Distributed TrainingPrice

(SN38)

Subnet 38 is a specialized division within the Bittensor ecosystem focused on distributed training. Unlike inference-based subnets that merely run existing models, SN38 coordinates a vast network of individual miners to collectively train massive Large Language Models (LLMs). By pooling decentralized compute power, the project aims to match the capabilities of major centralized AI firms through collaborative development, essentially creating a decentralized supercomputer for model training.

Backprop Finance, also known as DSTRBTD, is the founding organization responsible for developing and maintaining the Distributed Training subnetwork. They provide the technical framework, including whitepapers and documentation, that outlines the long-term vision for decentralized AI training. Users and miners look to this team for the strategic roadmap and architectural updates governing the subnet's growth and technological implementation.

Mining on SN38 is resource-intensive and requires high-end hardware. Participants typically need NVIDIA GPUs with at least 12GB of VRAM, though high-tier cards with 24GB or more are preferred for optimal rewards. Additionally, high internet bandwidth is critical because miners use the Hivemind library to communicate constantly. Proper firewall configuration and stable peer-to-peer connectivity are essential to avoid errors during the decentralized averaging steps of the training process.

The SN38 token serves as a subnet-specific asset that represents a stake in the subnet's success. It is used to incentivize miners who provide the necessary compute power for training large-scale models. The token allows participants to engage with the subnet's internal economy, and it functions as a reward mechanism for those contributing to the collective training goals of the network.

The Butterfly All-Reduce is a key technical algorithm used by SN38 to synchronize model training across thousands of independent computers. It allows miners to average their model weights across the network without relying on a central server. This ensures that every participant is contributing to the same global model simultaneously, overcoming the massive bandwidth bottlenecks that traditionally make distributed training slower than centralized server clusters.

The primary differentiator for SN38 is its collaborative training architecture. While other subnets often have miners compete to submit the best individual model, SN38 requires miners to work together on a single unified model. This cooperative method is designed to prove that decentralized coordination can scale to train massive models with 70 billion or more parameters, which were previously only achievable by large centralized corporations with massive private data centers.