arXiv (All)
open access
CRISP: Co-designing Pruning and Scheduling for Efficient MoE Inference on Edge Servers
Mixture-of-Experts (MoE) inference on memory-constrained edge platforms is often dominated by expert-weight movement rather than computation. Although each token activates only a small subset of experts, routing is input-dependent, so all experts must remain accessible and memory movement dominates
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
- MoEless: Efficient MoE LLM Serving with Serverless Experts#1142637
- SlimWise: Decoupling Expert Pruning Across Prefill and Decode for Efficient MoE Serving#1119190
- 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
- Joint Effects of GPU Server Topology, Parallelism, and Congestion Control on MoE Inference: A Controlled Simulation Study#1142136
Record · ID 1469045
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