Minimum GPU Benchmark for Video Editing on GPU Droplets

The NVIDIA H100 offers extensive CUDA, Tensor, and RT cores, providing the necessary resources for high-resolution video editing workloads.

The H100 can handle even the most intensive editing tasks with minimal lag and high-speed processing.

1.Create a New Project - You will need to create a new project from the cloud control panel and tie it to a GPU Droplet.

2.Create a GPU Droplet - Log into your DigitalOcean account, create a new GPU Droplet, and choose AI/ML Ready as the OS. This OS image installs all the necessary NVIDIA GPU Drivers. You can refer to our official documentation on how to create a GPU Droplet.

3.Add an SSH Key for authentication - An SSH key is required to authenticate with the GPU Droplet and by adding the SSH key, you can login to the GPU Droplet from your terminal.

4.Finalize and Create the GPU Droplet - Once all of the above steps are completed, finalize and create a new GPU Droplet.

Once the GPU Droplet is ready and deployed. You can SSH to the GPU Droplet from your terminal.

ssh root@<your-droplet-ip>

Ensure your Ubuntu-based GPU Droplet is up to date:

sudo apt update && sudo apt upgrade -y

Next, please reboot the GPU Droplet using the below command and wait for it to come online. Rebooting after running sudo apt update && sudo apt upgrade—y is often necessary to ensure that any updated system components, especially the kernel and hardware drivers, are fully loaded and applied.

[ -f /var/run/reboot-required ] && echo "Reboot is required"

Output
Reboot is required

Now,let’s verify the NVIDIA driver and CUDA version with the following:

nvidia-smi

This command should display the details of your NVIDIA GPU and the driver version.

Output
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.183.06             Driver Version: 535.183.06   CUDA Version: 12.2     |
|-----------------------------------------+----------------------+----------------------+
| 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  NVIDIA H100 80GB HBM3          Off | 00000000:00:09.0 Off |                    0 |
| N/A   28C    P0              67W / 700W |      0MiB / 81559MiB |      0%      Default |
|                                         |                      |             Disabled |
+-----------------------------------------+----------------------+----------------------+
                                                                                         
+---------------------------------------------------------------------------------------+
| Processes:                                                                            |
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
|        ID   ID                                                             Usage      |
|=======================================================================================|
|  No running processes found                                                           |
+---------------------------------------------------------------------------------------+

Verify the installation of CUDA:

nvcc --version

You should see information about the installed version of CUDA.

Output
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2023 NVIDIA Corporation
Built on Mon_Apr__3_17:16:06_PDT_2023
Cuda compilation tools, release 12.1, V12.1.105
Build cuda_12.1.r12.1/compiler.32688072_0

Next, you will install FFmpeg. FFmpeg enables GPU-accelerated video processing, which you will use to scale the video.

sudo apt install -y ffmpeg

Run the following to check if FFmpeg detects CUDA support.

ffmpeg -hwaccels

This command should list cuda, vdpau, and vappi, indicating that GPU acceleration is available.

Output
ffmpeg version 4.4.2-0ubuntu0.22.04.1 Copyright (c) 2000-2021 the FFmpeg developers
  built with gcc 11 (Ubuntu 11.2.0-19ubuntu1)
  configuration: --prefix=/usr --extra-version=0ubuntu0.22.04.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libdav1d --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librabbitmq --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libsrt --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzimg --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --enable-pocketsphinx --enable-librsvg --enable-libmfx --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared
  libavutil      56. 70.100 / 56. 70.100
  libavcodec     58.134.100 / 58.134.100
  libavformat    58. 76.100 / 58. 76.100
  libavdevice    58. 13.100 / 58. 13.100
  libavfilter     7.110.100 /  7.110.100
  libswscale      5.  9.100 /  5.  9.100
  libswresample   3.  9.100 /  3.  9.100
  libpostproc    55.  9.100 / 55.  9.100
Hardware acceleration methods:
vdpau
cuda
vaapi
qsv
drm
opencl

For this tutorial, you will use a sample video from the Blender Foundation to demonstrate GPU-accelerated video processing. Blender is a free and open-source 3D creation suite that supports the entirety of the 3D pipeline—modeling, rigging, animation, simulation, rendering, etc.

Download it with the below command:

wget https://download.blender.org/demo/movies/ToS/tears_of_steel_720p.mov

You can now process the video with FFmpeg installed and the GPU configured.

The basic syntax of the ffmpeg command is:

ffmpeg -i <input_file> -vf "scale=width:height" -c:v <codec_name> -preset <encoding_preset> -b:v <bitrate> <output_file>

Here is what each of the parameters mean:

• input_file: The name of the input video file.
• scale=width:height: The scaling filter, where width and height are the desired dimensions for resizing.
• codec_name: The codec to be used for encoding the video (e.g., libx264 for H.264).
• encoding_preset: The speed vs. compression efficiency preset for encoding (e.g., fast, medium, slow).
• bitrate: The target video bitrate (e.g., 10M for 10 Mbps).
• output_file: The name of the output file, including format and extension.

