arXiv (All)
open access
MoSE: Mode-Switching Expander for Mixed LLM Training and Inference
AI clusters increasingly run large language model (LLM) inference and training on the same fabric. Prefill-decode (P-D) disaggregation creates key-value (KV) cache transfers between prefill and decode groups, whereas training collectives and all-to-all traffic benefit from near-uniform global connec
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
- MoSE: Mode-Switching Expander for Mixed LLM Training and Inference#1136800
- When Does Disaggregation Pay? Simulating Prefill--Decode--Attention--FFN Specialization for Agentic LLM Inference#429326
- NetKV: Network-Aware Decode Instance Selection for Disaggregated LLM Inference#259408
- Don't Drop Dropout: Optimizing Layer Sparsity for Efficient LLM Training and Inference#972211
- Understanding LLM Parameter Update Sparsity through the Lens of Fisher#1131104
Record · ID 1144105
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