Building the performance layer for world models

MoonMath.ai is building the performance layer for world models, the video-native diffusion systems that are increasingly dominating AI workloads. World model inference is orders of magnitude more expensive than text inference, driven by massive spatio-temporal token counts and repeated diffusion passes.

Our mission is to make these systems scalable, deployable, and economically viable. We focus exclusively on performance for Physical AI, ensuring that world and video models can run faster, cheaper, and at production scale.

Building technology

We develop core acceleration primitives like LiteAttention and other kernel-level innovations that eliminate redundant compute and deliver lossless speedups.

Working with AI labs

We provide end-to-end inference acceleration, optimizing models across attention, FFNs, batching, scheduling, and hardware stacks to deliver repeatable 2x+ cost-performance improvements. Talk to us.

Shipping product

With WorldJen, we provide a high-performance benchmark and evaluation platform for video and world models, turning multi-modal evals into a scalable, reliable workflow and establishing the entry point into full serving acceleration.