arXiv:2507.09029v5 Announce Type: replace Abstract: Pre-training large neural networks at scale imposes heavy memory demands on accelerators and often requires costly communication. We introduce Subnetwork Data Parallelism (SDP), a distributed training framework that partitions a model into structured subnetworks trained across workers without exchanging activations. We study two complementary masking regimes: backward masking, which applies sparsity only in the backward step to retain unbiased gradients, and forward masking, which also removes parameters in the forward pass to deliver stronge
The continuous growth in size and complexity of large neural networks necessitates new distributed training frameworks to manage compute and memory demands efficiently.
This research outlines a method to significantly reduce memory and communication costs in training large AI models, which is crucial for scaling AI development and deployment.
Distributed training of large neural networks can become more memory-efficient and less communication-intensive, potentially lowering the barriers to entry for advanced AI model development.
- · AI developers
- · Cloud providers
- · Hardware manufacturers (specialized accelerators)
- · Legacy distributed training frameworks (non-optimized)
- · Companies with limited compute budgets (if they don't adopt similar techniques)
Reduced training costs and time for very large AI models, accelerating their development and improving accessibility.
Increased competition in the AI model development space as more actors can train high-performance models efficiently.
Faster progress in AI capabilities across various domains due to the ability to train larger, more complex models with fewer resource constraints.
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Read at arXiv cs.LG