Templar Has Just Broken AI’s #1 Rule: No More Data Centers Needed – Bittensor

By: CryptoZPunisher

Templar on Bittensor: when the impossible becomes possible

On Bittensor, everything is possible. And if there were one concrete proof, Templar is probably among the most striking.

The Templar team has just published an extremely dense research article, which I had to read several times to fully grasp the stakes. Let’s be honest: it’s not simple, especially for non-technical readers. But the result is huge. And I absolutely do not regret the time spent trying to understand it.

👉 This article is long, technical, demanding. 👉 What Templar has achieved in just 9 months is extraordinary. 👉 So here I’ll try a simple explanation (ELI5), followed by a clear synthesis, before inviting you to read the original paper.

The fundamental problem of modern AI

For 80 years, one constraint has dominated computing:

👉 To be extremely powerful, machines must be physically close to one another.

Large models like GPT-4 or Gemini are trained in massive data centers, where:

• thousands of GPUs sit side by side,
• connected by ultra-fast networks,
• cooled, powered, and controlled in the same place.

👉 Direct consequence: Whoever controls the data center controls the intelligence.

That’s why:

• Google
• OpenAI
• Meta
• Microsoft

dominate AI today. Not because they’re smarter, but because they control the physical infrastructure.

What Templar challenged

Templar attacked this core belief:

“You can’t train very large models without putting everything in the same place.”

With their research Heterogeneous Low-Bandwidth Pre-Training of LLMs, they show this belief is wrong.

👉 They demonstrate that you can train very large models:

• using GPUs scattered across the world,
• connected via the public internet,
• without a central authority,
• without a single data center.

And not “a little,” not “theoretically”: ➡️ At a level competitive with centralized alternatives.

Distributed ≠ Decentralized (the key distinction)

This point is essential.

🔹 Distributed

• Multiple machines work together
• A central entity controls everything

👉 Example: Google and its data centers

🔹 Decentralized

• Anyone can participate
• No authority decides who is allowed
• Value flows back to contributors

👉 Example: Bitcoin

Templar is building decentralized AI, not merely distributed AI.

ELI5 – very simple version

Imagine you want to write a gigantic book.

Before (classic model)

• Everyone must be in the same room
• Communication is ultra-fast
• If someone is far away → they can’t help

With Templar

• Everyone writes from home
• Even with a slow connection
• Only the essential is exchanged
• Small computers help as much as they can
• Big computers help more
• No single boss controls everything

👉 Result: A gigantic book written by thousands of people worldwide, with no central authority.

What Templar achieved in 9 months

This is where it becomes spectacular:

• From 1.8B to 72B parameters in 9 months
• Development of SparseLoCo, a core algorithm
• Truly permissionless training
• Integration of consumer GPUs (RTX, small clusters)
• Papers accepted at NeurIPS
• The largest truly decentralized model ever trained

All of this without a central data center, without hyperscalers, without gatekeepers.

Why this is philosophical (not just technical)

Templar isn’t just trying to “do better.”

They want to change the very nature of AI:

👉 from intelligence as private property to intelligence as public infrastructure

Like:

• electricity
• the internet
• GPS after its de-militarization

This aligns perfectly with Bittensor’s vision:

“Ensure that the ultimate commodity—intelligence—belongs to everyone.”

Why almost no one is talking about it (yet)

Because:

• Templar rejects corrupting capital
• They don’t do aggressive marketing
• They build first, talk later

But history shows one thing:

The most important infrastructures are ignored… until they become undeniable.

And Heterogeneous SparseLoCo is precisely that moment.

What comes next

• This research isn’t a marketing prototype
• It is published, peer-reviewed, reproducible
• It will be integrated into Basilica, Covenant AI’s decentralized compute platform
• It opens the path to: even larger models massive participation truly global AI

👉 The question is no longer “Is it possible?” 👉 But “How fast will this become inevitable?”

Personal conclusion

Yes, this article is difficult. Yes, it takes time. But what Templar is building is foundational.

On Bittensor, bold teams can:

• reinvent AI training
• compete with giants
• without permission
• without compromise

👉 That’s exactly why Bittensor exists.

I truly encourage you to read the original article. Even if you don’t understand everything, you’ll feel the magnitude of what’s unfolding.

AI doesn’t have to belong to a handful of corporations. With Templar, it’s starting to become shared infrastructure again.

Links

Website:

Templar > tplr.ai

Covenant ai > covenant.ai

Github:

one-covenant/templar: incenτivised inτerneτ-wide τraining

covenant.ai

X accounts:

Templar: @tplr_ai

Covenent: @covenant_ai

Founder:

X account: @DistStateAndMe

Linkendin:

Samuel Dare | LinkedIn

Covenant AI  | LinkedIn

A special thank you to Derek Barnes (@synapz_org) for this remarkable piece. This article is deep, demanding, and incredibly valuable. I truly encourage everyone to take the time to read it carefully, it’s one of those texts that reshapes how you think about decentralized AI and what’s actually possible.