DIFFLET: A Fast Inference Engine for Diffusion Transformer Models on AWS Trainium
On AWS Trainium2, every component of a diffusion model runs as a program compiled ahead of time for fixed shapes, while pipelines differ across models, prompt lengths across requests, and caching decisions across denoising steps. We present Difflet, a serving engine in which five model pipelines share one compilation backend, joint attention is split across cores by attention head, and a host controller decides at each denoising step whether to run the denoiser or reuse its earlier output. A small probe program computes the caching signal on the device and returns one number per step, and a resident model process keeps every program loaded across requests. Across five image and video mode...
| Authors | Bin Ma, Min Yu, Vijay Kodamalla et al. |
|---|---|
| Primary category | cs.DC |
| Published | 16 October 2026 |
| Last updated at source | 20 October 2026 |
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