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Hardware Transcoding [Experimental]

This feature allows you to use a GPU to accelerate transcoding and reduce CPU load. Note that hardware transcoding is much less efficient for file sizes. As this is a new feature, it is still experimental and may not work on all systems.


You do not need to redo any transcoding jobs after enabling hardware acceleration. The acceleration device will be used for any jobs that run after enabling it.

Supported APIs

  • Quick Sync (Intel)
  • RKMPP (Rockchip)
  • VAAPI (AMD / NVIDIA / Intel)


  • The instructions and configurations here are specific to Docker Compose. Other container engines may require different configuration.
  • Only Linux and Windows (through WSL2) servers are supported.
  • WSL2 does not support Quick Sync.
  • Raspberry Pi is currently not supported.
  • Two-pass mode is only supported for NVENC. Other APIs will ignore this setting.
  • Only encoding is currently hardware accelerated, so the CPU is still used for software decoding and tone-mapping.
  • Hardware dependent
    • Codec support varies, but H.264 and HEVC are usually supported.
      • Notably, NVIDIA and AMD GPUs do not support VP9 encoding.
    • Newer devices tend to have higher transcoding quality.



  • You must have the official NVIDIA driver installed on the server.
  • On Linux (except for WSL2), you also need to have NVIDIA Container Runtime installed.


  • For VP9 to work:
    • You must have a 9th gen Intel CPU or newer
    • If you have an 11th gen CPU or older, then you may need to follow these instructions as Low-Power mode is required
    • Additionally, if the server specifically has an 11th gen CPU and is running kernel 5.15 (shipped with Ubuntu 22.04 LTS), then you will need to upgrade this kernel (from Jellyfin docs)


For RKMPP to work:

  • You must have a supported Rockchip ARM SoC.
  • Only RK3588 supports hardware tonemapping, other SoCs use slower software tonemapping while still using hardware encoding.
  • Tonemapping requires /usr/lib/aarch64-linux-gnu/ to be present on your host system. Install libmali-valhall-g610-g6p0-gbm and modify the hwaccel.transcoding.yml file:
    • under rkmpp uncomment the 3 lines required for OpenCL tonemapping by removing the # symbol at the beginning of each line
    • - /dev/mali0:/dev/mali0
    • - /etc/OpenCL:/etc/OpenCL:ro
    • - /usr/lib/aarch64-linux-gnu/


Basic Setup

  1. If you do not already have it, download the latest hwaccel.transcoding.yml file and ensure it's in the same folder as the docker-compose.yml.
  2. In the docker-compose.yml under immich-microservices, uncomment the extends section and change cpu to the appropriate backend.
  • For VAAPI on WSL2, be sure to use vaapi-wsl rather than vaapi
  1. Redeploy the immich-microservices container with these updated settings.
  2. In the Admin page under Video transcoding settings, change the hardware acceleration setting to the appropriate option and save.

Single Compose File

Some platforms, including Unraid and Portainer, do not support multiple Compose files as of writing. As an alternative, you can "inline" the relevant contents of the hwaccel.transcoding.yml file into the immich-microservices service directly.

For example, the qsv section in this file is:

- /dev/dri:/dev/dri

You can add this to the immich-microservices service instead of extending from hwaccel.transcoding.yml:

container_name: immich_microservices
# Note the lack of an `extends` section
- /dev/dri:/dev/dri
command: ['', 'microservices']
- ${UPLOAD_LOCATION}:/usr/src/app/upload
- /etc/localtime:/etc/localtime:ro
- .env
- redis
- database
restart: always

Once this is done, you can continue to step 3 of "Basic Setup".

All-In-One - Unraid Setup

  1. In the container app, add this environmental variable: Key=NVIDIA_VISIBLE_DEVICES Value=all
  2. While still in the container app, change the container from Basic Mode to Advanced Mode and add the following parameter to the Extra Parameters field: --runtime=nvidia
  3. Restart the container app.
  4. Continue to step 4 of "Basic Setup".


  • You may want to choose a slower preset than for software transcoding to maintain quality and efficiency
  • While you can use VAAPI with NVIDIA and Intel devices, prefer the more specific APIs since they're more optimized for their respective devices