Scaling Vision Transformers: Evaluating DeepSpeed for Image-Centric Workloads

2026-02-24

AI Summary

This study addresses the challenges of high computational and memory costs, as well as inefficient scaling, faced by Vision Transformers (ViT) during large-scale training. It presents the first systematic application of the DeepSpeed framework—originally developed for language models—to ViT-based vision tasks. By leveraging data parallelism, gradient accumulation, and multi-node GPU configurations to modulate batch size and other hyperparameters, the work comprehensively evaluates strong and weak scaling performance across diverse software and hardware environments. The findings uncover key factors influencing the distributed training efficiency of ViT models and demonstrate DeepSpeed’s adaptability and optimization potential for vision tasks. This research provides empirical evidence and practical guidance for efficiently training large-scale ViT architectures.

Technology Category

Computer Vision: Large Vision ModelsMachine Learning: Deep Neural Architectures and Foundation ModelsNatural Language Processing: Learning & Optimization for NLP

Application Category

Security and Privacy: Large-scale security measurementsSearch and Retrieval-Augmented AI: Efficiency and scalability of Web search enginesEconomics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Vision Transformers (ViTs) have demonstrated remarkable potential in image processing tasks by utilizing self-attention mechanisms to capture global relationships within data. However, their scalability is hindered by significant computational and memory demands, especially for large-scale models with many parameters. This study aims to leverage DeepSpeed, a highly efficient distributed training framework that is commonly used for language models, to enhance the scalability and performance of ViTs. We evaluate intra- and inter-node training efficiency across multiple GPU configurations on various datasets like CIFAR-10 and CIFAR-100, exploring the impact of distributed data parallelism on training speed, communication overhead, and overall scalability (strong and weak scaling). By systematically varying software parameters, such as batch size and gradient accumulation, we identify key factors influencing performance of distributed training. The experiments in this study provide a foundational basis for applying DeepSpeed to image-related tasks. Future work will extend these investigations to deepen our understanding of DeepSpeed's limitations and explore strategies for optimizing distributed training pipelines for Vision Transformers.

Problem

• Vision Transformers
• scalability
• distributed training
• computational demands
• memory requirements

Innovation

• Vision Transformers
• DeepSpeed
• distributed training
• scalability
• image-centric workloads