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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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See AllOn 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
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.
Your GPU data center investment framework. Compare TCO for cloud, colo, & workstation, including power, cooling, ROI, and hidden costs.
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.
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.
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.
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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See AllOn 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
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.
Your GPU data center investment framework. Compare TCO for cloud, colo, & workstation, including power, cooling, ROI, and hidden costs.
Google Cloud Platform (GCP) has emerged as a formidable AI infrastructure provider. It’s done this by leveraging Google's decades of machine learning expertise and proprietary TPU (Tensor Processing Unit) technology. Boasting Vertex AI, BigQuery ML, and tight integration with TensorFlow and JAX, GCP offers a compelling ecosystem for AI teams already invested in Google's toolchain, as well as a compelling alternative to other hyperscalers like AWS and Azure. . When evaluated purely on GPU comput
Amazon Web Services (AWS) pioneered cloud computing in 2006 and, not surprisingly, remains the dominant player to the tune of 32% market share and $90B+ annual revenue. AWS offers the most comprehensive cloud ecosystem spanning compute, storage, databases, machine learning services, and 200+ integrated products. If you’re an enterprise with complex multi-cloud strategies, AWS will probably be your best option for its unmatched breadth and maturity. Yet, AWS is a wounded giant of sorts. In the G
Akash Network launched in 2020 as the "Airbnb for cloud compute”. In doing so, it pioneered the DePIN movement with a decentralized marketplace for spare CPU and storage capacity. Fast forward to 2026. Akash now offers GPU support that enables it to compete in the exploding AI infrastructure market. But Akash’s CPU-first architecture and container-focused approach creates some fundamental limitations, especially for startups running large-scale AI training and inference. io.net was purpose-bu
See how Leonardo.Ai scaled from 14K to 19M users and cut GPU costs by over 50% using io.net's high-performance, affordable compute solution for generative AI.
If you’re currently scaling your AI product, you’ve probably noticed something rather unsettling: your infrastructure bill is growing faster than your product. Many startup teams are experiencing compute costs that consume 50-60% of their entire operating budget. That’s more than salaries for engineering, customer acquisition, and other team roles combined. Let’s be clear: the economics of AI budgets are now, in an ironic feedback loop, threatening the stability of the entire AI sector. Don’t
Vistara Labs used io.net to scale its Zaara AI platform, building 5,600 apps in two months while cutting compute costs by 3x and achieving zero infrastructure failures.