arXiv (All)
open access
FlashMoE: Fast Distributed MoE in a Single Kernel
The computational sparsity of Mixture-of-Experts (MoE) models enables sub-linear growth in compute cost as model size increases, thus offering a scalable path to training massive neural networks. However, existing implementations suffer from low GPU utilization, significant latency overhead, and a f
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
- Mixture-of-Kittens: MoE Megakernel for NVL72s#1130912
- Mixture-of-Kittens: MoE Megakernel for NVL72s#1122270
- mKernel: Fast Multi-GPU, Multi-Node Fused Kernels#1071362
- mKernel: Fast Multi-GPU, Multi-Node Fused Kernels#919318
- Mira: Memory-Efficient MoE Inference Using Adaptive Caching and Predictive Expert Staging#1142386
Record · ID 1142540
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