io.net blog

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

Anyone who rented a GPU knows the routine. You visit AWS, navigate to a p4d instance, quickly hit a quota wall, file a support ticket, wait three days, and finally get your A100. Or perhaps that’s not your experience. Instead, maybe you got an "insufficient capacity" error and moved on to GCP. Either way, you ran your workload and didn't think much about what was happening underneath. This infrastructure experience of waitlists, opaque pricing, and quotas is the intentional design of centralize

We served the same model on both H100 and H200, under identical live traffic, for ten days. The results were not quite what the spec sheets would suggest, and the biggest factor turned out to be something neither datasheet mentions.

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

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.