Training each GPU on a fixed overlapping subnetwork and averaging shared parameters cuts per-device memory by up to 60 percent without exchanging activations, matching DDP accuracy under FLOP-matched budgets.
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Model Parallelism With Subnetwork Data Parallelism
Training each GPU on a fixed overlapping subnetwork and averaging shared parameters cuts per-device memory by up to 60 percent without exchanging activations, matching DDP accuracy under FLOP-matched budgets.