arXiv (All)
open access
Accelerating Dense LLMs via L0-regularized Mixture-of-Experts
Large language models (LLMs) achieve strong performance but suffer from slow and costly inference. Existing acceleration methods often lead to noticeable performance degradation, while Mixture-of-Experts (MoE) models require extensive computational resources. In this paper, we propose L0-MoE, a ligh
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
- Accelerating Dense LLMs via L0-regularized Mixture-of-Experts#1006905
- CORTEX: Learning to Share and Specialize in Dense Language Models#1128719
- Rethinking Visual Token Compression for Video Large Language Models: A Simple Yet Strong Baseline#1129983
- DynaNDE: Dynamic Near-Data Expert Scheduling for Batched MoE Inference#819855
- DynaNDE: Dynamic Near-Data Expert Scheduling for Batched MoE Inference#809859
Record · ID 1036065
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