arXiv:2510.01565v4 Announce Type: replace Abstract: Diffusion Transformer (DiT) models excel at generating high-quality images through iterative denoising steps, but serving them under strict Service Level Objectives (SLOs) is challenging due to their high computational cost, particularly at larger resolutions. Existing serving systems use fixed-degree sequence parallelism, which is inefficient for heterogeneous workloads with mixed resolutions and deadlines, leading to poor GPU utilization and low SLO attainment. In this paper, we propose step-level sequence parallelism to dynamically adjust
The increasing complexity and adoption of Diffusion Transformer models for image generation necessitate more efficient serving architectures to meet growing demand and overcome computational bottlenecks.
Efficiently serving AI models, particularly complex ones like DiT, directly impacts the scalability, cost-effectiveness, and real-world applicability of AI technologies across various industries.
The proposed 'step-level sequence parallelism' and dynamic adjustment of serving could significantly improve GPU utilization and service quality for mixed DiT workloads, enabling broader deployment.
- · Cloud AI providers
- · AI model developers
- · GPU manufacturers
- · Companies using generative AI
- · Inefficient AI serving systems
- · Enterprises with static AI infrastructure
Improved performance and reduced cost for generative AI image models.
Accelerated development and deployment of advanced generative AI applications due to more accessible and efficient infrastructure.
Enhanced competition among AI service providers as efficiency becomes a key differentiator, potentially leading to lower costs for end-users.
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Read at arXiv cs.LG