Finetuning LLMs on a Single GPU Using Gradient Accumulation

Gradient accumulation offers a single-GPU alternative to multi-GPU sharding for fine-tuning large language models when memory limits force impractically small batch sizes. Using BLOOM-560M for IMDB sentiment classification, the example shows that batch size 1 produces noisy gradient updates, fluctuating losses, and 78.53% test accuracy despite mixed-precision training with Lightning Fabric. By accumulating gradients across 16 microbatches before updating model parameters, the training process simulates a batch size of 16 without increasing memory use, yielding more stable loss behavior and improving test accuracy to 87.37% after one epoch. Although gradient accumulation does not reduce total computation and may be less efficient than directly fitting a large batch in memory, it is useful for overcoming hardware constraints; fewer optimizer updates can also improve runtime in some settings. The example further reports that applying PyTorch 2.0’s torch.compile reduced training time from about 51 to 43 minutes while retaining comparable performance, reaching 87.91% test accuracy.

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