io.net Re-poster

Affordability. Check.

Accessibility. Check.

Yes, @ionet lets you spin up GPUs in seconds at up to 70% less than hyperscalers.

But, that's only part of the story.

When you move away from centralized compute you also increase resilience, flexibility, and data security.

In a time of global instability, these matter more than ever.

@ionet CEO @Gaurav_ionet offers his thoughts on how distributed compute can help ensure critical systems stay online, even when centralized data centers go down.

Most AI teams aren’t limited by their ideas.

They’re limited by access to compute.

No matter how great the idea, the team, or the tech, if you can't get access to the right GPUs at affordable prices your project can't grow.

https://t.co/IjHEvTwGWy needed hundreds of GPUs to support real-time image generation at scale.

Using hyperscaler would have meant getting crushed by traditional cloud pricing and procurement delays.

But with https://t.co/ZuybGWvjv9 they were able to:
- Cut GPU costs by 50%+
- Provision faster
- Test new hardware sooner
- Keep scaling without slowing product velocity

The result? They grow from 14K → 19M users in a year.

Making AI affordable and accessible isn't a nice to have, it's a must have.

Affordable and accessible compute gives teams a fair chance to compete, bring their products to market, and create sustainable businesses.

Without it, we get something dystopian.

Companies now using surveillance software to tracks every click employees make at their computers to train AI to replace them.

This is what happens when you put profits over people, competition, and innovation.

@ionet we know first hand how important affordable and accessible AI is, and have built a platform to make it possible. 70% cheaper than AWS. No waitlists.

Check out our Head of Brand Strategy talk about the choices behind AI development in today's @Independent

Hyperscaler customers have caught a case of Supply Chain Paranoia.

And it's not surprising.

AI companies are hoarding GPUs or reserving capacity blocks as a business tactic against competitors.

But startups and enterprises don't need billions in the bank to tap into this idle compute.

With https://t.co/ZuybGWvRkH you bypass the supply chain paranoia.

Our decentralized GPU network gives you immediate access to the 95% of idle compute, at prices up to 70% less than hyperscalers.

If you want to build and ship without the fear, we've got you covered.

Deploy GPUs today, or whenever you need them:

Major tech companies are laying off up to 10% of their workforce, and blaming AI.

They are spending hundreds of billions of dollars on new data centers while up to 85% of existing GPUs are being underutilized due to inefficient infrastructure.

This isn't a human resources issue, it's what AI looks like when everything happens behinds closed doors and is controlled by a handful of companies.

It is AI for the few, not the many.

We believe in AI for the many, not the few.

@ionet makes underutilized GPUs from around the world instantly accessible at prices that are 70% less than major hyperscalers, so that anyone anywhere can build great products, and sustainable businesses.

Choosing the right GPUs for your project isn't about picking the "best" one.

It's about choosing the right one. For the right job. At the right time.

Each GPU has a different price/performance ratio. Understanding how to cluster them for your unique workloads can make the difference between burning through your runway, and having the resources to scale your project

Our new GPU cluster cheat sheet helps you get it right:

• H100 vs A100 vs L40S (when to use each)
• Cluster configs that actually work
• Networking + NCCL sanity checks
• Cost optimization rules that save real money

Check out the full guide:

According to a recent study, GPU utilization across enterprise servers sits is at 5%.

Yes, just 5%.

That means 95% of provisioned GPU capacity is not being used.

Hyperscalers are putting people on waitlists, costs continue to rise, billions are being spent on new data centers and utilization sits at 5%.

Something is very wrong here. We should be increasing access to AI, not hoarding it.

That's why @ionet gives you the flexibility to access affordable compute when and how you need it by orchestrating underutilized GPUs from around the world.

The US has 5,427 data centers.

That's 10X more than any other country.

This kind of centralization and concentration of power only benefits the few, not the many.

While hyperscalers continue buying up the market, the majority of the worlds developers and AI startups don't have access to the tools and resources they need to even compete.

This isn't okay. It limits opportunity. It limits innovation. And, it makes AI worse.

That's why we built a platform that makes AI accessible to everyone, everywhere.

Find out more at https://t.co/ZuybGWvjv9

Source:

Hyperscalers will soon control 2/3 of global data center capacity.

This will allow them to further control access, set terms and prices that exclude all but the largest companies, and ultimately decide who gets to participate in the AI revolution.

