Calhoun
metadata only
Balancing Accuracy and Cost: Trade-Offs in Large Language Model Quantization for the Systems Engineering Domain
As Large Language Models (LLMs) are increasingly deployed within the systems engineering domain, optimizing these models to balance performance accuracy and cost for given computational resources becomes essential. One process for finding the right balance is quantization, a process that involves co
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
- Similarity All The Way Up: Multilingual Generalization in LLMs Relies on Language-Level Similarity Structures#1078946
- Training Compute-Optimal Large Language Models#1317989
- Structure Tax: How Structured Output affects LLMs Performance#1249480
- Objects Without Morphisms: What LLMs for Mathematics Do Not Represent#1188229
- Objects Without Morphisms: What LLMs for Mathematics Do Not Represent#1181641
Record · ID 1349133
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