io.net

Introducing the Incentive Dynamic Engine (IDE), a demand driven tokenomics model for DePIN. Real fees → real burns. USD pegged supplier rewards. Dynamic, usage based supply. Deflation > dilution. Litepaper: io.net/tokenomics

Alibaba Cloud just announced it will raise prices by over 30%. And no one is surprised. This is just the latest hyperscaler to raise prices while posting massive profits. Centralized tech giants are thriving, while small and growing AI projects struggle to pay for the compute needed to keep the lights on. But there is another way. If you want to learn how to instantly deploy high-performing GPU clusters while protecting your budget, check out this guide: io.net/blog/gpu-clust…

Most GPU pricing pages are designed to confuse you. But we believe in transparency. We put io.net, CoreWeave, and the main alternatives side by side — same workload, real numbers. If you're scaling AI inference or training, this post can help you find the right solution. io.net/blog/io-vs-cor…

Claude went down, again. Even if it's a limited outage this time, it may not be next time. Centralized AI infrastructure will always be fragile. That's why decentralization is so important. @ionet's decentralized GPU cloud means no single outage takes everything with it. If you want to make sure your workloads keep running, head over to io.net

$1 trillion. Just let that sink in. While the majority of AI projects are struggling to get accessible and affordable compute, Nvidia is projecting $1 trillion in profits from new chips. The current AI market doesn't work for startups and growing companies. But, it doesn't have to be that way. If you're looking to build innovative products, and a sustainable business, io.net's distributed compute platform can help. 70% cheaper than hyperscalers. No Waitlists. io.net is for builders, not billionaires (or trillionaires).

@fekuuuu @ionet_community Don't you worry, we would never let any harm come to small furry animals. The release of the final Litepaper is due on March 31st.

Time to release some IDE information @ionet or this team of cat & hamster will keep the mouse as collateral. your move @ionet_community

You can pay hyperscaler prices if you want. Or you can save money, extend your runway, and get instant access to the compute power you need, when you need it. Run NVIDIA H100 from $1.19/hr. Access +5,000 GPUs in Minutes. cloud.io.net/cloud/virtual-…

A 2 person team operating with 10 person efficiency is good. Doing it while saving 60% on infrastructure is better. KayOS, a company building living world models of organizations, plugged into @ionet and multiplied their dev power by 5x. They didn't hire 5 more engineers. They didn't burn out their team. They got instant access to leading models and high power compute at prices that allowed them to focus on building, not their runway. Find out more at io.net/blog/kayos-cas…

Ready to give your project the runway it needs to succeed? Get started: cloud.io.net

Region Locking For latency-sensitive training, lock your cluster to a specific geographic region (e.g., us-east) to ensure the fastest possible interconnects.

AI startups spend up to 60% of their budget on Infrastructure. And that number can increase by 300% year. Finding the right compute solution can make the difference between building a successful business and reaching the end of your runway. io.net is already 70% cheaper than AWS. But, there are are additional moves you can make that can further optimize for performance and price, including the right size, fault-tolerant, tiers, and region locking. Here's what to do:

AI agents don’t behave like other AI workloads. They run long sessions, call multiple models, burst unpredictably, and idle between steps. This requires a change in how we think about GPU provisioning. Clouds that were built for inference and training, make the economics of agents unsustainable. And something needs to change. Find out more in our blog: AI Agent Infrastructure — The GPU Cloud Workload Nobody Planned For io.net/blog/ai-agent-…

It's always tricky to make predictions about AI. By the time you do everything has already changed. But, we have seen some interesting trends that we think will shape the next 12 months+: → Inference dominance → Increased decentralization → Token deflation → Sovereign fragmentation Want to be sure you're ready? Head over to io.net to find out more.

NVIDIA alternatives do exist. AMD MI300X and Blackwell B200, which are now available on-demand on io.net, give teams looking for high-memory alternatives exactly what they need. Deploy your first cluster on io.net: cloud.io.net

For inference + scaling, go with the RTX 4090, the king of throughput-per-dollar. For quantized models or batch inference, these units provide massive parallelization at roughly 75% less cost than centralized alternatives.

GPUs are a tactical lever for your growing AI project. But how do you know you're choosing the right setup? io.net’s decentralized GPU marketplace offers everything from high-VRAM enterprise units to cost-efficient consumer cards. Here’s a quick primer 👇