Michael Taolor ⚡️ (τ , τ)

🧵 Templar: The Subnet Turning the Holy Grail of Decentralized Training Into Reality

This week’s Novelty Search, hosted by @const_reborn and @opentensor, showcased the stunning progress of Templar Subnet 3 (#SN3),  @tplr_ai.

Templar is training real models with 200+ GPUs in a fully permissionless, incentivized system.

This isn't a testnet. This is the future of DeAI infrastructure.

Here’s everything you need to know:

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📍 What is Templar?

• Subnet 3 on Bittensor, designed for decentralized  model training.

• Incentivizes miners to submit gradients that reduce loss.

• Validators test, filter, and aggregate gradients each epoch (7 blocks = 84 seconds).

• Gradients are stored on R2 buckets—validators verify quality and timing.

• Fully adversarial from day one. Miners are anonymous and permissionless—yet it works.

Templar turns decentralized compute into a coordinated intelligence engine.

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⚙️ Key Innovations

• Live training: The subnet is actively training a 1.2B parameter model now.

• Incentive-layer design: Gradient scoring is embedded directly into the validator protocol.

• Auto-sync enforcement: Validators enforce strict upload windows using on-chain timestamps + R2 object metadata.

• Permissionless participation: Anyone with a GPU can join—but to earn, they must perform.

• Adversarial resilience: 200+ test runs, countless exploits patched. Still standing.

No other team has pulled off this level of open, incentivized coordination.

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🔥 Why Templar Matters

• Major labs like OpenAI & DeepSeek train behind closed doors with $100M+ budgets.

• Templar proves that open networks can coordinate compute at scale.

• This isn’t "decentralized compute"—this is decentralized training.

• Real-time model optimization. Live adversarial testing. Fully open architecture.

Other teams are still fundraising. Templar is shipping.

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🧠 From Control to Collaboration

• Created by @formalised_tensor (Discord handle), Templar originally founded as a company—then given to the community.

• Slashing, exploits, bandwidth races—miners are pushed to the edge, adapting and evolving with every block.

• Templar taught us that incentivized open source can challenge closed labs.

And they’re already preparing for a 70B parameter run.

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🌐 Only Possible on Bittensor

• Subnet-native incentives via $TAO.

• Watchtower + Docker = rapid iteration. 200+ protocol updates tested in production.

• R2/S3 buckets + blockchain timestamps = globally verifiable data sync.

• Miners become shareholders through open tokenomics—meaning they defend and improve the system they earn from.

#Bittensor’s infrastructure makes this level of coordination possible. Templar proves it.

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🏁 Conclusion

Templar (Subnet 3) is the first working proof that decentralized AI training at scale is not a fantasy.

It’s not theory. It’s not a demo. It’s live.

Incentivized compute. Permissionless contribution. Production-quality results.

If you're still watching from the sidelines, this is your call to dig deeper.

This is what $TAO was made for.

Explore Templar:
🌐 Website: https://www.tplr.ai/
💻 GitHub: https://github.com/tplr-ai/templar
💬 Discord: https://discord.gg/PFBxkZHF

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