Real-time video generation inference on Baseten

Baseten's model performance team has achieved significant improvements in the video generation speed of the Wan 2.2 model, a leading open-source text-to-video generation tool, achieving a 53.6x speed increase by reducing the video generation time from over two minutes to just 2.75 seconds per clip. This enhancement is attributed to several key optimizations, including timestep distillation that reduces the diffusion process to four steps, custom kernel engineering for faster execution, and NVFP4 quantization that improves the throughput of tensor operations. These advancements not only decrease the generation cost per video significantly but also require an efficient and scalable infrastructure to handle increased traffic demands. The team has implemented autoscaling and queuing mechanisms to ensure stable service delivery and has established content guardrails to mitigate misuse, ensuring that video generation is conducted responsibly. A public demo of this high-performance video inference setup is available through July 31, 2026, showcasing Baseten's prowess in runtime optimizations and infrastructure development.

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