Heterogeneous hardware is the biggest pain point nobody talks about in distributed training. You've got a cluster with RTX 3090s mixed with 4090s, different VRAM configs (12GB vs 24GB), wildcard network speeds, and that one cursed node bottlenecking everything.
Most training frameworks assume homogeneous setups. They load balance naively, sync globally, and choke when hardware specs diverge. Real-world open source devs are stuck babysitting clusters, manually partitioning workloads, or just accepting terrible GPU utilization.
The core issue: frameworks treat all nodes equally. But a 3090 shouldn't get the same batch size as a 4090. Slow network links need async gradient updates. Memory-constrained nodes need different model sharding strategies.
This is why projects like DeepSpeed, Megatron, and distributed PyTorch still feel clunky for heterogeneous setups. They weren't designed for the messy reality of community compute or multi-gen GPU pools.
Most training frameworks assume homogeneous setups. They load balance naively, sync globally, and choke when hardware specs diverge. Real-world open source devs are stuck babysitting clusters, manually partitioning workloads, or just accepting terrible GPU utilization.
The core issue: frameworks treat all nodes equally. But a 3090 shouldn't get the same batch size as a 4090. Slow network links need async gradient updates. Memory-constrained nodes need different model sharding strategies.
This is why projects like DeepSpeed, Megatron, and distributed PyTorch still feel clunky for heterogeneous setups. They weren't designed for the messy reality of community compute or multi-gen GPU pools.