arXiv (OAI Expanded)
open access
BigMoMo: Efficient Inference of Large-Scale MoE with Speculative Decoding on Mobile Devices
Mixture-of-Experts (MoE) models expand language model capacity on smartphones, but expert offloading remains constrained by limited DRAM capacity and costly data movement. Sequential token routing couples expert execution to fragmented flash reads and multistage NPU preparation, leaving sparse compu
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
- BigMoMo: Efficient Inference of Large-Scale MoE with Speculative Decoding on Mobile Devices#919358
- Mira: Memory-Efficient MoE Inference Using Adaptive Caching and Predictive Expert Staging#1142386
- Mira: Memory-Efficient MoE Inference Using Adaptive Caching and Predictive Expert Staging#1122291
- CascadeEP: Asynchronous Expert Execution for MoE Prefill under Attention Imbalance#1117964
- CascadeEP: Asynchronous Expert Execution for MoE Prefill under Attention Imbalance#1108732
Record · ID 1084578
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