When to Use PyTorch Lightning or Lightning Fabric

PyTorch Lightning and Lightning Fabric are complementary frameworks for scaling PyTorch training across multiple devices and nodes, sharing consistent configuration options for accelerators, device counts, node counts, and distributed strategies such as DDP, FSDP, and DeepSpeed. Lightning Fabric is the lower-level, lightweight option, centered on a Fabric class that adds capabilities including mixed precision, distributed execution, checkpointing, and communication utilities while preserving an existing PyTorch training loop with minimal changes. PyTorch Lightning provides a higher-level managed training experience through LightningModule, Trainer, and LightningDataModule, reducing boilerplate by organizing model logic, training processes, and data handling into standardized components. Fabric is intended for users who need close control over custom training workflows, while PyTorch Lightning is suited to conventional models and training loops where concise, reusable code is more important. Both form part of the broader Lightning AI ecosystem alongside TorchMetrics and are used in projects such as Lit-GPT, SDXL, NVIDIA NeMo, HyenaDNA, and other research and production tools.

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