arXiv (All)
open access
COPE: Composable Performance Emulation for Mixture-of-Experts Offloading Policies
As LLMs scale, mixture-of-experts (MoE) architectures increase model capacity while keeping per-token computation manageable through sparse expert activation. This sparsity also enables memory-efficient deployment by retaining most expert weights in CPU memory and transferring or executing only the
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
- COPE: Composable Performance Emulation for Mixture-of-Experts Offloading Policies#1450763
- Who Should Own the Expert Cache? Kernel-Managed Tiering for Trillion-Parameter MoE Inference#1043174
- Who Should Own the Expert Cache? Kernel-Managed Tiering for Trillion-Parameter MoE Inference#793848
- BaGuaLu-2: Taming Dynamic Memory Fluctuation for Efficient MoE Model Training over 10,000 GPUs#1387352
- BaGuaLu-2: Taming Dynamic Memory Fluctuation for Efficient MoE Model Training over 10,000 GPUs#1374998
Record · ID 1455885
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