# Hardware Transcoding

This feature allows you to use a GPU to accelerate transcoding and reduce CPU load.
Note that hardware transcoding produces significantly larger videos than software transcoding with similar settings, typically with lower quality. Using slow presets and preferring more efficient codecs can narrow this gap.
As this is a new feature, it is still experimental and may not work on all systems.

:::info
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

- NVENC (NVIDIA)
- Quick Sync (Intel)
- RKMPP (Rockchip)
- VAAPI (AMD / NVIDIA / Intel)

## Limitations

- 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.
- By default, only encoding is currently hardware accelerated. This means the CPU is still used for software decoding and tone-mapping.
  - You can benefit from end-to-end acceleration by enabling hardware decoding in the video transcoding settings.
- 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.

## Prerequisites

#### NVENC

- You must have the official NVIDIA driver installed on the server.
- On Linux (except for WSL2), you also need to have [NVIDIA Container Toolkit][nvct] installed.

#### QSV

- 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][jellyfin-lp] 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][jellyfin-kernel-bug])

#### RKMPP

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/libmali.so.1` to be present on your host system. Install the [`libmali`][libmali-rockchip] release that corresponds to your Mali GPU (`libmali-valhall-g610-g13p0-gbm` on RK3588) and modify the [`hwaccel.transcoding.yml`][hw-file] 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/libmali.so.1:/usr/lib/aarch64-linux-gnu/libmali.so.1:ro`

## Setup

#### Basic Setup

1. If you do not already have it, download the latest [`hwaccel.transcoding.yml`][hw-file] file and ensure it's in the same folder as the `docker-compose.yml`.
2. In the `docker-compose.yml` under `immich-server`, uncomment the `extends` section and change `cpu` to the appropriate backend.

   Note: For VAAPI on WSL2, be sure to use `vaapi-wsl` rather than `vaapi`

3. Redeploy the `immich-server` container with these updated settings.
4. In the Admin page under `Video transcoding settings`, change the hardware acceleration setting to the appropriate option and save.

   Note: For Jasper Lake and Elkhart Lake CPUs, you will need to set the `Hardware Acceleration` -> `Constant quality mode` to `CQP`

5. (Optional) Enable hardware decoding for optimal performance.

#### 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`][hw-file] file into the `immich-server` service directly.

For example, the `qsv` section in this file is:

```yaml
devices:
  - /dev/dri:/dev/dri
```

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

```yaml
immich-server:
  container_name: immich_server
  image: ghcr.io/immich-app/immich-server:${IMMICH_VERSION:-release}
  # Note the lack of an `extends` section
  devices:
    - /dev/dri:/dev/dri
  volumes:
  ...
```

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

#### All-In-One - Unraid Setup

##### QSV

1. Unraid > Docker > (Stop) Immich container > Edit
2. Scroll down and select `Add another Path, Port, Variable, Label or Device`
3. In the drop-down menu, select `Device` and an entry with any name and the value `/dev/dri`.
4. Continue to step 4 of "Basic Setup".

##### NVENC

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".

## Tips

- 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
- You can confirm the device is being recognized and used by checking its utilization (via `nvtop` for NVIDIA, `intel_gpu_top` for Intel, etc.) when transcoding. A lack of error logs when transcoding also indicates that it's being used.

[hw-file]: https://github.com/immich-app/immich/releases/latest/download/hwaccel.transcoding.yml
[nvct]: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html
[jellyfin-lp]: https://jellyfin.org/docs/general/post-install/transcoding/hardware-acceleration/intel#low-power-encoding
[jellyfin-kernel-bug]: https://jellyfin.org/docs/general/post-install/transcoding/hardware-acceleration/intel#known-issues-and-limitations-on-linux
[libmali-rockchip]: https://github.com/tsukumijima/libmali-rockchip/releases
