arXiv (All)
open access
Stable Token-level Scheduling for Serverless LLM Inference
Serverless inference is emerging as an efficient deployment model for Large Language Models (LLMs) due to its ability to elastically scale resources, improve GPU utilization through resource sharing, and reduce infrastructure costs. However, existing approaches primarily optimize time to first token
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
- Blink: CPU-Free LLM Inference by Delegating the Serving Stack to GPU and SmartNIC#2544
- GeoMesh: Workload-Balanced and Sign-Compressed Geo-Distributed LLM Training#1012574
- GeoMesh: Workload-Balanced and Sign-Compressed Geo-Distributed LLM Training#965385
- Fast Recovery for LLM Serving via Decoupled Device Memory Lifetime in Dynamo#1061091
- Fast Recovery for LLM Serving via Decoupled Device Memory Lifetime in Dynamo#1043071
Record · ID 1342899
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