AI infrastructure was built for humans. And it comes with human barriers and limitations. Enterprise logins, KYC verification, approval workflows, and admin portals all require a person at the keyboard. Someone needs to sign up, authenticate, pay, and manage all of these services. And that someone has always been a human, until now. io.net is changing this. Agent Cloud uses an MCP library to remove the need for a human in the equation, marking a genuine turning point in how autonomous AI system
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.
AI infrastructure was built for humans. And it comes with human barriers and limitations. Enterprise logins, KYC verification, approval workflows, and admin portals all require a person at the keyboard. Someone needs to sign up, authenticate, pay, and manage all of these services. And that someone has always been a human, until now. io.net is changing this. Agent Cloud uses an MCP library to remove the need for a human in the equation, marking a genuine turning point in how autonomous AI system
Lambda Labs is know as the home for the "SSH-and-go" developer. With its academic simplicity and pre-configured deep learning stacks, Lambda Labs has positioned itself as a gold standard for researchers in need of a GPU cloud solution. But, the game is changing. In 2026, with production models demanding thousands of synchronized GPUs and global inference footprints, this centralized boutique cloud model is being pushed to its limits. AI developers are up against a new reality: the so-called "P
The AI landscape of 2026 is now more of a battle of over infrastructure instead of a clash of models. For many AI/ML developers, CoreWeave has been the reliable "specialized" choice for NVIDIA hardware. However, as the "Power Wall" of 2026 makes electricity and high-density data center space more precious than the chips themselves, many startup and enterprise teams are searching for alternatives that offer both better availability and more affordable pricing. So, if you’ve been priced out by Co
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.
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.
AI infrastructure was built for humans. And it comes with human barriers and limitations. Enterprise logins, KYC verification, approval workflows, and admin portals all require a person at the keyboard. Someone needs to sign up, authenticate, pay, and manage all of these services. And that someone has always been a human, until now. io.net is changing this. Agent Cloud uses an MCP library to remove the need for a human in the equation, marking a genuine turning point in how autonomous AI system
Lambda Labs is know as the home for the "SSH-and-go" developer. With its academic simplicity and pre-configured deep learning stacks, Lambda Labs has positioned itself as a gold standard for researchers in need of a GPU cloud solution. But, the game is changing. In 2026, with production models demanding thousands of synchronized GPUs and global inference footprints, this centralized boutique cloud model is being pushed to its limits. AI developers are up against a new reality: the so-called "P
The AI landscape of 2026 is now more of a battle of over infrastructure instead of a clash of models. For many AI/ML developers, CoreWeave has been the reliable "specialized" choice for NVIDIA hardware. However, as the "Power Wall" of 2026 makes electricity and high-density data center space more precious than the chips themselves, many startup and enterprise teams are searching for alternatives that offer both better availability and more affordable pricing. So, if you’ve been priced out by Co
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
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.
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.
18 production-ready AI agents for NLP, market data, & automation on io.intelligence. Consolidate your AI stack with one API.
Complete technical guide to decentralized compute: benchmarks, cost calculator, compliance checklist, and step-by-step migration from AWS/GCP.
Your GPU data center investment framework. Compare TCO for cloud, colo, & workstation, including power, cooling, ROI, and hidden costs.
