Meeting AI Demand with Decentralized Compute: Real Use Cases
Decentralized compute in action: from inference to offline and edge machine learning.
In conversations around the possibilities — and limits — of building AI applications, a recurring theme is emerging loud and clear: the AI revolution demands a radical rethink of how compute power is distributed and accessed.
As generative AI adoption skyrockets and centralized compute providers like AWS, Google Cloud, and Microsoft Azure struggle to keep up with demand, thought leaders converged at RenderCon, hosted by the Render Network Foundation, to spotlight a different path forward. That path is one built on decentralized compute, powered by a distributed network of consumer-grade GPUs.
The Centralization Bottleneck
AI’s explosive growth has pushed the limits of traditional infrastructure. As of early 2025, OpenAI’s ChatGPT boasted over 400 million weekly users. This massive uptake highlights the scale and urgency of AI demand — but also the mounting pressure on GPU supply chains.
Not every AI-related task requires the latest, most powerful, enterprise-grade GPU model to run a job at lightning speed or in real time. Increasingly varied AI use cases, including offline learning, edge machine learning, and inferencing, now require affordable, scalable solutions. Yet the current paradigm of centralized compute comes with high costs, constrained supply, and unnecessary over-performance. That’s where decentralized compute can play an important role.
Why Consumer-Grade GPUs Matter
Consumer-grade GPUs, distributed across a decentralized network, can provide the parallel compute necessary to power AI applications without the burden of centralized infrastructure.
The Render Network is pioneering this shift. Founded by Jules Urbach in 2017 as a peer-to-peer rendering network, the Render Network became available to the public in 2020 and has rendered 50 million frames since. With a distributed GPU architecture, the Render Network enables organizations to run AI tasks affordably and efficiently across a global network. As demonstrated during RenderCon, partners like the Manifest Network, Jember, and THINK are already leveraging this infrastructure in creative and complementary ways.
A New Kind of Partnership: Modular, Distributed Compute
Take the collaboration between the Manifest Network and the Render Network. Manifest provides enterprise-grade CPUs, storage, and secure infrastructure which is ideal for regulated environments like financial services. By integrating the Render Network’s decentralized GPU layer, Manifest extends its compute stack to include fast, affordable GPU power for AI-related tasks that does not require compute to be done in real time and can be processed when the decentralized network gets to it in queue. By providing hybrid compute options for the user, the Render Network and Manifest ensure the most efficient use of resources as opposed to unnecessary compute and cost for the task.
This hybrid model is already in motion. Jember, an AI Financial Trust company, is looking to use the Render Network’s compute infrastructure to run asynchronous workflows and inference which are tasks that don’t require real-time processing or strict data custody. In one test deployment, Jember’s Chain of Trust Choreography™ demonstrated how distributed compute can power secure and verifiable AI systems. The Jember collaboration demonstrated that distributed compute could be deployed and operate at a fraction of the cost of traditional cloud providers.
Meanwhile, THINK is using the Render Network to support the Think Agent Standard, a permissionless protocol for building thousands of onchain AI agents. The flexibility and affordability of decentralized GPU compute are enabling entirely new categories of AI development, once limited by cloud costs or infrastructure bottlenecks.
What’s Next: Toward Decentralized AI at Scale
Training large language models (LLMs) on the edges using distributed consumer-grade GPUs is one of the latest, increasingly attractive areas for developers. Thought leaders like Emad Mostaque, founder of Stability AI and Intelligent Internet, have spoken about what this could look like, where training workloads could be fragmented across a global network, saving time, money, and improving access for smaller teams and startups.
On the RenderCon AI at the Frontier of Art, Science, and Society mainstage panel with Richard Kerris, VP | GM of Media and Entertainment at NVIDIA, Emad explained: “The latest paradigm is time test compute…the longer we give it to think, the more compute sequential, which is what makes it suitable for things like the Render Network where you can distribute, the better the quality output will be.” Emad concluded with: “You’ve seen OpenAI CEO Sam Altman and others actually say this, that the next paradigm is this distributed, sequential paradigm, especially for generating those end assets.”
As AI evolves, real-time training of data at the edges will likely become more critical than pre-trained models. Distributed infrastructure is naturally suited for this future, offering global access, modular compute, rapid scalability, and resilience against centralized chokepoints.
An application well-suited for distributed compute at scale that has attracted a lot of attention in recent years is AI agents. While showcasing the agentic use case for distributed compute at RenderCon, Mike Anderson, CEO of THINK, the developer behind the Think Agent Standard, said, “Digital and physical are merging. Our GPUs are so powerful and our AI models are so powerful we’re literally having less understanding of what reality is…So you’ve got to ask yourself, in a world where that’s true, what’s my role?…We’re the ones who, if we actually can own these things and control them, we can actually be like the seed that plants a forest each. I can control as many agents as the CEO of the biggest corporation or the head of the biggest government. That’s the kind of power that’s going to be in the hands of creators soon.”
The Render Network’s plans to expand into a new Render Compute Network underscores this ambition: to become a go-to platform for AI developers seeking affordable, distributed compute power.
Shaping the Digital Infrastructure of Tomorrow
At its core, RenderCon wasn’t just about showcasing GPU capacity. It was about redefining who controls and benefits from compute infrastructure.
Trevor Harries-Jones from the Render Network Foundation moderated the GPU Markets and Compute panel. He spoke of Simulon, a next generation, comprehensive VFX workflow solution for iPhone to create shots using a real-time AR experience: “We’re really excited because what that does is in terms of opening up a whole new range of creators who haven’t yet learned 3D to the extent many of our artists have but want to create amazing things online…As these guys [Simulon] scale up in the App Store, we’re hoping to be the provider who really helps them scale and deliver that rendering compute to their users across the globe.”
Adding to the conversation, Edward Katzin, CEO of Jember, compared the expectations for Hollywood-level output to financial transactions. Partnering with the distributed Render Network, would be, he said, “an amazing synergy. We’re all about high availability, high reliability, and high trust. No one has a commitment to quality and output like Hollywood…we want to generate those kinds of results, by making sure your money is where it’s supposed to be, how it’s supposed to be, without any fraud.”
With the Render Network’s distributed GPU power, Manifest’s secure infrastructure, and a growing ecosystem of builders like Jember and THINK, the path forward for AI compute is more distributed, more accessible, and more open than ever.
