🧵 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: (1/7)
📍 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. (2/7)
⚙️ 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. (3/7)
🔥 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. (4/7)
🧠 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. (5/7)
🌐 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. (6/7)
🏁 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/tem... 💬 Discord: https://discord.gg/PFBxkZHF (7/7)
