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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

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.

Centralized ML pipelines hamper AI innovation. Learn how io.net’s decentralized infrastructure eliminates bottlenecks for startups

How a Singapore robotics startup proved their navigation AI dataset was 25x larger than competitors—and cut compute costs by 92.8% with io.cloud

Distributed GPU networks are breaking Big Tech's ML infrastructure monopoly with 90% cheaper training, instant scaling, and democratized AI compute

Tired of 25-call limits killing your AI coding flow? Learn how io.net and Void Editor unlock truly autonomous development without artificial constraints.

io.net launches Total Network Earnings (TNE) for complete transparency in AI infrastructure costs with real-time tracking, automated payments, and verifiable metrics.

io.net celebrates its first anniversary, showcasing growth to 10,000+ GPUs across 138 regions and $13M revenue while democratizing AI infrastructure access.

io.net enables privacy-first AI training through Flashback Labs' Stargazer model, using federated learning and TEEs to protect personal data during training.

Launch I/O is a 35-day hackathon where developers build autonomous AI agents using io.intelligence's unified toolkit, with $6,500 in prizes and ecosystem access.

Injective partners with io.net to revolutionize DeFAI development by combining AI-driven blockchain tools with decentralized GPU computing infrastructure.

Theoriq partners with io.net to power decentralized AI agent swarms on blockchain, reducing compute costs by 90% while scaling modular AI ecosystems.

io.net's developer tools provide simplified API integration and decentralized GPU access, reducing AI deployment costs by up to 90% compared to centralized providers.

Explore how decentralized computing networks are revolutionizing AI development by providing cheaper, faster, and more scalable alternatives to traditional cloud services.