Except when they can't.

Open networks like https://t.co/ZuybGWvjv9 are pushing back by offering affordable and accessible compute to everyone, everywhere.

No backroom deals. No hidden costs. No gatekeeping. Transparency. Access. And prices that are 70% less.

The future of AI isn't for the few, it's for the many.

https://t.co/ZuybGWvjv9 is turning on the lights.

Haven't gotten a chance to read full the @ionet tokenomics litepaper yet?

No problem, here's the lowdown.

The IDE:

→ Replaces fixed, inflationary token emissions with a dynamic, demand driven model

→ Provides GPU supplier income in USD-targeted payouts to remove token price volatility

→ Burns at least 50% of remaining emissions supply over time, creating real scarcity tied to real usage

→ Introduces a dual-vault system that protects suppliers in downturns, absorbing surplus in upturns

Find out more:

AI infrastructure was built for the few, not the many.

Private deals are negotiated behind closed doors. The biggest players pay less. Everyone else gets in line. And most of us are left in the dark.

But there is a better way.

AI shouldn't be a private club few people can afford. It should be an open network everyone can access.

Bold ideas are born in the light. Limiting ideas are born in darkness. When we build in the open, we make space for creativity, collaboration, and innovation.

The lights are on.

Read more about it here:

The feedback period has come to a end for https://t.co/ZuybGWvRkH's new tokenomics.

We're excited to share that the feedback has been overwhelmingly positive!

In fact, it helped inspire another change. But, this one we are going to keep a bit of a surprise.

We hope you can hang on for just a few more days, because we will release both together one week from today...

The ability to quickly spin up GPUs is important for any AI project.

So is the ability to scale.

The next wave of AI infra isn’t about containers, it’s about instant access, scalability, and affordability.

We put RunPod and https://t.co/ZuybGWvjv9 side by side in our latest guide to see how https://t.co/ZuybGWvjv9's GPU orchestration solves many of the challenges faced by growing projects.

https://t.co/ZuybGWvjv9 turns thousands of global GPUs into one programmable network:
- Instant clusters (not waitlists)
- Low latency by design
- 50–75% cost savings

Spend less time building your infrastructure, and more time building your product.

AI agents that can’t deploy and manage their own compute, are like autonomous cars that can’t plug themselves in.

They are autonomous. To a point.

https://t.co/ZuybGWvjv9’s new Agent Cloud is moving that point.

Agent Cloud gives agents full autonomy to buy, deploy, and manage compute resources when they need them and how they need them.

Find out more at:

We all know there are massive chip shortages.

So why is Nvidia is launching features no one wants, while the majority of AI projects are struggling to get the compute they need?

Our Head of Brand Strategy called it out in a recent interview:

"While the gaming world debates whether Nvidia’s new AI graphics are “AI slop,” there’s a bigger question: why is the world’s leading GPU company investing in cosmetic features when we’re in the middle of a compute crisis?"

Check out the full article here:

Different clouds serve different purposes.

Lambda Labs is great for research. But when you need to scale, the picture changes.

- GPUs go out of stock
- Limited to centralized regions
- Hard to scale to production workloads

This is where https://t.co/ZuybGWvjv9's decentralized cloud comes in.

- Instant access to H100/H200 GPUs (no waitlists)
- Global infrastructure for low-latency inference
- Up to 70% cheaper than traditional providers

Same workflows, but with more scale.

Check out our new guide to see the full comparison:

Many AI projects waste up to 70% of their GPU budget on idle resources.

Think about that.

Teams are investing a substantial part of their budgets on something they aren't using.

This is what centralized hyperscalers do. They force teams into long contracts that can't be changed, and have them pay for resources they aren't even using.

Seem unfair? It is.

With https://t.co/ZuybGWvjv9, you can spin up and down resources as you need them, and only pay for what you use.

Seem better? It is.

Find out more at

Many AI projects waste up to 70% of their GPU budget on idle resources.

Just think about that.

AI projects are investing a substantial part of their budgets on something they aren't using.

But, this is what centralized hyperscalers do. They force teams into long contracts that can't be changed, and have them pay for resources they aren't even using.

Seem unfair? It is.

When you switch to https://t.co/ZuybGWvjv9, you can spin up and down resources as you need them, and only pay for what you use.

Seem better? It is.

Find out more at