Distributed Training With Adapter Synchronization

Parameter-efficient fine-tuning — LoRA, QLoRA, adapter methods for LLMs on consumer GPUs.

Unique: Leverages PyTorch DDP's gradient synchronization to coordinate adapter training across devices while keeping base model weights frozen and non-communicating. Reduces communication bandwidth by 99%+ compared to full model distributed training because only adapter parameters (0.1-2% of model) are synchronized across devices.

vs others: Enables efficient multi-GPU training with minimal communication overhead compared to full model DDP, achieving near-linear scaling efficiency (90%+) because adapter parameters are orders of magnitude smaller than full model weights.

Parameter-Efficient Fine-Tuning (PEFT)

Unique: Integrates with PyTorch's DistributedDataParallel and DeepSpeed through the standard transformers Trainer API, requiring no PEFT-specific distributed code. Adapters are treated as regular parameters in the distributed graph, enabling seamless use of existing distributed training infrastructure.

vs others: More straightforward than custom distributed implementations because it leverages standard PyTorch/DeepSpeed primitives, while maintaining full compatibility with all PEFT methods. Enables scaling from single-GPU to multi-node training without API changes.

via “distributed training orchestration”