arXiv (All)
open access
Mira: Memory-Efficient MoE Inference Using Adaptive Caching and Predictive Expert Staging
Mixture-of-Experts (MoE) models are a compelling architecture for scaling model capacity, making them especially attractive for deployment on resource-constrained, single-GPU systems. However, this benefit is difficult to realize because expert parameters dominate memory, and token-level routing is
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
- Mira: Memory-Efficient MoE Inference Using Adaptive Caching and Predictive Expert Staging#1122291
- DynaNDE: Dynamic Near-Data Expert Scheduling for Batched MoE Inference#819855
- DynaNDE: Dynamic Near-Data Expert Scheduling for Batched MoE Inference#809859
- Fengshui: Demystifying Chiplet Ecosystem and Bespoke Neural Network Accelerator Codesign#998542
- FlashMoE: Fast Distributed MoE in a Single Kernel#1142540
Record · ID 1142386
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