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

arXiv: https://arxiv.org/abs/2507.23399 · HTML: https://mlsanity.com/paper/2507.23399/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 31 Jul 2025.

## Builds on (most cited first, 7 of 7)

- [A causal framework for explaining the predictions of black-box sequence-to-sequence models](https://mlsanity.com/paper/1707.01943/) (2017)
- [Multilingual Machine Translation with Open Large Language Models at Practical Scale: An Empirical Study](https://mlsanity.com/paper/2502.02481/) (2025)
- [Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectives](https://mlsanity.com/paper/2311.09227/) (2023)
- [Near to Mid-term Risks and Opportunities of Open-Source Generative AI](https://mlsanity.com/paper/2404.17047/) (2024)
- [Large Language Models "Ad Referendum": How Good Are They at Machine Translation in the Legal Domain?](https://mlsanity.com/paper/2402.07681/) (2024)
- [Towards a Framework for Openness in Foundation Models: Proceedings from the Columbia Convening on Openness in Artificial Intelligence](https://mlsanity.com/paper/2405.15802/) (2024)
- [Generative AI Models: Opportunities and Risks for Industry and Authorities](https://mlsanity.com/paper/2406.04734/) (2024)

## Built on by (most cited first, 1 of 1)

- [Translation Analytics for Freelancers II: Benchmarking Local LLMs for Confidential Translation Workflows](https://mlsanity.com/paper/2605.31452/) (2026)

## Abstract (arXiv)

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.