对比自建与云服务，给出大模型本地部署的省钱时机
A Cost-Benefit Analysis of On-Premise Large Language Model Deployment: Breaking Even with Commercial LLM Services
- 构建成本收益框架，量化自建与商用服务的经济性差异
- 发现当使用量超过一定阈值时，自建可实现成本平衡
- 适合关注数据隐私和长期成本的大型企业决策参考
大型语言模型日益普及，组织在提升生产力时面临选择：订阅OpenAI、Anthropic、Google等商业服务，或在自有基础设施上部署开源模型。云端服务虽易用且可扩展，但数据隐私顾虑、迁移困难及长期运营成本促使本地部署需求上升。本文提出一套成本-效益分析框架，评估Qwen、Llama、Mistral等最新开源模型在自建环境下的硬件要求、运维开销与性能表现，并与主流云服务商订阅费用对比。研究基于使用量与性能需求，估算出本地部署的盈亏平衡点，为组织制定大模型战略提供实用依据。
原文摘要 · Abstract (English)
Large language models (LLMs) are becoming increasingly widespread. Organizations that want to use AI for productivity now face an important decision. They can subscribe to commercial LLM services or deploy models on their own infrastructure. Cloud services from providers such as OpenAI, Anthropic, and Google are attractive because they provide easy access to state-of-the-art models and are easy to scale. However, concerns about data privacy, the difficulty of switching service providers, and long-term operating costs have driven interest in local deployment of open-source models. This paper presents a cost-benefit analysis framework to help organizations determine when on-premise LLM deployment becomes economically viable compared to commercial subscription services. We consider the hardware requirements, operational expenses, and performance benchmarks of the latest open-source models, including Qwen, Llama, Mistral, and etc. Then we compare the total cost of deploying these models locally with the major cloud providers subscription fee. Our findings provide an estimated breakeven point based on usage levels and performance needs. These results give organizations a practical framework for planning their LLM strategies.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。