arXiv (All)
open access
Cobalt: Leveraging Expert Co-activation for Efficient Distributed MoE Training
Mixture-of-Experts (MoE) has increasingly become a mainstream approach for scaling large language models, as it expands model capacity while keeping computation cost nearly constant. Training large-scale MoE models relies on Expert Parallelism (EP), which distributes expert replicas across GPUs and
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
- Cobalt: Leveraging Expert Co-activation for Efficient Distributed MoE Training#1122261
- HERO-MoE: Historical Expert Routing with Scale-Preserving Fusion#1116912
- Moebius: Serving Mixture-of-Expert Models with Seamless Runtime Parallelism Switch#310786
- Training-Free Halving of Activated Experts in Fine-Grained Mixture-of-Experts Models#927851
- MoEGen: Mixture-of-Experts for Instance-Adaptive LoRA Generation#1131632
Record · ID 1132160
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