Beyond the Cloud: Assessing the Benefits and Drawbacks of Local LLM Deployment for Translators

TL;DR

This paper evaluates the performance and benefits of deploying open-source large language models locally for translation tasks, emphasizing privacy, data control, and accessibility for individual users and small businesses.

Contribution

It provides an assessment of three open-source LLMs on CPU platforms, highlighting their viability as alternatives to cloud-based AI solutions for translators.

Findings

Local models enhance data privacy and control.

Open-source models are feasible on CPU platforms.

Local deployment reduces reliance on cloud services.

Abstract

The rapid proliferation of Large Language Models presents both opportunities and challenges for the translation field. While commercial, cloud-based AI chatbots have garnered significant attention in translation studies, concerns regarding data privacy, security, and equitable access necessitate exploration of alternative deployment models. This paper investigates the feasibility and performance of locally deployable, free language models as a viable alternative to proprietary, cloud-based AI solutions. This study evaluates three open-source models installed on CPU-based platforms and compared against commercially available online chat-bots. The evaluation focuses on functional performance rather than a comparative analysis of human-machine translation quality, an area already subject to extensive research. The platforms assessed were chosen for their accessibility and ease of use across various operating systems. While local deployment introduces its own challenges, the benefits of enhanced data control, improved privacy, and reduced dependency on cloud services are compelling. The findings of this study contribute to a growing body of knowledge concerning the democratization of AI technology and inform future research and development efforts aimed at making LLMs more accessible and practical for a wider range of users, specifically focusing on the needs of individual translators and small businesses.