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description: >-
  Unleashing The Power Of Parallel Processing Solution To Slash Computation
  Times For AI Developers By Up To 20x
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# Introduction

The exponential rise of generative AI technology has created unprecedented demand for GPUs, which current supply is struggling to keep up with. Multiple decentralized GPU marketplaces have emerged to fill this supply gap, allowing AI developers to tap into idle compute power distributed amongst both individuals and enterprise-scale solutions providers. However, increasing the volume of available GPUs is only half the battle in meeting the ever-growing demands of the AI industry. \


For increasingly complex generative AI tools to become scaleable, not only do they need access to more GPU capacity, but they also need to use that capacity in a more cost-effective and efficient manner. Whilst much is being done to address the capacity issue, little progress is being made in ensuring this new capacity is used in ways that maximize processing speed and minimize costs.\


A key opportunity for reducing the cost and improving the efficiency of GPU usage lies in the way that code is executed. Traditional sequential programming does not fully utilize the computational power available from modern multi-core CPUs and GPUs. This results in underperformance, where the potential of the hardware is not fully exploited, leading to slower processing times and increased operational costs.\


Parallel AI solves this problem by providing a novel parallel processing solution that makes it faster and more efficient for AI developers to run complex tasks on GPUs and CPUs. By enabling automatic parallelization of code, Parallel AI ensures that applications can run tasks concurrently across multiple processing cores. This maximizes hardware utilization, reduces processing time, and increases overall system efficiency. A task that would typically take 10 minutes can now be done in 10 seconds, reducing computation time by up to 20x.\


The Parallel AI solution takes out all of the complexity of executing parallel processing by providing high-level programming interfaces that automate and simplify the parallelization process. Developers can write code in a style they are familiar with, which Parallel AI then converts into an optimized parallel format, abstracting away the complexities of parallel execution.\


Finally, Parallel AI matches AI developers to GPU providers that offer the optimal specifications for managing the parallel processing tasks that it facilitates. Aggregating compute power from existing decentralized GPU marketplaces, Parallel AI selects the best-fit hardware offerings and optimizes their outputs via its preconfigured parallelization process. By accepting payments for Parallel AI’s services in the form of its native $PAI tokens, Parallel AI can seamlessly integrate payment flows into the aggregated decentralized GPU marketplaces in which its parallel code is executed. As a bulk buyer of compute power, Parallel AI can also negotiate discounts with GPU marketplace partners that accept $PAI, allowing it to emerge as a universal currency for GPU procurement. In doing so, $PAI facilitates Parallel AI’s vision of delivering a fully decentralized parallel processing solution for AI developers. \


In summary, Parallel AI provides an end-to-end parallel processing solution all the way from coding, through processing, to back-end infrastructure that ensures AI developers reduce their GPU costs and decrease their processing times.  In doing so, Parallel AI solves the biggest problem facing AI developers today and provides the key missing piece in solving the GPU supply crunch.\
