How We Scaled Our Distributed Training Infrastructure to 10,000 GPUs

Training foundation models at the scale of Genesis AI requires infrastructure that simply didn't exist when we started. Here's how we built it.

The Challenge

Training a model like Genesis 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.