Training large models on heterogeneous GPU clusters — mixing A100, H100, and consumer-grade accelerators — introduces failure modes that standard checkpoint-restart strategies handle poorly. Lost progress and idle GPU time compound quickly at scale.
System Design
Our fault-tolerance layer maintains redundant gradient copies across worker groups with erasure coding. When a node fails, surviving workers reconstruct the lost gradient shard in-flight without rolling back to the last checkpoint.
Evaluation
- Training restarts eliminated: 94% reduction vs. checkpoint-only recovery
- Recovery time on node failure: <8 seconds (vs. 4–12 minutes checkpoint reload)
- Communication overhead: +7% vs. standard FSDP
- Tested on clusters up to 512 GPUs across 3 cloud providers
Availability
This infrastructure layer is available to enterprise partners as part of our distributed training engagement. Contact us for integration documentation and cluster sizing guidance.