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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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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
There's a specific moment every ML engineer has dreads. In the early morning hours, after finishing the debugging of a data pipeline, you’re just about to run an experiment that requires 8 H100s for roughly 6 hours. After hopping on your cloud console, you click through the instance request, and see this message: "Your request for p5.48xlarge has been denied. Current quota: 0. Request quota increase." The thing is, that quota increase takes 3-10 business days. So, your experiment waits. A
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.
When LLM agents started performing automated tasks, their architects failed to mention that they consume compute in ways that are fundamentally different from your usual DevOps pipeline. In a conventional web service, there is a predictable compute profile. Requests come in, get processed by known-size containers, complete, and then this process repeats. Traditionally, autoscaling at the edges is well-understood, so that your company’s infrastructure team can model capacity, set alarms, and sle
KayOS, an AI startup, achieved 5x developer power with io.net. Learn how their 2-person team cut compute costs by 60% ($2.5k to $1k/month) using io.intelligence.
AI has already changed the world. But, for it to reach its full potential, issues of accessibility and affordability need to be addressed. It needs to happen soon, before the industry leaves a huge swath of devs and builders from around the world behind. AI teams need infrastructure that allows them to get their product to market, not burn through their runway before they ever get off the ground. Models are growing exponentially. Llama 3.1 405B requires 16,000 H100 GPUs for training, GPT-4 tak
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.Intelligence delivers real-time monitoring for AI workloads, helping optimize performance, cut costs, and ensure reliable system stability.
"IO.net offers a decentralized GPU cloud, enabling scalable, cost-effective AI training, rendering, and simulations with global resources."
Master effective AI communication prompt frameworks including R-T-F, T-A-G, B-A-B, C-A-R-E, and R-I-S-E to unlock better AI results and consistent outputs.