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Home»ADOPTION NEWS»Using the potential of AI with a distributed computing network
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Using the potential of AI with a distributed computing network

By Crypto FlexsMay 18, 20253 Mins Read
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Using the potential of AI with a distributed computing network
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Jesse Ellis
May 18, 2025 08:19

Look at how the diversified Compute Network increases the demand for AI applications and provide an expandable solution through the consumer grade GPU. Learn about actual use cases and industrial partnerships.





According to Render Network, the rapid growth of artificial intelligence (AI) applications emphasized the need for a reconstructed approach to computing power. Distributed computing networks are appearing as an executable alternative because existing cloud providers such as AWS, Google Cloud and Microsoft Azure are having difficulty meeting AI demand.

Centralized bottleneck phenomenon

By early 2025, the surge in AI’s use of OPENAI’s CHATGPT to more than 400 million people emphasizes the tremendous demand for Compute Resources. However, the dependence on the centralized infrastructure has been expensive and limited to supply. Distributed computing networks driven by consumer grade GPUs offer extended and inexpensive solutions for various AI tasks, such as offline learning and the eg machine learning.

The reason why consumer GPUs are important

Distributed consumer grade GPUs provide parallel computing power for AI applications without the burden of the centralized system. Founded in 2017, Render Network stands at the forefront of these changes so that organizations can run AI work efficiently in the GPU’s global network. Partners such as Manifest Network, Jember and Think are using this infrastructure for innovative AI solutions.

New kind of partnership: module type, distributed computing

The partnership between manifest and render networks shows the benefits of distributed computing. By combining Manifest’s security infrastructure with Render Network, it provides a hybrid computing model that optimizes resources and reduces costs. JEMBER is already in operation, thinking about using a render network for asynchronous workflow and supporting Onchain AI Agents.

Next: Towards the distributed AI depending on the size

Distributed computing networks open the way to train LLM (Lange Language Models) at the edge, allowing small teams and new companies to access cheaper computing power. The founder of Stability AI, the founder of the AI, has emphasized the potential to improve efficiency and accessibility by distributing educational workloads worldwide.

Rendercon presented this development with discussions about the future of AI Compute related to industry leaders such as NVIDIA’s Richard Kerris. The event emphasized the importance of distributed infrastructure in forming a digital environment, providing modular computing, scalability and elasticity for centralized bottlenecks.

Tomorrow’s digital infrastructure formation

Rendercon not only shows the GPU function, but also redefines control of computing infrastructure. The Trevor Harries-Jones of the Render Network Foundation emphasized the role of a decentralized network that gives the producers and ensures high-quality output. Cooperation between Render Network, Manifest, Jember and Think shows the potential of distributed computing to change AI development.

Through this partnership and innovation, the future of AI Compute will be more distributed, accessible and open to solve more demands of the AI ​​revolution with efficiency and expansion.

For more information, visit the render network.

Image Source: Shutter Stock


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