For a full list of GPUs and formats supported, please see the available GPU Support Matrix. The tqdm is also recommended to be installed. However, we recommend users to use the following script to get the dependencies quickly: python webtools.py This script requires users to install urllib3. Users could download dependencies manually by checking the release page. Ability to add your own custom high-performance CUDA filters using the shared CUDA context implementation in FFmpeg Download the FFMpeg dependencies, including include and lib.Create high-performance end-to-end hardware-accelerated video processing, 1:N encoding and 1:N transcoding pipeline using built-in filters in FFmpeg.Granular control over encoding settings such as encoding preset, rate control and other video quality parameters.
Hardware-accelerated decoding of H.264, HEVC, VP9, VP8, MPEG2, MPEG4*, and AV1.Hardware-accelerated encoding of H.264 and HEVC*.
If you have an NVIDIA GPU which supports hardware-accelerated video encoding and decoding, it’s simply a matter of compiling FFmpeg binary with the required support for NVIDIA libraries and using the resulting binaries to speed up video encoding/decoding.įFmpeg supports following functionality accelerated by video hardware on NVIDIA GPUs:
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