What do tech businesses want? GPU compute. When do they want it? Right now. How much do they want? Everything you got. How much are they willing to pay? Way less than you’re charging. That, in a nutshell, is why tech companies are increasingly turning away from centralized cloud providers in favour of decentralized AI and web3 networks that can meet their needs.
They’re not doing it for the decentralization – they’re doing it for the significant cost savings it can yield, coupled with the flexibility of not being locked into rigid pricing models. That plus the speed with which clusters can be spun up and put to work on AI training and inference.
Traditional cloud providers like AWS and Google Cloud still dominate the market, but their market share is being nibbled away at by decentralized GPU networks. These DePINs are transforming how AI models are trained by delivering on-demand computing with the following five leading the charge.
1. Render
One of the most notable players in the space is Render Network, originally designed for decentralized GPU rendering but now expanding into AI computing. Render Network has built a mature ecosystem, connecting thousands of GPU providers with artists, developers, and data scientists. Its existing pay-as-you-go model, powered by the RENDER token, makes GPU access seamless and affordable.
The network is actively expanding its capabilities to support AI inference and model training, and is betting big on AI agents – a major web3 growth area where it’s eager to provide the GPUs these autonomous bots require. Recent announcements emphasize AI resource allocation and developer-friendly APIs, indicating a deeper push into on-chain AI computing.
2. io.net
One of the best-known DePINS operating in the space, io.net aggregates GPU power from data centres and individual contributors across 130 countries to create an on-demand decentralized GPU cloud. io.net significantly reduces costs, offering AI compute at 90% less per TFLOP than centralized alternatives while ensuring rapid deployment of high-performance clusters, including NVIDIA H100s, in under two minutes.
The platform leverages Solana’s blockchain to provide transparent transactions and features a unique Proof of Time-Lock mechanism that guarantees dedicated GPU resources.
With growing enterprise adoption, expanded hardware support for models like 4090s, A100s, and H100s, and a scalable cluster rental system, io.net has become a leading choice for AI-powered organizations seeking high-performance computing.
3. Hypercycle
For developers seeking web3-native AI inference, Hypercycle is the DePIN they fire up. Unlike traditional decentralized GPU platforms that focus on raw compute power, Hypercycle specializes in AI model inference, allowing dapps to execute machine learning tasks without centralized bottlenecks.
The protocol integrates micro-payments for inference tasks, ensuring a seamless and cost-effective billing structure. Hypercycle’s architecture is optimized for low-latency AI execution, making it a valuable resource for real-time applications.
Hypercycle is actively forging partnerships with AI-driven dapps and continues to refine its layered AI solutions to enhance speed and efficiency. Like several of the other companies profiled here, Hypercycle is a long AI agent and believes it can capture significant market share here.
4. Akash Network
Akash Network serves as a decentralized cloud marketplace catering to a blend of AI and web3 developers. Its auction-based pricing model ensures that developers get access to computing resources at competitive rates, while its decentralized matching system allows for instant provisioning of GPU instances.
Akash has steadily expanded its support for AI training and inference workloads, onboarding new infrastructure providers to meet the growing demands of decentralized applications and machine learning research.
Its commitment to creating a truly open and developer-friendly cloud infrastructure has made Akash an attractive alternative to traditional centralized providers.
Ongoing integrations with AI frameworks have extended Akash’s capabilities beyond general-purpose workloads into specialized GPU-driven tasks for machine learning. Its 2025 roadmap reveals where Akash is headed next with plenty of new products poised to be rolled out.
5. Gensyn
Another rising star in the decentralized AI computing space is Gensyn, a blockchain-powered solution designed to facilitate large-scale ML workloads.
Unlike other decentralized GPU providers, Gensyn operates on a Proof-of-Compute mechanism that ensures verifiable contributions to AI training tasks. By tokenizing AI compute incentives, Gensyn makes high-performance AI training accessible to organizations of all sizes, democratizing machine learning infrastructure.
The company has gained significant industry recognition, securing funding from venture capital giant a16z, which has accelerated its development.
Recent pilot projects have demonstrated its potential for decentralized AI model training, and ongoing infrastructure upgrades are enhancing its ability to support large-scale compute operations. Gensyn doesn’t currently have a token but one is on the way, making this a DePIN to watch.
Decentralized Compute Is Just Getting Started
With AI computational requirements escalating, decentralized platforms offer a compelling alternative to traditional cloud services.
Whether it’s Render Network’s expansion into AI workloads, io.net’s rapid deployment of high-performance clusters, Hypercycle’s real-time AI inference, Akash Network’s permissionless cloud compute, or Gensyn’s blockchain-based AI training framework, decentralized GPU compute is allowing AI innovation to flourish.
Also Read : How Anyone Can Build a Top-Performing AI Model and Earn Rewards
