Explore our Blog for
Latest News & Insights
Stay updated with the latest updates and new products. Discover what's happening around the io.net.
io.net blog
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
Latest
See All
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
With demand outstripping the supply, most people's first thought is to build more data centers. But that framing misdiagnoses the problem. The GPU crisis won’t be solved with more construction.
Cloud storage abstracts away the very urgent compliance question that any AI startup or LLM research project should be asking itself: Where does my data physically sit? The answer to this question has very real and direct consequences, including legal, financial, and operational. Under GDPR, processing EU personal data on US-based infrastructure without adequate safeguards exposes companies to fines up to €20 million or 4% of global annual turnover, whichever is higher. Beyond regulation, there
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
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.
Latest By Topic (122)
See AllLet’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
With demand outstripping the supply, most people's first thought is to build more data centers. But that framing misdiagnoses the problem. The GPU crisis won’t be solved with more construction.
Cloud storage abstracts away the very urgent compliance question that any AI startup or LLM research project should be asking itself: Where does my data physically sit? The answer to this question has very real and direct consequences, including legal, financial, and operational. Under GDPR, processing EU personal data on US-based infrastructure without adequate safeguards exposes companies to fines up to €20 million or 4% of global annual turnover, whichever is higher. Beyond regulation, there
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