Picking a distributed training framework for mixed consumer hardware? Ask these 5 questions first:
Most frameworks claim they "support" heterogeneous setups, but there's a huge difference between being designed for it vs just tolerating it. The right questions reveal whether the framework actually understands your constraints or will bottleneck on the weakest GPU in your cluster.
Key technical distinctions: Does it handle dynamic peer discovery? Can it rebalance workloads when one node drops? Does gradient synchronization account for different compute speeds, or does your RTX 4090 just sit idle waiting for that old GTX 1080?
If you're running a mix of consumer cards, the framework's scheduling algorithm and communication protocol matter way more than raw theoretical throughput.
Most frameworks claim they "support" heterogeneous setups, but there's a huge difference between being designed for it vs just tolerating it. The right questions reveal whether the framework actually understands your constraints or will bottleneck on the weakest GPU in your cluster.
Key technical distinctions: Does it handle dynamic peer discovery? Can it rebalance workloads when one node drops? Does gradient synchronization account for different compute speeds, or does your RTX 4090 just sit idle waiting for that old GTX 1080?
If you're running a mix of consumer cards, the framework's scheduling algorithm and communication protocol matter way more than raw theoretical throughput.