A kernel-centric path to real-time video generation on Trainium

A kernel-centric path to real-time video generation on Trainium - Amazon Science

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The AWS Neuron Science team and Reactor collaborated to bring real-time video generation to Trainium.

Machine learning

A kernel-centric path to real-time video generation on Trainium

Using the Neuron Kernel Interface, a Reactor–AWS collaboration tackled the dynamic shapes, memory access patterns, and cache management that make real-time autoregressive diffusion hard—building techniques that generalize across models.

By Stephen Zorio

September 25, 2026

12 min read

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In 2018, Jürgen Schmidhuber — who in 1990 was the first person to propose world models as a machine learning concept — published a paper with David Ha. They wrote about “a predictive world model” which could “extract useful representations of space and time” and use that to train agents to drive, among other things. The growth in the capability and real-world applications of world models in the eight years since that seminal paper is staggering. The emergence of massive training datasets, paired with variational autoencoders, and both diffusion and autoregressive transformers enable today’s world models to convert text, images, audio, and video inputs into latent space that is used to build and maintain breathtaking, immersive world models which can predict state changes and respond to actions. These models have vital and expanding roles in robotics, transport, climate modeling, video game development, scientific simulation, and design prototyping.

World models can generate immersive, explorable environments that respond to user input in real time.

The sharp increase in the power and potential of world models has been accompanied by an increased need for hardware and infrastructure capable of supporting them. That is a challenge that Bryce Schmidtchen , co-founder and chief technology officer of Reactor, is well acquainted with. “It's about real time, it's about low latency, and it's about doing that as efficiently as you can at scale,” Schmidtchen observed. Reactor is a platform that allows developers, designers, and researchers to deploy, use, and scale real-time interactive AI models. “Efficiency means everything from how you schedule the inference on the given chip, in our case Trainium , to how you think about maximally bin packing every forward pass of the model.” The challenge of efficiency is made even more acute by Reactor’s global presence. “We think about GPU clusters in terms of regions — we have hundreds all over the world. The latent that turns into a pixel that comes off the GPU needs to be able to get to the network without hopping around through Kubernetes. And you need to do this in a way where it doesn't matter if it's a well-supported cluster or bare metal in a closet. “And then,” Schmidtchen continued, “you need to tie this to world-class networking and media streaming that supports different codecs, different resolutions, and allows that to connect to APIs and SDKs across different languages that can support all different types of applications.”

Reactor's platform provides APIs...