io.net Re-poster

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.

https://t.co/ZuybGWvjv9 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

Unified API access for all of the leading AI models is great.

Making it affordable is even better.

Just a reminder that http://io.net is now a provider for @openrouter.

Check it out for yourself: https://openrouter.ai/provider/io-net

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 https://t.co/ZuybGWvRkH to find out more.

GPUs are a tactical lever for your growing AI project.

But how do you know you're choosing the right setup?

http://io.net’s decentralized GPU marketplace offers everything from high-VRAM enterprise units to cost-efficient consumer cards.

Here’s a quick primer 👇

Hyperscalers:
“Wait 6 months for GPUs and pay a premium.”

Us:
“Spin up a cluster in minutes and pay 70% less.”

It's an easy choice.

Our new guide can make in even easier. Check it our here:
https://io.net/blog/how-to-build-and-scale-gpu-clusters-on-io-net-a-practical-guide

Everyone wants AI.

But no one has enough chips.

The AI boom has triggered a global chip crisis. But there is already a solution.

Read more 👇
https://startupnews.fyi/2026/03/05/the-ai-boom-is-creating-a-chip-crisis/

The AI cloud infrastructure market looks totally different than a year ago.

What's driving the change? Here's the highlights:

• Inference dominating training
• Cost-per-token replacing cost-per-GPU-hour
• Multi-cloud becoming the default
• Decentralized GPU networks entering the enterprise stack

What's now a $60 billion market is expected to grow to over to over $250 billion by 2030.

But, the future of AI infrastructure isn’t about who owns GPUs.

It’s about who orchestrates them best.

Find out all about the latest trends and outlooks it our latest blog:

Times of crisis show why distributed compute is so important.

Centralized systems are fragile. Even one data center going down can take your workloads offline for hours, or days.

Distributed networks are resilient. They allow you to instantly spin up new GPUs where and when you need them.

@ionet provides access to powerful and affordable GPU clusters around the world. So no matter who you are, where you are, or what's happening, you always have the resources you need to keep your project online.

Find out more at

Chip shortages and high costs are making it hard for most AI projects to compete.

Decentralized Physical Infrastructure (DePIN) is the best solution for these challenges.

And @solana is where it's happening.

In our latest blog, we break down @ionet's place in the Solana ecosystem and why the future of decentralized infrastructure and AI compute is being built there.

Check it out:

GPU prices continue to rise. Except when they don't.

Wondera, the AI powered music creation platform, scaled to 200,000 users across 171 countries while cutting compute costs by 75%.

Lower costs. Rapid growth. Instant access to powerful GPUs. They can go together. You just need the right partner.

Find out more:

Claude has experienced an outage.

This shows why self-hosted models and decentralized compute are so important to the future of AI.
https://status.claude.com/

We woke up this morning with a question on our minds (or at least our content guy did):

Is centralizing AI similar to limiting free speech?

We'll lay out the case. You let us know what you think.

AI is built on language models. Those models are developed by a small number of companies. Those companies are driven by their desire to maintain control (and money of course).

AI seen this way is a actually a limit. It's a limit to competition. It's a limit to innovation. But it may also a limit to free speech.

When we all use these tools to write for us, the language we use isn't free. It's given to us by centralized companies. And that can't be a good thing.

The world is made better by having more voices, more ideas, and more perspectives. Basically, by being more open.

That's why open source AI matters so much. That's why access to affordable compute matters so much.

We're committed to both.

Want to help shape the future of AI one post at a time?

We are looking for some creative types who can spot the next trend before it happens, are excited about the https://t.co/ZuybGWvjv9 mission, and have the skills to spread the word far and wide.

Sound like you? Then join our Social Crew.

Give us a shout at https://t.co/OhRidhHapO to find out more.

**Community feedback period closes February 27th**

Making AI compute accessible and affordable is more important than ever.

Our new tokenomic model based on a first-of-its-kind Incentive Dynamic Engine (IDE) was built to achieve exactly that.

The IDE makes decentralized GPU networks as predictable and resilient as centralized clouds, creating a real alternative for growing AI projects everywhere.

If you haven't read the lite paper or shared your feedback, now's the time.

Here's something to get excited about.

@ionet is now a provider for @OpenRouter!

This brings decentralized GPU clusters to the world’s most flexible AI API. It will deliver faster, cheaper, and more accessible model inference for teams everywhere.

- Lower costs
- Scalable compute
- One point of access

Check it out at:

Another Monday, another story about problems with AI compute.

It looks like the massive $500 billion Stargate data center project won't be moving forward, leaving OpenAI searching for other options.

Building data centers is a long and expensive process. Even when the teams have and the money and resources to make it happen (and OpenAI, Softbank, and Oracle definitely do), it can quickly fall apart.

If massive billion dollar companies can't get the compute power they need, how will the thousands of startups and growing AI projects?

The answer is distributed compute. No long waits. No expensive building projects. Just accessible, affordable compute, right now.

Vitalik Buterin recently declared 2026 the year to “take back lost ground in computing self-sovereignty.”

But... A startup training a specialised model could burn through more compute in a week than a high-end laptop provides in a year.

So how do we protect sovereignty while ensuring developers have the resources they need to bring their projects to life?

@ionet CEO @Gaurav_ionet has an answer. Decentralised compute networks can do what crypto has always promised: distributed systems that can match and exceed centralised alternatives.

There's a lot of talk about GPU shortages.

@elonmusk thinks we can solve the problem by putting data centers in space. Very cool. Not very practical. It would take 3+ years. And AI projects need solutions today.

The real issue for most people, isn't shortages, it's access.

There are GPUs out there. What projects need is a way to easily access them and seamlessly deploy them. That's where we come in.

Star wars is great. But decentralized compute solves the problem today. May the Force of DePIN be with you.

There is a big difference between being simply good, and being good for something.

We grew out of the realization that access to high-power, affordable compute, is the line between success and failure in AI.

From day 1 we've been committed to ensuring as many teams as possible have the chance to build both great products and successful businesses.

Check out how we helped Leonardo Ai scale from 14K to 19M users while cutting GPU costs by 50%.