Some types of hardware acceleration are detected and used automatically, but you may need to update your configuration to enable hardware accelerated decoding in ffmpeg. To verify that hardware acceleration is working:
- Check the logs: A message will either say that hardware acceleration was automatically detected, or there will be a warning that no hardware acceleration was automatically detected
- If hardware acceleration is specified in the config, verification can be done by ensuring the logs are free from errors. There is no CPU fallback for hardware acceleration.
- [RKNN](#rockchip-platform): Frigate can utilize the media engine in RockChip SOCs to accelerate video decoding.
**Other Hardware**
Depending on your system, these presets may not be compatible, and you may need to use manual hwaccel args to take advantage of your hardware. More information on hardware accelerated decoding for ffmpeg can be found here: https://trac.ffmpeg.org/wiki/HWAccelIntro
The default driver is `iHD`. You may need to change the driver to `i965` by adding the following environment variable `LIBVA_DRIVER_NAME=i965` to your docker-compose file or [in the `config.yml` for HA App users](advanced.md#environment_vars).
Navigate to <NavPathpath="Settings > Global configuration > FFmpeg"/> and set **Hardware acceleration arguments** to `VAAPI (Intel/AMD GPU)`. For per-camera overrides, navigate to <NavPathpath="Settings > Camera configuration > FFmpeg"/>.
Navigate to <NavPathpath="Settings > Global configuration > FFmpeg"/> and set **Hardware acceleration arguments** to `Intel QuickSync (H.264)`. For per-camera overrides, navigate to <NavPathpath="Settings > Camera configuration > FFmpeg"/>.
Navigate to <NavPathpath="Settings > Global configuration > FFmpeg"/> and set **Hardware acceleration arguments** to `Intel QuickSync (H.265)`. For per-camera overrides, navigate to <NavPathpath="Settings > Camera configuration > FFmpeg"/>.
- Linux kernel **5.19 or newer** for the `i915` driver, or any release of the `xe` driver.
- Frigate running with permission to read other processes' fdinfo. Running as root inside the container (the default) satisfies this; non-root setups may need `CAP_SYS_PTRACE`.
If the host has more than one Intel GPU (e.g. an iGPU plus a discrete GPU, or SR-IOV virtual functions), pin stats collection to a specific device by setting `intel_gpu_device` to either its PCI bus address or a DRM card/render-node path:
You need to change the driver to `radeonsi` by adding the following environment variable `LIBVA_DRIVER_NAME=radeonsi` to your docker-compose file or [in the `config.yml` for HA App users](advanced.md#environment_vars).
Navigate to <NavPathpath="Settings > Global configuration > FFmpeg"/> and set **Hardware acceleration arguments** to `VAAPI (Intel/AMD GPU)`. For per-camera overrides, navigate to <NavPathpath="Settings > Camera configuration > FFmpeg"/>.
While older GPUs may work, it is recommended to use modern, supported GPUs. NVIDIA provides a [matrix of supported GPUs and features](https://developer.nvidia.com/video-encode-and-decode-gpu-support-matrix-new). If your card is on the list and supports CUVID/NVDEC, it will most likely work with Frigate for decoding. However, you must also use [a driver version that will work with FFmpeg](https://github.com/FFmpeg/nv-codec-headers/blob/master/README). Older driver versions may be missing symbols and fail to work, and older cards are not supported by newer driver versions. The only way around this is to [provide your own FFmpeg](/configuration/advanced#custom-ffmpeg-build) that will work with your driver version, but this is unsupported and may not work well if at all.
A more complete list of cards and their compatible drivers is available in the [driver release readme](https://download.nvidia.com/XFree86/Linux-x86_64/525.85.05/README/supportedchips.html).
Additional configuration is needed for the Docker container to be able to access the NVIDIA GPU. The supported method for this is to install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker) and specify the GPU to Docker. How you do this depends on how Docker is being run:
Using `preset-nvidia` ffmpeg will automatically select the necessary profile for the incoming video, and will log an error if the profile is not supported by your GPU.
Navigate to <NavPathpath="Settings > Global configuration > FFmpeg"/> and set **Hardware acceleration arguments** to `NVIDIA GPU`. For per-camera overrides, navigate to <NavPathpath="Settings > Camera configuration > FFmpeg"/>.
`nvidia-smi` will not show `ffmpeg` processes when run inside the container [due to docker limitations](https://github.com/NVIDIA/nvidia-docker/issues/179#issuecomment-645579458).
