> For the complete documentation index, see [llms.txt](https://litepaper.topnetwork.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://litepaper.topnetwork.org/content/problem.md).

# Problem

Currently, both model training and inference are provided by centralized service providers. However, their services have the following issues:

* **Inability to use models freely:**
  * Models provided by centralized service providers face licensing and censorship restrictions. The models' ability to answer questions is limited, leading to diminished user experience.
* **Lack of free circulation of models:**
  * Centralized model hosting providers impose various reviews on the models they host, restricting normal model providers from offering their models freely.
* **Lack of model diversity:**
  * Centralized service providers often create models to address general problems, while real user needs are diverse and require more customized models from different providers.
* **Data privacy concerns:**
  * When centralized providers obtain training data, there are risks of data loss or unauthorized misuse. This high-quality data is often the core asset of enterprises or individuals and must be safeguarded.
  * When engineers fine-tune models on centralized platforms, the fine-tuned models are often shared with other users for cost and profit maximization. Private data can easily be leaked through specially constructed prompts, which is unacceptable.
* **High computational costs:**
  * Small to medium-sized users often cannot afford expensive GPU equipment necessary for large-scale AI training. Current cloud services do not significantly reduce these costs.