(Read time: 5 minutes)

What If AI Didn’t Belong to Big Tech?

Imagine this: instead of OpenAI or xAI training the next frontier model, it’s powered by thousands of people around the world. Gamers, developers, even students—contributing idle GPUs to train something big, powerful, and open.

That’s the vision behind decentralized AI training.

It’s one of the most exciting corners of Crypto AI. And while we’re still early, the pieces are starting to come together.

Sounds exciting, right? But we’re not quite there yet.

Today, most big models are trained in mega-scale data centers—rooms packed with tens of thousands of GPUs humming in perfect sync.

Why? Because training a frontier model takes speed and precision.

Data centers nail both:

• GPUs are physically close, which makes data transfer fast.

• They use tricks like data parallelism (splitting training across GPUs) and model parallelism (splitting the model itself).

This matters when training costs can hit $50M+ per model. Every efficiency counts.

Why Decentralized Training Is Hard

Here’s the problem: GPUs in a decentralized network are far apart—physically and digitally.

Trying to train across scattered hardware is like hosting a group Zoom call with people on dial-up in three different countries. Everything lags.

That makes it hard to keep GPUs in sync. And when training depends on step-by-step precision? That’s a killer.

But the game is changing.

Researchers and developers are finding clever ways to work around these limitations. For example:

• Prime Intellect’s “local steps” technique: Instead of GPUs syncing up every single step, they perform up to 500 steps independently before syncing. Less back-and-forth = faster across distance.

• Nous Research’s DisTrO approach: This method reduces the amount of data that needs to be shared by 1000 times. Makes smaller-scale decentralized training viable.

These methods represent exciting steps forward, even if they’re not yet practical for the largest models like GPT-4. Still, they’re proof that decentralized training can work, especially for smaller or specialized AI models.

Why This Matters Long-Term

Big Tech’s data centers are powerful—but they’re not infinite.

There’s only so much space, power, and hardware you can cram into one location. Decentralized networks can grow globally, tapping unused compute at scale.

This opens the door to:

• Training niche models for underserved industries.

• Open-source ecosystems where AI is co-owned, not black-boxed.

• Token incentives that reward people for contributing compute, governance, or data.

Some researchers believe that, one day, decentralized networks could harness more computing power than even the largest data centers.

Decentralized Training = Real Crypto Utility

Decentralized training is a long-term vision, but its impact could be huge.

Imagine a world where creating powerful AI doesn’t depend on a few big companies but instead relies on contributions from people all over the globe.

Tokens could play a big role here, rewarding people who contribute their computing power to these networks. If these networks succeed, we could see the rise of an open-source AI ecosystem. One where AI models are collectively owned and managed.

If these networks work, we’ll have a real shot at building open AI infrastructure—without needing a trillion-dollar war chest.

It’s early. But it’s real.

What’s Next?

You’ve now made it through 8 lessons—and you’re officially ahead of 99% of the internet when it comes to understanding Crypto AI!

Next: check out the rest of our learning portal for more interesting and useful reads.

Cheers,

Teng Yan