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.
Discover io.net's Incentive Dynamic Engine (IDE): an adaptive tokenomics model bringing sustainable economics and predictable stability to decentralized GPU compute.
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.
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.
KayOS, an AI startup, achieved 5x developer power with io.net. Learn how their 2-person team cut compute costs by 60% ($2.5k to $1k/month) using io.intelligence.
![AI Training vs Inference: Key Differences, Costs & Use Cases [2025]](/_next/image?url=https%3A%2F%2Fio-net.ghost.io%2Fcontent%2Fimages%2F2025%2F11%2Fio-Blog-AI-Inference-vs-Training.png&w=640&q=75)
AI training teaches models to recognize patterns. AI inference applies those models to make predictions. Learn the differences, costs, and optimization strategies in io.net’s complete guide.
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.
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.
Blockchain promised to solve centralization, but focused on wrong problems. DePIN networks like io.net finally deliver real value through affordable GPU access.
Unified Chat is the single, intelligent AI workspace that unifies every model and tool. Auto-routes for optimal quality and cost. End fragmentation.
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.
Complete financial framework for GPU infrastructure decisions. Cost modeling, ROI analysis & budget optimization for AI companies.
io.net surpasses $20M in verifiable on-chain revenue, proving decentralized GPU infrastructure can compete with AWS and GCP on cost, performance, and real-world adoption.
