io.net blog

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

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