If you do not see these processes, check the `docker logs` for the container and look for decoding errors.
These instructions were originally based on the [Jellyfin documentation](https://jellyfin.org/docs/general/administration/hardware-acceleration.html#nvidia-hardware-acceleration-on-docker-linux).
Navigate to <NavPathpath="Settings > Global configuration > FFmpeg"/> and set **Hardware acceleration arguments** to `Raspberry Pi (H.264)` (for H.264 streams) or `Raspberry Pi (H.265)` (for H.265/HEVC streams). For per-camera overrides, navigate to <NavPathpath="Settings > Camera configuration > FFmpeg"/>.
A separate set of docker images is available for Jetson devices. They come with an `ffmpeg` build with codecs that use the Jetson's dedicated media engine. If your Jetson host is running Jetpack 6.0+ use the `stable-tensorrt-jp6` tagged image. Note that the Orin Nano has no video encoder, so frigate will use software encoding on this platform, but the image will still allow hardware decoding and tensorrt object detection.
The `runtime:` tag is not supported on older versions of docker-compose. If you run into this, you can instead use the nvidia runtime system-wide by adding `"default-runtime": "nvidia"` to `/etc/docker/daemon.json`:
```
{
"runtimes": {
"nvidia": {
"path": "nvidia-container-runtime",
"runtimeArgs": []
}
},
"default-runtime": "nvidia"
}
```
:::
### Setup Decoder
The decoder you need to pass in the `hwaccel_args` will depend on the input video.
A list of supported codecs (you can use `ffmpeg -decoders | grep nvmpi` in the container to get the ones your card supports)
Navigate to <NavPathpath="Settings > Global configuration > FFmpeg"/> and set **Hardware acceleration arguments** to `NVIDIA Jetson (H.264)` (or `NVIDIA Jetson (H.265)` for HEVC streams). For per-camera overrides, navigate to <NavPathpath="Settings > Camera configuration > FFmpeg"/>.
Hardware accelerated video de-/encoding is supported on all Rockchip SoCs using [Nyanmisaka's FFmpeg 6.1 Fork](https://github.com/nyanmisaka/ffmpeg-rockchip) based on [Rockchip's mpp library](https://github.com/rockchip-linux/mpp).
Set the FFmpeg hwaccel preset to enable hardware video processing.
<ConfigTabs>
<TabItemvalue="ui">
Navigate to <NavPathpath="Settings > Global configuration > FFmpeg"/> and set **Hardware acceleration arguments** to `Rockchip RKMPP`. For per-camera overrides, navigate to <NavPathpath="Settings > Camera configuration > FFmpeg"/>.
Make sure that your SoC supports hardware acceleration for your input stream. For example, if your camera streams with h265 encoding and a 4k resolution, your SoC must be able to de- and encode h265 with a 4k resolution or higher. If you are unsure whether your SoC meets the requirements, take a look at the datasheet.
If one or more of your cameras are not properly processed and this error is shown in the logs:
```
[segment @ 0xaaaaff694790] Timestamps are unset in a packet for stream 0. This is deprecated and will stop working in the future. Fix your code to set the timestamps properly
[Parsed_scale_rkrga_0 @ 0xaaaaff819070] No hw context provided on input
[Parsed_scale_rkrga_0 @ 0xaaaaff819070] Failed to configure output pad on Parsed_scale_rkrga_0
Error initializing filters!
Error marking filters as finished
[out#1/rawvideo @ 0xaaaaff3d8730] Nothing was written into output file, because at least one of its streams received no packets.
Restarting ffmpeg...
```
you should try to uprade to FFmpeg 7. This can be done using this config option:
You can set this option globally to use FFmpeg 7 for all cameras or on camera level to use it only for specific cameras. Do not confuse this option with:
Set the FFmpeg hwaccel args to enable hardware video processing.
<ConfigTabs>
<TabItemvalue="ui">
Navigate to <NavPathpath="Settings > Global configuration > FFmpeg"/> and configure the hardware acceleration args and input args manually for Synaptics hardware. For per-camera overrides, navigate to <NavPathpath="Settings > Camera configuration > FFmpeg"/>.
Make sure that your SoC supports hardware acceleration for your input stream and your input stream is h264 encoding. For example, if your camera streams with h264 encoding, your SoC must be able to de- and encode with it. If you are unsure whether your SoC meets the requirements, take a look at the datasheet.
