Make your ZeroGPU Spaces go brrr with ahead-of-time compilation

Back to Articles Make your ZeroGPU Spaces go brrr with ahead-of-time compilation Published September 2, 2025 Update on GitHub Upvote 75 +69 Charles Bensimon cbensimon Follow Sayak Paul sayakpaul Follow Linoy Tsaban linoyts Follow Apolinário from multimodal AI art multimodalart Follow ZeroGPU lets anyone spin up powerful Nvidia H200 hardware in Hugging Face Spaces without keeping a GPU locked for idle traffic. It’s efficient, flexible, and ideal for demos but it doesn’t always make full use of everything the GPU and CUDA stack can offer. Generating images or videos can take a significant amount of time. Being able to squeeze out more performance, taking advantage of the H200 hardware, does matter in this case. This is where PyTorch ahead-of-time (AoT) compilation comes in. Instead of compiling models on the fly (which doesn’t play nicely with ZeroGPU’s short-lived processes), AoT lets you optimize once and reload instantly. The result: snappier demos and a smoother experience, with speedups ranging from 1.3×–1.8× on models like Flux, Wan, and LTX 🔥 In this post, we’ll show how to wire up Ahead-of-Time (AoT) compilation in ZeroGPU Spaces. We'll explore advanced tricks like FP8 quantization and dynamic shapes, and share working demos you can try right away. If you cannot wait, we invite you to check out some ZeroGPU-powered demos on the zerogpu-aoti organization. Pro users and Team / Enterprise org members can create ZeroGPU Spaces, while anyone can freely use them (Pro, Team...