arXiv (All)
open access
MOLT: A Fine-Grained GPU Memory Sharing System for LLM Serving with Opportunistic Fine-Tuning
Large language model (LLM) serving scales its replica count with the request load, yet GPU memory still stands idle inside the replicas. Adding a replica takes minutes, while the memory that a replica needs changes within seconds. Even instant autoscaling could not return this idle memory, because t
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
- MOLT: A Fine-Grained GPU Memory Sharing System for LLM Serving with Opportunistic Fine-Tuning#1194389
- A Deterministic Constant-Competitive Algorithm for Dynamic Mixture-of-Experts Serving#792807
- A Deterministic Constant-Competitive Algorithm for Dynamic Mixture-of-Experts Serving#788803
- RW-TTT: Batched Serving for Request-Owned Test-Time Training State#926051
- Optimizing Effective Training Time for Large-Scale Recommendation Systems#1157405
Record · ID 1201761
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