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Stream/Area:
-
Subject:
-
Paper ID:
10.55041/IJSREM36175
Volume:
8 |
6 |
Jun 2024
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
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
Publication Date
11/22/2024
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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