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[2602.21081] Scaling Vision Transformers: Evaluating DeepSpeed for Image-Centric Workloads
Summary
This article evaluates the use of DeepSpeed to enhance the scalability of Vision Transformers (ViTs) for image-centric workloads, focusing on training efficiency across various GPU configurations.
Why It Matters
As Vision Transformers become increasingly important in image processing, understanding how to optimize their training through frameworks like DeepSpeed is crucial for researchers and practitioners. This study provides insights into improving scalability and performance, which can lead to more efficient AI applications in computer vision.
Key Takeaways
- DeepSpeed can significantly improve the scalability of Vision Transformers.
- The study identifies key factors affecting distributed training performance.
- Evaluations were conducted using datasets like CIFAR-10 and CIFAR-100.
- Understanding communication overhead is essential for optimizing training.
- Future research will focus on DeepSpeed's limitations and optimization strategies.
Computer Science > Machine Learning arXiv:2602.21081 (cs) [Submitted on 24 Feb 2026] Title:Scaling Vision Transformers: Evaluating DeepSpeed for Image-Centric Workloads Authors:Huy Trinh, Rebecca Ma, Zeqi Yu, Tahsin Reza View a PDF of the paper titled Scaling Vision Transformers: Evaluating DeepSpeed for Image-Centric Workloads, by Huy Trinh and 3 other authors View PDF HTML (experimental) 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 inve...
