# Rolling Forcing

Using Rolling Forcing as a proxy for autoregressive diffusion video generation, Reactor and the Amazon Neuron Science team adopted a kernel-centric, bottom-up approach—writing hardware-level kernels via the Neuron Kernel Interface, applying hybrid sequence and tensor parallelism sharding, and rewriting model code to batch shared components of the diffusion and cache-update phases—successfully generating a correct video on the first end-to-end run on Trainium and delivering real-time generation above 16 fps.

Using Rolling Forcing as a proxy for autoregressive diffusion video generation, Reactor and the Amazon Neuron Science team adopted a kernel-centric, bottom-up approach—writing hardware-level kernels via the Neuron Kernel Interface, applying hybrid sequence and tensor parallelism sharding, and rewriting model code to batch shared components of the diffusion and cache-update phases—successfully generating a correct video on the first end-to-end run on Trainium and delivering real-time generation above 16 fps.