How to Run GPU-Accelerated VMAF on Windows Using WSL2, Docker, and NVIDIA

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Running GPU-accelerated VMAF (Video Multimethod Assessment Fusion) on Windows has no official prebuilt binary support, making the setup process difficult for most users. A developer has published a step-by-step guide using WSL2, Docker Desktop, and the NVIDIA Container Toolkit to enable the libvmaf_cuda filter on Windows machines. The workflow relies on the open-source easyVmaf project, which includes a CUDA-specific Dockerfile designed for this purpose. On an NVIDIA RTX 3060 Mobile, the setup achieves roughly 15x real-time processing speed, analyzing a 46-minute video in about three minutes compared to nearly an hour on CPU. The guide is exclusive to NVIDIA GPUs, as libvmaf_cuda does not support AMD or Intel graphics hardware.

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