arXiv (OAI Expanded)
open access
Quality-Constrained Routing over a Fixed Pool of Quantized Mixture-of-Experts Instances
Quantized Mixture-of-Experts (MoE) services can hold several pre-materialized instances of one base model, but quantization damage varies sharply across requests and bitwidths. Because instance materialization and replica counts consume memory and require slow reconfiguration, we treat them as upstr
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
- Loss-tolerant distributed lattice surgery using fusion networks#1157194
- A Fair Benchmarking of Deep Relational Database Learning Models#343580
- TOWARDS DATA-EFFICIENT DEEP LEARNING#254778
- Knowledge Bases and Neural Network Synthesis#717420
- Dual-GNN Multilevel Coarsening for Maximum Independent Set#1051642
Record · ID 1070042
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