In this example, you will upscale the Video from 720p to 4K resolution.

Run the following FFmpeg command to upscale the video to 4K (3840x2160 resolution):

ffmpeg -i tears_of_steel_720p.mov -vf "scale=3840:2160" -c:v libx264 -preset fast -b:v 10M tears_of_steel_4k.mov

Once the processing finishes, you should observe the following output:

Output
Output #0, mov, to 'tears_of_steel_4k.mov':
  Metadata:
    major_brand     : qt  
    minor_version   : 512
    compatible_brands: qt  
    encoder         : Lavf58.76.100
  Stream #0:0(eng): Video: h264 (avc1 / 0x31637661), yuv420p(tv, progressive), 3840x2160 [SAR 120:89 DAR 640:267], q=2-31, 10000 kb/s, 24 fps, 12288 tbn (default)
    Metadata:
      handler_name    : VideoHandler
      vendor_id       : FFMP
      encoder         : Lavc58.134.100 libx264
    Side data:
      cpb: bitrate max/min/avg: 0/0/10000000 buffer size: 0 vbv_delay: N/A
  Stream #0:1(eng): Audio: aac (LC) (.mov / 0x6134706D), 44100 Hz, stereo, fltp, 128 kb/s (default)
    Metadata:
      handler_name    : SoundHandler
      vendor_id       : [0][0][0][0]
      encoder         : Lavc58.134.100 aac
frame=17620 fps= 69 q=-1.0 Lsize=  949201kB time=00:12:14.07 bitrate=10592.7kbits/s speed=2.88x    
video:937125kB audio:11532kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.057354%
[libx264 @ 0x564504ee7f40] frame I:223   Avg QP:22.07  size:180259
[libx264 @ 0x564504ee7f40] frame P:5973  Avg QP:25.97  size: 92140
[libx264 @ 0x564504ee7f40] frame B:11424 Avg QP:27.21  size: 32306
[libx264 @ 0x564504ee7f40] consecutive B-frames:  8.2% 14.4%  5.4% 72.0%
[libx264 @ 0x564504ee7f40] mb I  I16..4: 20.9% 75.8%  3.2%
[libx264 @ 0x564504ee7f40] mb P  I16..4:  9.4% 21.2%  1.1%  P16..4: 29.5%  4.3%  1.4%  0.0%  0.0%    skip:33.0%
[libx264 @ 0x564504ee7f40] mb B  I16..4:  0.9%  1.4%  0.1%  B16..8: 25.0%  1.3%  0.2%  direct: 1.8%  skip:69.3%  L0:44.6% L1:53.4% BI: 2.0%
[libx264 @ 0x564504ee7f40] final ratefactor: 25.73
[libx264 @ 0x564504ee7f40] 8x8 transform intra:66.4% inter:91.8%
[libx264 @ 0x564504ee7f40] coded y,uvDC,uvAC intra: 30.5% 41.4% 7.3% inter: 6.3% 10.8% 0.3%
[libx264 @ 0x564504ee7f40] i16 v,h,dc,p: 31% 23%  7% 38%
[libx264 @ 0x564504ee7f40] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 34% 16% 23%  3%  5%  6%  4%  5%  3%
[libx264 @ 0x564504ee7f40] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 37% 17% 10%  4%  8%  9%  6%  6%  4%
[libx264 @ 0x564504ee7f40] i8c dc,h,v,p: 55% 16% 22%  6%
[libx264 @ 0x564504ee7f40] Weighted P-Frames: Y:2.2% UV:1.1%
[libx264 @ 0x564504ee7f40] ref P L0: 62.5% 12.6% 18.5%  6.3%  0.1%
[libx264 @ 0x564504ee7f40] ref B L0: 90.1%  8.2%  1.7%
[libx264 @ 0x564504ee7f40] ref B L1: 96.5%  3.5%
[libx264 @ 0x564504ee7f40] kb/s:10456.65
[aac @ 0x564504f67ec0] Qavg: 259.313

Here is the command’s breakdown:

• -i tears_of_steel_720p.mov: Specifies the input video file.
• -vf "scale=3840:2160": Sets the scale filter to upscale the video to 4K resolution (3840x2160).
• -c:v libx264: Uses the libx264 codec to encode the video.
• -preset medium: Specifies the encoding speed/quality balance (fast is a good balance).
• -b:v 10M: Sets the target video bitrate to 10 Mbps to maintain quality.

To learn more about the FFmpeg command, you can refer to its official documentation.

Once the video is processed, download it from the droplet to your local machine using scp. Replace <your_droplet_ip> with your droplet’s IP address.

scp root@<your_droplet_ip>:~/tears_of_steel_4k.mov ~/Downloads/

This command copies the 4K scaled video file to your Downloads folder on your local desktop.