Gradient Checkpointing

Gradient checkpointing is a memory-saving training technique that trades compute for memory by recomputing intermediate activations during the backward pass rather than storing them from the forward pass. Standard training stores every layer's activations to enable gradient computation through backpropagation, which can require hundreds of gigabytes for large LLM fine-tuning. Gradient checkpointing strategically discards intermediate activations and recomputes them when needed, typically reducing memory by 50%-80% at the cost of 20%-30% additional compute. The technique was popularized by Chen et al. (2016) and is now standard in every major training framework including PyTorch, DeepSpeed, FSDP, Axolotl, and Hugging Face Trainer. Combined with mixed precision training, QLoRA, and FSDP, gradient checkpointing enables fine-tuning of 70B-parameter models on hardware that would otherwise require eight times more memory. AI governance teams encounter gradient checkpointing mainly in training pipeline configuration; it does not affect deployed model behavior, only training memory profile and training time.

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