arXiv (All)
open access
Geometry-Aware Online Scheduling for LLM Serving: From Theoretical Bound to System Practice
The rapid growth of interactive Large Language Model serving has made efficient management of dynamic Key-Value cache footprints increasingly important for inference performance. Modern inference systems overwhelmingly rely on time-centric scheduling heuristics, such as Shortest Job First. However,
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
- Towards Multi-Model LLM Schedulers: Empirical Insights into Offloading and Preemption#798900
- Towards Multi-Model LLM Schedulers: Empirical Insights into Offloading and Preemption#797486
- PackServe: SLO-Aware Request Scheduling for Agentic LLM Serving at Scale#1117936
- PackServe: SLO-Aware Request Scheduling for Agentic LLM Serving at Scale#1108733
- The Weight Is Over - Interactive Diffusion on Consumer GPUs#1036238
Record · ID 1233897
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