arXiv CS
open access
Democratizing MoE inference on commodity GPUs with CoMoE
Deploying Mixture-of-Experts (MoE) models relies heavily on Expert Parallelism, which generates intense inter-GPU communication. Consequently, state-of-the-art inference systems require high-bandwidth, P2P interconnects (e.g., NVLink) in datacenter GPUs to handle massive token routing, making deploy
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
- Democratizing MoE inference on commodity GPUs with CoMoE#1233463
- FluxMoE: Decoupling Expert Residency for High-Performance MoE Serving#998172
- Cascadia: Resident 975B MoE Inference on Eleven AI PCs#1216594
- Cascadia: Resident 975B MoE Inference on Eleven AI PCs#1210270
- Mixture-of-Kittens: MoE Megakernel for NVL72s#1130912
Record · ID 1227305
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