Scaling ML Training to 10,000 GPUs
How we optimized our distributed training infrastructure to handle massive model training efficiently.
David Liu
CTO
GPUs in Parallel
The Challenge of Scale
Training modern foundation models requires computational resources that would have been unimaginable just a few years ago. When we set out to build infrastructure capable of coordinating 10,000 GPUs for a single training run, we knew we were entering uncharted territory.
Distributed Training Architecture
Our approach combines data parallelism, tensor parallelism, and pipeline parallelism into a unified framework. Each GPU cluster is organized into pods of 128 GPUs connected via NVLink, with pods interconnected through 400Gbps InfiniBand fabric.
Key Optimizations
- •Gradient compression reducing communication overhead by 8x
- •Asynchronous checkpointing eliminating training pauses
- •Dynamic batch sizing adapting to network conditions
- •Fault-tolerant training recovering from GPU failures in under 30 seconds
Memory Optimization
Training trillion-parameter models requires careful memory management. We implemented ZeRO-3 optimization with custom memory pooling, allowing us to train models 4x larger than naive implementations would permit on the same hardware.
Results and Impact
With these optimizations, we achieved 87% scaling efficiency from 1,000 to 10,000 GPUs—meaning we retained 87% of the theoretical speedup. Training runs that previously took 3 months now complete in under 2 weeks.
What's Next
We're already working on scaling to 100,000 GPUs and beyond. The techniques we've developed—and are making available through our platform—will enable the next generation of AI breakthroughs.
David Liu
CTO
David leads engineering at 1.ML, previously a Principal Engineer at OpenAI where he built distributed training infrastructure.