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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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One of the great ironies of AI infrastructure is that AI agent workloads are structurally incompatible with how hyperscalers sell compute. The reserved-instance model designed for sustained, predictable throughput collapses under the weight of agentic pipelines that spin up 40 workers in 90 seconds, run them for 7 minutes, then need zero capacity until the next trigger fires. That’s some extremely annoying architectural friction that anyone building an AI model or incorporating it into their exi
Cloud procurement used to be a straightforward infrastructure decision. With GPU cluster growth across various clouds, from hyperscalers to neoclouds and DePIN solutions, that’s just no longer the case. Enterprise buyers now commit six- and seven-figure compute budgets to providers whose compliance posture, contractual protections, and operational reality vary enormously. This often happens in ways that aren't so obvious from a pricing page or a sales deck. This blog is your checklist coveri
The current AI infrastructure conversation has settled on one big number: 38 gigawatts. That's Morgan Stanley's estimated US data-center power shortfall through 2028. What’s creating this shortfall? The GPU compute needed for inference workloads, and what the existing grid can actually deliver to new AI data facilities on any realistic timeline. Here are some quick numbers. Grid interconnection queues are presently running 5+ years out in most major markets. Add another 2–3 years for transform
Vendor lock-in used to be the province of IT departments. Now it’s a real budget risk. According to widely-cited survey data, 94% of IT professionals report concern about vendor lock-in. For anyone dealing with AI infrastructure, that worry is about to get more expensive. On August 26, 2026, AWS announced an expanded multi-billion-dollar commitment to Nvidia GPU capacity that cemented the hyperscaler-as-gatekeeper model. It did so at precisely the moment AI workloads are scaling the fastest. W
You don't need one GPU. You need 32 GPUs that talk to each other like they're in the same rack. Every DePIN network’s GPU marketing leads with price per GPU-hour. They publish comparison tables showing their H100 rates against AWS, their A100 spot costs against GCP, or their RTX 4090 per-second billing against Azure. Read between the lines and that marketing suggests that cheaper access to hardware is equivalent to actually usable infrastructure. Well, it isn't that simple. At least not for th
The AI budget conversation is the hot topic this year. So, it’s safe to say that If you've probably heard some version of this line: token costs are falling fast and AI is about to get a lot cheaper. It's a reasonable proposition. You want to believe it. But according to Gartner's own research, it’s a notion that is mostly wrong for the people actually paying the bills. Gartner's forecast, published this spring, makes some other striking proclamations. By 2030, they believe that running infer
The AI budget conversation is the hot topic this year. So, it’s safe to say that If you've probably heard some version of this line: token costs are falling fast and AI is about to get a lot cheaper. It's a reasonable proposition. You want to believe it. But according to Gartner's own research, it’s a notion that is mostly wrong for the people actually paying the bills. Gartner's forecast, published this spring, makes some other striking proclamations. By 2030, they believe that running infer
Let’s imagine that you’ve budgeted $10,000 for an AI/LLM training run. You hop on AWS and the app says, “no H100s available for 72 hours”. Apart from some stress and frustration, what does that delay actually cost your team? In answering that question, perhaps like Thanos, when asked by Dr. Strange how much it cost to collect the infinity rings, he replied: “Everything”. All joking aside, most finance models would record “zero”. There’s no invoice because you’ve consumed no GPU-hours. Your $10
When you ask an AI to help with something and it refuses, that refusal didn't happen by accident. Someone, or more precisely, a team of researchers, lawyers, and ethicists at a major AI lab, made a deliberate choice to build that boundary into the model. Today, major model providers (e.g. OpenAI, Anthropic, Google, Meta, Mistral, and Cohere) each maintain their own alignment teams, each with distinct values, risk tolerances, and commercial pressures shaping what their models will and won't do. T
Let’s imagine that you’ve budgeted $10,000 for an AI/LLM training run. You hop on AWS and the app says, “no H100s available for 72 hours”. Apart from some stress and frustration, what does that delay actually cost your team? In answering that question, perhaps like Thanos, when asked by Dr. Strange how much it cost to collect the infinity rings, he replied: “Everything”. All joking aside, most finance models would record “zero”. There’s no invoice because you’ve consumed no GPU-hours. Your $10
io.net is utilizing the Solana blockchain for instant GPU payments, smart contract automation, and secure, decentralized cloud computing transactions.
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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Open-weight models like Llama 3.1, Mistral, and Falcon are technically free to download. But running them at anything approaching production scale is a different story altogether. The reason for this is that just a handful of hyperscalers and well-funded inference platforms basically control the GPU layer. So, if you need AI infra to serve your 70B parameter model (NVM3 storage, high-bandwidth interconnects, A100s or H100s in quantity), then your AI startup or LLM research project will run hea
When OpenAI's API went down in November 2023, it took thousands of production applications with it for over 4 hours. When AWS us-east-1 had its major outage in December 2021, it knocked out Netflix, Disney+, Slack, and hundreds of SaaS products simultaneously. That’s what centralized infrastructure looks like under immense load. These are case studies in why your GPU compute should never sit in just one hyperscaler basket. Doubly so for AI startup or LLM research project that can’t afford that d
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
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
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