本地部署开源大模型，让译者更安全、自主地使用AI翻译。
Beyond the Cloud: Assessing the Benefits and Drawbacks of Local LLM Deployment for Translators
- 在本地CPU上运行三款开源模型，替代云端商业聊天机器人。
- 本地部署提升数据隐私与控制权，降低对云服务依赖。
- 适合关注隐私、成本与自主性的个体译者和小企业。
大型语言模型的快速普及为翻译领域带来机遇与挑战。尽管商业云端AI聊天机器人备受关注，但数据隐私、安全及公平访问问题促使探索替代部署模式。本文评估了三款可在CPU平台本地部署的开源语言模型，对比其与商用在线聊天机器人的性能表现。研究聚焦功能性能，而非人机翻译质量的对比（该领域已有大量研究）。所选平台具备跨操作系统兼容性与易用性。虽然本地部署存在自身挑战，但其在数据控制、隐私保护和减少云服务依赖方面的优势显著。研究结果有助于推动AI技术民主化，为未来提升大模型在个体译者与中小企业中的可及性与实用性提供依据。
原文摘要 · Abstract (English)
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.
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