# Architectural Foundations for the Large Language Model Infrastructures

arXiv: https://arxiv.org/abs/2408.09205 · HTML: https://mlsanity.com/paper/2408.09205/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 6 references that are in the map.
- First posted on arXiv 17 Aug 2024.

## Builds on (most cited first, 2 of 2)

- [Pre-training Language Model Incorporating Domain-specific Heterogeneous Knowledge into A Unified Representation](https://mlsanity.com/paper/2109.01048/) (2021)
- [MetaAID 2.5: A Secure Framework for Developing Metaverse Applications via Large Language Models](https://mlsanity.com/paper/2312.14480/) (2023)

## Abstract (arXiv)

The development of a large language model (LLM) infrastructure is a pivotal undertaking in artificial intelligence. This paper explores the intricate landscape of LLM infrastructure, software, and data management. By analyzing these core components, we emphasize the pivotal considerations and safeguards crucial for successful LLM development. This work presents a concise synthesis of the challenges and strategies inherent in constructing a robust and effective LLM infrastructure, offering valuable insights for researchers and practitioners alike.