Adapting organizational IT-solutions for local large language model support

dc.contributor.authorMäkelä, Tuukka
dc.contributor.departmentfi=Johtamisen ja yrittäjyyden laitos|en=Department of Management and Entrepreneurship|
dc.contributor.facultyfi=Turun kauppakorkeakoulu|en=Turku School of Economics|
dc.contributor.studysubjectfi=Tietojärjestelmätiede|en=Information Systems Science|
dc.date.accessioned2025-06-30T21:05:45Z
dc.date.available2025-06-30T21:05:45Z
dc.date.issued2025-06-04
dc.description.abstractThis research aims to broaden understanding on the topic of localized large language models (LLMs) in organizational contexts. As a technology, these can offer a lot of advantages, that are suitable for the corporate world. Most notably, these offer significant benefits for controlling security related aspects – as such a system can be ran completely in-house, without any organizational and potentially sensitive data leaving the full control of a given organization. Due to the demands of especially higher-end LLMs, most organizations are not prepared and ready to start supporting them on their pre-existing hardware structures. Whilst it’s normal for most organizations to have deployed computing solutions of some degree within their whole organization – due to the unique and ML-heavy focus of LLM-based workloads, these may not be compatible, forcing organizations to re-think their IT-solutions in order to begin utilizing localized LLMs. This research has been completed on the basis of a few different methodologies and stages. Firstly, the driving factors and key trends impacting these developments were investigated with the aid of a systematic gray-literature review. After understanding these factors, the research aimed to understand the functionalities and requirements of a few key tiers of LLMs, through the completion of practical benchmark runs, as well as third-party benchmark data. On the basis of these findings, the research is completed with a list of suggestions and factors of different tiers of LLM-systems. These include factors such as requirements and benefits an organization may expect to achieve from such systems. These solutions have been made on the principles of a design science-based artifact – giving anyone seeking to utilize these findings a solid foundation for understanding and completing such a project in a sensible and cost-effective manner. The artifact built within this research includes methods for planning such projects, and details about phases and tasks which should be completed to ensure a successful deployment takes place.
dc.format.extent140
dc.identifier.olddbid199509
dc.identifier.oldhandle10024/182540
dc.identifier.urihttps://www.utupub.fi/handle/11111/12654
dc.identifier.urnURN:NBN:fi-fe2025063075758
dc.language.isoeng
dc.rightsfi=Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty.|en=This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.|
dc.rights.accessrightsavoin
dc.source.identifierhttps://www.utupub.fi/handle/10024/182540
dc.subjectLocalized LLMs, Machine Learning, Artificial Intelligence, data-security, open-source models
dc.titleAdapting organizational IT-solutions for local large language model support
dc.type.ontasotfi=Pro gradu -tutkielma|en=Master's thesis|

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