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.

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.

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.

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.