Infrastructure

Training large AI models requires significant compute resources. These resources are concentrated in a few hyperscalers, creating bottlenecks and single points of control. Today we announce the Zoo Compute Network, a decentralized alternative. The Compute Concentration Problem Current AI training is dominated by: Cloud providers: AWS, GCP, Azure control most AI-grade compute Hardware scarcity: H100s have year-long waitlists High costs: Training GPT-4 class models costs $100M+ Geographic concentration: Most clusters are in a few regions This concentration creates risks:...

Training large language models requires more than algorithms. It requires infrastructure: distributed training frameworks, data pipelines, experiment tracking, and evaluation harnesses. Today we open source Training Gym, our complete platform for model development. Why Training Gym? Open AI development faces an infrastructure gap. Publishing model weights is valuable, but it’s not enough. Researchers need: Reproducible training pipelines Scalable distributed training Standardized evaluation Experiment management Data processing tools Training Gym provides all of this in an integrated, open source package....