arXiv:2606.13501v2 Announce Type: replace-cross Abstract: Diffusion Transformers (DiTs) have become the dominant architecture for image and video generation, creating growing demand for efficient DiT serving. Existing systems assign each request a fixed parallel configuration throughout its lifetime. However, DiT workloads exhibit substantial heterogeneity across requests, execution stages, and system conditions, making static parallelism inefficient and often leading to poor GPU utilization and degraded service quality. This paper argues that DiT serving should treat GPU parallelism as a firs
The rapid adoption of Diffusion Transformers (DiTs) for image and video generation is creating urgent demand for more efficient serving infrastructure.
Optimizing DiT serving directly impacts the economic viability and scalability of advanced AI generation models, affecting sectors reliant on visual AI.
The shift from static to dynamic GPU parallelism for DiT serving will lead to significant improvements in efficiency, reducing operational costs and increasing throughput.
- · Cloud providers
- · AI model developers
- · Generative AI startups
- · GPU manufacturers
- · Companies with inefficient inference infrastructure
- · Legacy AI serving solutions
Reduced cost and increased accessibility of high-quality image and video generation.
Accelerated development and deployment of new visual AI applications across industries.
Potentially exacerbates the demand for more powerful and specialized GPUs, impacting compute supply chains.
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Read at arXiv cs.LG