Asymmetric Edge-Cloud Collaborative Framework for Communication-Efficient LLM Inference#924677
- TokenWeave: Efficient Compute-Communication Overlap for Distributed LLM Inference#189269
- Learning-Based Collaborative MEC for LLM Inference with Soft-Deadline Awareness via Transformer-Enhanced PPO#926145
- SiDP: Memory-Efficient Data Parallelism for Offline LLM Inference#238580
- The Battery Price of edge AI: A study of the Environmental Impact of LLM Inference on Mobile Devices#999925
Record · ID 920443
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