Training foundation models at the scale of Cerebretron AI requires infrastructure that simply didn't exist when we started. Here's how we built it.
The Challenge
Training a model like Cerebretron AI requires thousands of GPUs working in perfect synchronization. A single node failure can waste hours of computation. Network bottlenecks can reduce effective throughput by 50% or more.
Our Approach
Custom Scheduling: We built a custom job scheduler that optimizes GPU allocation based on model parallelism requirements, data locality, and network topology. This alone improved our training throughput by 35%.
Fault Tolerance: We implemented hierarchical checkpointing with async writes, reducing checkpoint overhead from 15 minutes to under 30 seconds. Combined with automatic restart on failure, our effective uptime exceeds 99.5%.
Network Optimization: We designed a custom all-reduce implementation that accounts for our specific network topology, reducing communication overhead by 40% compared to off-the-shelf solutions.
Results
Our infrastructure now supports training runs across 10,000+ GPUs with near-linear scaling efficiency. The lessons we've learned are being incorporated into our enterprise AI infrastructure products.