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Hardware Acceleration

It is recommended to update your configuration to enable hardware accelerated decoding in ffmpeg. Depending on your system, these parameters may not be compatible. More information on hardware accelerated decoding for ffmpeg can be found here:

Raspberry Pi 3/4#


There is currently a bug in ffmpeg that causes hwaccel to not work for the RPi kernel 5.15.61 and above. For more information see

Ensure you increase the allocated RAM for your GPU to at least 128 (raspi-config > Performance Options > GPU Memory). NOTICE: If you are using the addon, you may need to turn off Protection mode for hardware acceleration.

hwaccel_args: -c:v h264_v4l2m2m

Intel-based CPUs (<10th Generation) via Quicksync#

hwaccel_args: -hwaccel vaapi -hwaccel_device /dev/dri/renderD128 -hwaccel_output_format yuv420p

NOTICE: With some of the processors, like the J4125, the default driver iHD doesn't seem to work correctly for hardware acceleration. 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 frigate.yml for HA OS users.

Intel-based CPUs (>=10th Generation) via Quicksync#

hwaccel_args: -c:v h264_qsv

AMD/ATI GPUs (Radeon HD 2000 and newer GPUs) via libva-mesa-driver#

Note: You also need to set LIBVA_DRIVER_NAME=radeonsi as an environment variable on the container.

hwaccel_args: -hwaccel vaapi -hwaccel_device /dev/dri/renderD128 -hwaccel_output_format yuv420p


Supported Nvidia GPUs for Decoding

These instructions are based on the jellyfin documentation

Add --gpus all to your docker run command or update your compose file. If you have multiple Nvidia graphic card, you can add them with their ids obtained via nvidia-smi command

image: blakeblackshear/frigate:stable
deploy: # <------------- Add this section
- driver: nvidia
device_ids: ['0'] # this is only needed when using multiple GPUs
count: 1 # number of GPUs
capabilities: [gpu]

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 cuvid in the container to get a list)

V..... h263_cuvid Nvidia CUVID H263 decoder (codec h263)
V..... h264_cuvid Nvidia CUVID H264 decoder (codec h264)
V..... hevc_cuvid Nvidia CUVID HEVC decoder (codec hevc)
V..... mjpeg_cuvid Nvidia CUVID MJPEG decoder (codec mjpeg)
V..... mpeg1_cuvid Nvidia CUVID MPEG1VIDEO decoder (codec mpeg1video)
V..... mpeg2_cuvid Nvidia CUVID MPEG2VIDEO decoder (codec mpeg2video)
V..... mpeg4_cuvid Nvidia CUVID MPEG4 decoder (codec mpeg4)
V..... vc1_cuvid Nvidia CUVID VC1 decoder (codec vc1)
V..... vp8_cuvid Nvidia CUVID VP8 decoder (codec vp8)
V..... vp9_cuvid Nvidia CUVID VP9 decoder (codec vp9)

For example, for H264 video, you'll select h264_cuvid.

hwaccel_args: -c:v h264_cuvid

If everything is working correctly, you should see a significant improvement in performance. Verify that hardware decoding is working by running docker exec -it frigate nvidia-smi, which should show the ffmpeg processes:

| NVIDIA-SMI 455.38 Driver Version: 455.38 CUDA Version: 11.1 |
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
| 0 GeForce GTX 166... Off | 00000000:03:00.0 Off | N/A |
| 38% 41C P2 36W / 125W | 2082MiB / 5942MiB | 5% Default |
| | | N/A |
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
| 0 N/A N/A 12737 C ffmpeg 249MiB |
| 0 N/A N/A 12751 C ffmpeg 249MiB |
| 0 N/A N/A 12772 C ffmpeg 249MiB |
| 0 N/A N/A 12775 C ffmpeg 249MiB |
| 0 N/A N/A 12800 C ffmpeg 249MiB |
| 0 N/A N/A 12811 C ffmpeg 417MiB |
| 0 N/A N/A 12827 C ffmpeg 417MiB |