Distributed training infrastructure is the silent killer of ML projects—not the model itself.
The real bottlenecks:
• Manual role config per node (coordinator, worker, parameter server)
• Memory estimation that assumes uniform hardware but breaks on heterogeneous clusters
• Different launch scripts for different topologies (single-node multi-GPU vs multi-node)
• Cluster failures before the first forward pass even completes
Most teams spend weeks debugging NCCL timeouts, gradient synchronization bugs, and OOM errors that only appear at scale. The model training? That's the easy part.
The real bottlenecks:
• Manual role config per node (coordinator, worker, parameter server)
• Memory estimation that assumes uniform hardware but breaks on heterogeneous clusters
• Different launch scripts for different topologies (single-node multi-GPU vs multi-node)
• Cluster failures before the first forward pass even completes
Most teams spend weeks debugging NCCL timeouts, gradient synchronization bugs, and OOM errors that only appear at scale. The model training? That's the easy part.
