Year: 2024
Type: article
Abstract: Large Language Models (LLMs) when used in business settings face challenges related to storage infrastructure that have an influence, on performance efficiency and costs as well as scalability aspects. The examination here delves into the recommended methods and structural elements to consider when setting up storage solutions for LLMs in enterprise scenarios with a spotlight on implications for decision makers and tech experts. Based on studies and real-world encounters in the industry, we explore how tailored storage setups can substantially lessen inference delays while also leading to cost reductions in enterprise LLM implementation. Keywords: Large Language Models, Enterprise Storage, Model Weights, Vector Databases, Storage Optimization, MLOps, AI Infrastructure
DOI: 10.55041/ijsrem36175
Authors: Senior Technical Marketing Engineer, Prabu Arjunan
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4404645544