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.