> For the complete documentation index, see [llms.txt](https://neuralbyte.gitbook.io/neuralbyte-erc/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://neuralbyte.gitbook.io/neuralbyte-erc/introduction/challenges-in-traditional-ai-model-training.md).

# Challenges in Traditional AI Model Training

**Challenges in Traditional AI Model Training:**

* Dependency on GPU and TPU: Traditional AI model training heavily relies on Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs), which can be expensive and have limited availability.
* Cost Barriers: The high cost of GPU and TPU usage poses a significant barrier for small-scale developers and researchers, hindering innovation and progress in the AI field.
* Scalability Issues: Scaling AI model training with traditional hardware often faces limitations in terms of scalability and efficiency, particularly for large-scale projects.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://neuralbyte.gitbook.io/neuralbyte-erc/introduction/challenges-in-traditional-ai-model-training.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.