> For the complete documentation index, see [llms.txt](https://parallel-ai.gitbook.io/parallel-ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://parallel-ai.gitbook.io/parallel-ai/overview/problem-statement.md).

# Problem Statement

The primary problem that Parallel AI solves is inefficiencies at the level of coding and processing infrastructure that translate into higher costs, sub-optimal GPU usage and slower processing speeds for AI developers. <br>

Parallel AI’s parallel processing solution addresses 3 key inefficiencies:

1. Failure To Harness The Full Capacity Of Modern Multi-Core GPUs / CPUs: Modern CPUs and GPUs are equipped with numerous cores that can execute many operations in parallel, significantly speeding up data processing and computational tasks. Sequential processing fails to capitalize on this functionality leading to slower processing times and increased costs.<br>
2. The Complexity Of Writing Parallel Code: The reason that more AI developers aren’t using parallel processing techniques more often is that current methods for writing parallel code is inherently complex and requires a deep understanding of concurrency, synchronization, and potential race conditions. This complexity can be a significant barrier for developers, limiting the broader adoption of parallel programming techniques.<br>
3. Lack Of Appropriate Infrastructure To Support Parallel Processing Tasks: The infrastructure required to support the intensive computational tasks associated with parallel processing can be prohibitively expensive, especially for startups and smaller organizations. Reliance on centralized providers poses risks to data privacy and security. Meanwhile, any given decentralized GPU marketplace is unlikely to provide the best solution for the full range of different tasks that need to be executed at different times.


---

# 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, and the optional `goal` query parameter:

```
GET https://parallel-ai.gitbook.io/parallel-ai/overview/problem-statement.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

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.