Petascale Distributed Training: Lessons from Training 10T Models
Training the largest AI models requires orchestrating thousands of accelerators. This paper shares our experience scaling to 10 trillion parameters.
Scale Challenges
At this scale, everything breaks:
- Communication becomes the bottleneck
- Memory hierarchies are insufficient
- Fault tolerance is mandatory
- Debugging is nearly impossible
The 7lineas Training Framework
Our framework addresses these challenges through:
Hierarchical Parallelism
- Data parallelism across nodes
- Pipeline parallelism across stages
- Tensor parallelism within stages
Communication Optimization
- Gradient compression (8x reduction)
- Overlapped computation and communication
- Topology-aware collective operations
Fault Tolerance
- Asynchronous checkpointing
- Elastic scaling
- Automatic node replacement
Results
Training statistics:
- 4096 H100 GPUs
- 3 weeks wall-clock time
- 99.2% hardware utilization
- 0 training restarts required
Key Lessons
- Over-engineer reliability - Failures are certain at scale
- Optimize the bottleneck - Profile relentlessly
- Design for debuggability - You will need it
- Start small - Validate at reduced scale first