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.
Complete comparison of GPU vs CPU for AI: deep learning performance, hardware cost, TCO, and ideal use cases. Choose the right processor for your training and inference workloads.
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.
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.
io.net partners with CreatorBid to scale AI image models using decentralized GPUs, powering the future of AI agents and creator economy tools.
DeepSeek's breakthrough AI model disrupts Western tech dominance, sparking global competition while highlighting the growing need for efficient compute infrastructure.
io.net powers DefAI with decentralized GPU infrastructure, enabling censorship-resistant AI agents for DeFi at 90% lower costs than Big Tech.
SQD.ai partners with io.net to scale decentralized blockchain data processing, cutting costs 90% while powering AI agents with petabyte workloads.
IO launches Dev Hub community for builders using IO Intelligence. Early adopters like Soh are creating innovative AI agents and sharing projects.
Scale AI infrastructure for 90% less with decentralized GPU networks. Avoid Big Tech pricing while maintaining enterprise performance for startups.
Decentralized GPU networks cut AI training costs by up to 70%, boost flexibility, and overcome centralized cloud bottlenecks for scalable, global ML.
Multi-agent systems are the future of autonomous work. io.net's decentralized GPUs enable seamless collaboration between AI, robots, and IoT devices.
Blockchain meets cloud computing: io.net uses smart contracts and Solana for instant GPU payments, automated rentals, and zero middlemen fees.
AI agent Zerebro taps io.net's decentralized GPU network to power Ethereum validation, merging autonomous AI with blockchain infrastructure.
Everyone’s chasing affordable, scalable computing power these days. So platforms like io.net, Akash, and Render Network are all stepping in to meet that demand. They’re all leveraging underused resources, but each one has its own unique approach. So, how do they actually compare when it comes to approach, cost, performance, and scalability? Let’s break it down. io.net: The Internet of GPUs io.net is laser-focused on cranking out affordable, scalable GPU power for AI and machine learning. And h
"The 2025 GPU shortage drives high costs and limited access, but IO Cloud offers decentralized, scalable, and affordable GPU power worldwide."
