NVENC patch removes restriction on maximum number of simultaneous NVENC video encoding sessions imposed by Nvidia to consumer-grade GPUs.

NvFBC patch allows to use NvFBC on consumer-grade GPUs. It should be applied same way as NVENC patch.sh, except you have to use patch-fbc.sh instead.

Main target operating system is GNU/Linux, but for Windows support see win (clickable).

❤️ ❤️ ❤️

You can say thanks to the author and support ongoing efforts with donations to these wallets:

• BTC: 1Q9uQAFNviHZEW7yT5sNsi4MJnxaL2tvvK
• ETH: 0xE55e48b116D0dd4d26adafB65B92e74F0ac73636

IPFS site mirror: https://ipfs.io/ipns/QmYTEwv2GjQhtdN9bDfpLfQrVD7YLb1Sbh8igX8cEe9hHF/

IPFS git mirror:

git clone https://ipfs.io/ipns/Qmed4r8yrBP162WK1ybd1DJWhLUi4t6mGuBoB9fLtjxR7u nvidia-patch

• x86_64 system architecture
• GNU/Linux operating system
• nvenc-compatible gpu (https://developer.nvidia.com/video-encode-decode-gpu-support-matrix#Encoder)
• Nvidia driver. Patch available for versions in version table below.

# bash ./patch.sh -h

SYNOPSIS
       patch-fbc.sh [-s] [-r|-h|-c VERSION|-l]

DESCRIPTION
       The patch for Nvidia drivers to allow FBC on consumer devices

       -s             Silent mode (No output)
       -r             Rollback to original (Restore lib from backup)
       -h             Print this help message
       -c VERSION     Check if version VERSION supported by this patch.
                      Returns true exit code (0) if version is supported.
       -l             List supported driver versions
       -d VERSION     Use VERSION driver version when looking for libraries
                      instead of using nvidia-smi to detect it.

Examples are provided for driver version 430.50. All commands are executed as root.

Skip this step if you already have installed driver with version supported by this patch (from distro packages, for example).

Make sure you have kernel headers and compiler installed before running Nvidia driver installer. Kernel headers and compiler are required to build nvidia kernel module. Recommended way to do this is to install dkms package, if it is available in your distro. This way dkms package will pull all required dependencies to allow building kernel modules and kernel module builds will be automated in a reliable fashion.

mkdir /opt/nvidia && cd /opt/nvidia
wget https://international.download.nvidia.com/XFree86/Linux-x86_64/430.50/NVIDIA-Linux-x86_64-430.50.run
chmod +x ./NVIDIA-Linux-x86_64-430.50.run
./NVIDIA-Linux-x86_64-430.50.run

nvidia-smi

Output should show no errors and details about your driver and GPU.

This patch performs backup of original file prior to making changes.

bash ./patch.sh

You're all set!

If something got broken you may restore patched driver from backup:

bash ./patch.sh -r

It is possible to use this patch with nvidia-docker containers, even if host machine hasn't patched drivers. See Dockerfile for example.

Essentially all you need to do during build is:

• COPY the patch.sh and docker-entrypoint.sh files into your container.
• Make sure docker-entrypoint.sh is invoked on container start.

docker-entrypoint.sh script does on-the-fly patching by means of manipulating dynamic linker to workaround read-only mount of Nvidia runtime. Finally it passes original docker command to shell, like if entrypoint was not restricted by ENTRYPOINT directive. So docker run --runtime=nvidia -it mycontainer echo 123 will print 123. Also it can be just invoked from your entrypoint script, if you have any.

• Plex Media Server: nVidia Hardware Transcoding Calculator for Plex Estimates - useful benchmark of achieved simultaneous transcodes with various stream quality and hardware with patched drivers.

• Plex Media Server: enable HW decoding:
  • GH Issue
  • PMS Forum:
    1. https://forums.plex.tv/t/hardware-accelerated-decode-nvidia-for-linux/233510/158
    2. https://forums.plex.tv/t/hardware-accelerated-decode-nvidia-for-linux/233510/172
• Unraid / Docker:
  • GH Issue
  • Unraid Nvidia plugin:
    • Repo
    • Forum page (archive link)
• Original research behind this patch
  • Original post in Russian
  • 3rd party English translation

If you experience CreateBitstreamBuffer failed: out of memory (10), then you have to lower buffers number used for every encoding session. If you are using ffmpeg, see option -surfaces ("Number of concurrent surfaces") and try value near -surfaces 8.