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On June 11, 2023, io.net launched with a straightforward idea. AI workloads need more compute than centralized hyperscalers can ever deliver, and the solution would be a decentralized network of GPUs, not another mega data center, that turns underutilized capacity into on-demand infrastructure. Three years later, we’ve fundamentally changed the AI compute market. io.net is now the largest decentralized GPU network in the world. Thousands of GPUs distributed globally, with $8 million in enterpri
Three years ago, we started io.net with a simple, powerful belief that the infrastructure behind AI shouldn't be in the hands of a few giant corporations. Today, we're taking a huge step toward making that vision a reality. As we celebrate our third anniversary, we're excited to announce the official launch of the Incentive Dynamic Engine (IDE). It's a new way of thinking about our tokenomics that ties the supply of $IO directly to how much people are actually using the network. We'll be perman
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RunPod has a reputation for being the GPU solution for the "instant-deploy" developer. Its intuitive "Pods" and robust serverless GPU offerings make it a good fit for startups and hobbyists who frequently prototype. Whereas legacy providers focus on enterprise contracts and academic researchers stick to boutique clouds, RunPod captured the mid-market by mastering serverless GPU compute and container-based flexibility. Its reputation is built on "FlashBoot" technology (sub-200ms cold starts) and
GPU cloud costs have climbed steadily since 2022, and most developers don't really understand why. An NVIDIA H100 SXM on AWS (p4d.24xlarge equivalent) runs roughly $32–$36/hr on-demand. The same GPU on io.net costs $2.99/hr. That amounts to a 10x gap.
On June 11th, io.net's third anniversary, we launched the Incentive Dynamic Engine (IDE). Not a roadmap, not a litepaper. Live, on-chain, utility driven, and fully transparent. A month in, here's another look at what the IDE is, why it matters, and what's happened since. What the IDE does Most token networks grow the same way. They pay suppliers with emissions, and hope demand eventually catches up to justify the supply. It works until it doesn't. When the token price drops, suppliers leave
For infrastructure decision-makers at both startups and growing companies, the GPU landscape of 2026 looks nothing like it did even last year. The mad dash for GPU capacity has now matured into a $140B+ global market defined by architectural diversity, pricing pressure, and a fundamental reevaluation of how compute should be provisioned. As these three forces converge, we are seeing GPU supply expand. While hyperscaler buildouts capture a lot of attention, there has also been a rise in decentr
The AI landscape of 2026 is now more of a battle of over infrastructure instead of a clash of models. For many AI/ML developers, CoreWeave has been the reliable "specialized" choice for NVIDIA hardware. However, as the "Power Wall" of 2026 makes electricity and high-density data center space more precious than the chips themselves, many startup and enterprise teams are searching for alternatives that offer both better availability and more affordable pricing. So, if you’ve been priced out by Co
Frontier AI training, or models with 70B+ parameters, multimodal architectures, MoE variants, requires GPU clusters operating at a scale most teams just can’t self-host. On io.net, AI teams don’t even have to think about self-hosting your compute, or paying hyperscaler prices. You can effortlessly spin up a distributed cluster of H100s or A100s in minutes, paying $1.49–$2.29/hr per GPU with no reserved-capacity commitment. Instead of all of the overhead costs that come with self-hosting, you c
Your 2026 guide to building a purpose-built GPU cluster for AI. Includes TCO, vendor-agnostic benchmarks, hardware selection (H100/MI300X), and rollout plan.
Z.ai's GLM-4.7-Flash (30B MoE) is live on io.intelligence. Get the strongest 30B model for coding & reasoning with best-in-class performance-per-dollar.
Complete technical guide to decentralized compute: benchmarks, cost calculator, compliance checklist, and step-by-step migration from AWS/GCP.
Learn what a GPU cluster is, how it differs from multi-GPU servers, and use our cost calculator to decide if you should build or rent one.
Your 2026 guide to building a purpose-built GPU cluster for AI. Includes TCO, vendor-agnostic benchmarks, hardware selection (H100/MI300X), and rollout plan.
Complete technical guide to decentralized compute: benchmarks, cost calculator, compliance checklist, and step-by-step migration from AWS/GCP.
Discover io.net's Incentive Dynamic Engine (IDE): an adaptive tokenomics model bringing sustainable economics and predictable stability to decentralized GPU compute.
New io.net study shows consumer GPUs (RTX 4090) can cut AI inference costs by up to 75% for LLMs, enabling a sustainable, heterogeneous compute infrastructure.
Blockchain promised to solve centralization, but focused on wrong problems. DePIN networks like io.net finally deliver real value through affordable GPU access.
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io.net partners with Alpha Network to create secure, privacy-compliant AI training using decentralized GPUs and Zero-Knowledge protocols.
io.net partners with CreatorBid to scale AI image models using decentralized GPUs, powering the future of AI agents and creator economy tools.
DeepSeek's breakthrough AI model disrupts Western tech dominance, sparking global competition while highlighting the growing need for efficient compute infrastructure.
io.net powers DefAI with decentralized GPU infrastructure, enabling censorship-resistant AI agents for DeFi at 90% lower costs than Big Tech.
SQD.ai partners with io.net to scale decentralized blockchain data processing, cutting costs 90% while powering AI agents with petabyte workloads.
IO launches Dev Hub community for builders using IO Intelligence. Early adopters like Soh are creating innovative AI agents and sharing projects.
Scale AI infrastructure for 90% less with decentralized GPU networks. Avoid Big Tech pricing while maintaining enterprise performance for startups.
Decentralized GPU networks cut AI training costs by up to 70%, boost flexibility, and overcome centralized cloud bottlenecks for scalable, global ML.
Multi-agent systems are the future of autonomous work. io.net's decentralized GPUs enable seamless collaboration between AI, robots, and IoT devices.
Blockchain meets cloud computing: io.net uses smart contracts and Solana for instant GPU payments, automated rentals, and zero middlemen fees.
AI agent Zerebro taps io.net's decentralized GPU network to power Ethereum validation, merging autonomous AI with blockchain infrastructure.
Everyone’s chasing affordable, scalable computing power these days. So platforms like io.net, Akash, and Render Network are all stepping in to meet that demand. They’re all leveraging underused resources, but each one has its own unique approach. So, how do they actually compare when it comes to approach, cost, performance, and scalability? Let’s break it down. io.net: The Internet of GPUs io.net is laser-focused on cranking out affordable, scalable GPU power for AI and machine learning. And h