Into:τemplar
The world's GPU cluster, owned by nobody.
As of · Jun 4, 10:37 UTC
The that trained Covenant-72B, a 72-billion parameter language model, using decentralized infrastructure. It is the largest model trained on decentralized compute to date. The subnet is currently in a 100% burn period while the team builds the next phase: Templar Crusades.
What is τemplar
τemplar is a decentralized AI training framework that coordinates GPU owners around the world to train a single AI model collaboratively. Individual nodes contribute compute, and the system's incentive mechanism ensures only quality contributions are integrated into the model.
The simple version: Imagine building a skyscraper where every construction worker brings their own crane. Nobody owns the whole site, but the building still gets built. τemplar does this for AI model training: your GPU contributes a piece of the work, and the finished model belongs to everyone.
Centralized equivalent: Think OpenAI's training infrastructure or Google DeepMind's TPU clusters, but built from individually-owned GPUs coordinated over the internet.
How it works:
- synchronize with the global model state, receive deterministic data subsets for each training window, compute gradients locally, compress them using DCT (Discrete Cosine Transform) with top-k selection, and upload to shared storage. They also gather and aggregate peer gradients to keep their local model current.
- retrieve the same data assigned to each miner, apply that miner's submitted gradient to a model copy, and measure whether the model's loss actually decreased. Miners are scored based on how much their gradients improve the model. Only beneficial updates are integrated.
Why This Matters
Other research from the same neighborhood of the network.
