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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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GPU cloud was engineered for two primary workloads. LLM training runs that consume thousands of GPUs for days, and batch inference that processes queued requests in predictable bursts. The scheduling models, pricing structures, and orchestration layers of every major cloud provider reflect these assumptions: reserved instances for training and autoscaling groups for inference endpoints. It’s all very neat, predictable, and optimizable. But… AI agents really don't work like that. A single age
TL;DR * Infrastructure gap: Don’t get stuck on a 6-month waitlist for Blackwell chips at hyperscalers. With io.net, you get instant B200/H200 access. * Cost performance: Get 50-70% lower costs compared to AWS/GCP on-demand rates. * Hardware Versatility: io.net offers a full mix of GPUS including Nvidia chips and high-VRAM AMD MI300X clusters (192GB memory) for large-scale Mixture-of-Experts (MoE) training. * Quality Assurance: We verify all hardware via zkTFLOPs (Proof-of-Contribution) and
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 $60B+ 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 decentra
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.
GLM-4.7 is now live on io.intelligence. Z.ai's open-source coding model scores 84.9% on LiveCodeBench vs Claude's 64%. Access it via a single API endpoint.
io.net's 2025: $4M+ saved across 5 case studies, 320K GPUs in 138 countries, 21 partnerships, and a tokenomics redesign. What happens when infrastructure stops being the constraint.
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.
Solve compute bottlenecks with parallel computing. Compare models (parallel, concurrent, distributed), hardware, cloud costs, and best practices for performance gains.
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.