AI in Multiple GPUs: Gradient Accumulation & Data Parallelism | Towards Data Science

Towards Data Science

by Lorenzo Cesconetto

February 23, 2026

AI-Generated Deep Dive Summary

Distributed AI techniques like gradient accumulation and data parallelism are crucial for optimizing neural network training across multiple GPUs. These methods help increase computational efficiency and scalability, enabling faster and more accurate model training. In this article, we explore how these approaches work and why they matter to AI researchers and practitioners. Gradient accumulation is a technique used when the dataset is too large to fit in GPU memory for a single optimization step. By splitting the data into smaller micro-batches, gradients are computed and accumulated over several forward and backward passes before updating model weights. This method allows for larger effective batch sizes without overwhelming the GPU’s memory limits. While gradient accumulation improves memory efficiency, it can slow down training due to sequential processing of micro-batches. Distributed Data Parallelism (DDP) addresses this limitation by leveraging multiple GPUs to process data in parallel. DDP distributes input data across GPUs, allowing each to handle a portion of the batch simultaneously. After computing gradients on their respective portions, GPUs communicate and aggregate these gradients before updating model weights. This approach significantly speeds up training, especially for small-scale GPU clusters (up to ~8 devices), where scaling is nearly linear. The combination of gradient accumulation and DDP enables efficient training across multiple GPUs while maintaining large batch sizes. This is particularly valuable for deep learning tasks requiring extensive computational resources. By understanding and implementing these techniques, AI researchers can optimize their workflows, reduce training time, and improve model performance. For those interested in advancing AI applications, mastering distributed training methods is essential to unlock the full potential of modern GPU architectures and accelerate progress in machine learning.

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Originally published on Towards Data Science on 2/23/2026