io.net blog

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

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

The DePIN use case for AI and ML compute is pretty straightforward: physical infrastructure networks make efficiency gains when supply-side coordination moves on-chain. With DePIN, no single operator provisions compute hardware and takes on all of the capital risk. Instead, decentralized networks incentivize distributed participants, from GPUs and storage nodes to wireless radios and sensors, to deploy resources and receive compensation by way of token economics. Amongst Layer 1s, Solana has em

Your 2026 guide to building a purpose-built GPU cluster for AI. Includes TCO, vendor-agnostic benchmarks, hardware selection (H100/MI300X), and rollout plan.

TL;DR * A LoRA fine-tune on a 7B model costs under $10.  * A 70B QLoRA run costs $15–30.  * Full fine-tuning a 70B on 8 GPUs for a day costs $200–300.  * If your actual spend is materially higher, the gap is almost certainly the GPU pricing layer, not the job itself. Fine-tuning a large language model costs anywhere from $3 to $3,000. Model size, GPU tier, and whether you're running LoRA adapters or attempting a full-weight update are all factors that can impact pricing. The reality is tha

Wondera cut AI training costs 75% and scaled to 200,000 users in 4 months using io.net's decentralized GPU infrastructure, launching 3 months ahead of schedule.