When an AI model stops being used, it doesn't just vanish without a trace. In fact, it can go through different stages before being fully retired.
This can raise questions about what happens to these models and why they are retired. Let's explore these questions and better understand the process.
1. What does 'retired model' mean?
An AI model is considered retired when it stops being a priority for the company and starts being withdrawn from public use. This happens in stages: the model can be active, legacy, discontinued and, finally, retired.
Imagine you have a car that was replaced by a newer model. The old car can be sold, donated or kept in storage. Something similar happens with AI models.
2. Why are AI models retired?
AI models are retired due to a combination of technical and strategic factors. Newer versions tend to be faster, more accurate and safer, which naturally leads them to replace the previous ones.
Keeping several large models running at the same time requires a lot of infrastructure, which increases costs, server consumption and technical complexity. It's like having several production lines in a factory: each one requires maintenance, resources and space.
3. What happens to retired AI models?
Even after being retired, these models do not always cease to exist. They can follow different paths, such as being made available via API for developers, temporarily returning by public demand, being kept in storage for a possible relaunch or being recycled for the next generation.
A retired model can be compared to an employee who retires: they can keep working on a freelance basis, be called back for a specific project or have their skills and experience used to train new employees.
4. Which models cannot be retired?
Open-weight models are the only ones that cannot really be retired by a company, since their parameters are made publicly available. Examples include Llama, Mistral, DeepSeek and Qwen.
These models are like open-source software: anyone can download them, run them and modify them. They keep existing outside central control, as long as there is hardware capable of running them.
Frequently asked questions
What happens to the data of a retired AI model?
The data of a retired AI model can be stored for future use, such as in research or audits. In addition, the model's parameters can be reused to train new versions.
Can I keep using a retired AI model?
It depends on the case. If the model is made available via API, you can keep using it. However, if the model is fully retired, it may no longer be possible to access it.
What are the advantages of retiring an AI model?
The advantages include reducing costs, improving the user experience and the possibility of focusing on newer and more efficient versions. In addition, retiring an AI model can help reduce technical complexity and improve security.
In short, the process of retiring an AI model is natural and necessary for the advancement of technology. Understanding what happens to these models can help companies make informed decisions about their AI strategies.