開源 AI 模型崛起：Llama、DeepSeek 如何改變局勢
開源模型正快速追上閉源巨頭，也讓企業有了自架 AI 的選項。這對台灣意味著什麼？
Open-Source Models Are No Longer Second Choices
In the past, open-source AI models were often considered "usable but not strong enough," but with the rapid progress of models like Llama, DeepSeek, and Qwen, the gap has narrowed significantly, making open-source a serious option.
Why Open-Source Matters
- Cost: Hosting or using open-source model APIs can be much more cost-effective than closed-source alternatives.
- Privacy and Control: Data can be kept in a self-hosted environment, which is particularly important for sensitive industries.
- Customization: Models can be fine-tuned to create bespoke versions that fit specific needs.
How to Use Open-Source Models
- Local deployment: Ollama, LM Studio, Jan.
- Cloud inference: Together AI, Fireworks AI, Groq.
- Model sources: Hugging Face.
Impact on Taiwan
- SMBs: Can adopt AI at a lower cost without relying entirely on foreign closed-source services.
- High-privacy industries (medical, legal, financial): Can keep data in a self-hosted environment.
- Developers: Have more customizable and controllable options.
Things to Note
Open-source does not mean free — self-hosting requires hardware and maintenance capabilities; quality and security must also be self-monitored.
Future Trends
The landscape of "closed-source for capability, open-source for cost and control" will continue, with most companies eventually using a mix of both.
Conclusion
Open-source models give you the freedom to not be tied to a single service. When evaluating, consider not just capability, but also cost, privacy, and maintenance. For further reading: Using local AI to protect your data, 2026 comparison of mainstream AI large models.
Frequently Asked Questions
開源 AI 模型免費嗎？
模型本身多免費，但自架需要硬體與維運成本。
開源模型贏在哪？
成本更低、資料可自控、可微調客製。
企業該用開源還是閉源？
多數最終會混用：閉源比能力、開源比成本與掌控。