The Challenge
Training state-of-the-art language models requires coordinating thousands of GPUs across multiple data centers. Codelab's existing infrastructure suffered from coordination bottlenecks and data residency constraints that prevented efficient multi-region training. Traditional orchestration systems introduced unacceptable latency overhead for gradient synchronization.
The team needed an architecture that could scale to thousands of nodes while respecting jurisdiction-specific data governance requirements. Cross-border data transfers for model checkpoints violated compliance policies, yet centralized training in a single region left compute capacity underutilized.
The Ooto Solution
Codelab deployed Kernel v2.1 across sovereign GPU clusters in three continents. The Neural Mesh Protocol coordinates distributed training with cryptographic isolation between regional boundaries. Zero-copy memory architecture eliminates serialization overhead for gradient exchanges, reducing synchronization latency by 60%.
The distributed consensus scheduler optimizes batch allocation based on current GPU availability and network topology. When nodes fail or become degraded, the system automatically redistributes workload without human intervention. Every training checkpoint remains cryptographically signed and immutably logged across the mesh.
