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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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Training a large language model in 2026 is not a $10 million moonshot reserved for hyperscalers. That said, it's not cheap either. A 7B-parameter model trained on 1 trillion tokens requires roughly 300,000 H100 GPU-hours. If we apply those numbers to AWS on-demand pricing ($11.37/hr for a single H100 via p5 instances), it works out to $3.4 million for the GPU compute alone. At io.net rates ($1.49/hr H100 PCIe), the same workload runs closer to $447,000. That $3M difference between the two nu
Three companies control roughly 65% of all global cloud infrastructure. If you need a GPU today, you are almost certainly renting it from either Amazon, Google, or Microsoft. You’ll pay Big Cloud’s prices, operate under their terms, and be at the mercy of availability they allocate first to their largest customers. That is the status quo with centralized cloud providers. DePIN is the only alternative. DePIN, which stands for Decentralized Physical Infrastructure Networks, turns the hyperscaler
Four companies control 85–92% of the GPU compute powering commercial AI training and inference globally. But there is a way around this monopolistic control and GPU hoarding.
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
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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See AllBlockchain promised to solve centralization, but focused on wrong problems. DePIN networks like io.net finally deliver real value through affordable GPU access.
Unified Chat is the single, intelligent AI workspace that unifies every model and tool. Auto-routes for optimal quality and cost. End fragmentation.
Complete financial framework for GPU infrastructure decisions. Cost modeling, ROI analysis & budget optimization for AI companies.
io.net surpasses $20M in verifiable on-chain revenue, proving decentralized GPU infrastructure can compete with AWS and GCP on cost, performance, and real-world adoption.
Model deployment connects trained ML models to users, yet most stall due to cloud costs and vendor lock-ins. Decentralized platforms cut costs 90%.
Discover how AI data centers optimize workloads, boost efficiency, and power the future of artificial intelligence with advanced infrastructure.
Forget AWS's $37/hour GPU costs. Decentralized networks deliver the same power for 50-70% less, turning idle gaming rigs into AI supercomputers.
Learn how io.net evolved from trading infrastructure to decentralized GPU cloud computing, using distributed resources and blockchain for scalable AI.
Mobile Edge Computing + 5G enables low-latency, secure AI/ML apps by processing data locally, complementing cloud in hybrid architectures.
Distributed systems power AI/ML with scalability, fault tolerance, and performance, yet 73% fail to scale, demanding careful design and optimization.
Comparing cloud and edge computing architectures. Explaining when to use each model and how hybrid approaches optimize latency, scalability, and cost efficiency.
Most ML models fail not from bad algorithms but from $50K/month cloud bills. Learn how decentralized GPUs slash costs 70% while keeping enterprise performance.