Post your video card FPS here!

[E3V3A]

It's interesting to see what kind of frame rate (FPS = Frames Per Second) we can expect from our video cards. So if your card is not already listed here, or if your software versions or OS is very different from what's already here, please post a comment with relevant table info, and I'll add it here.

Expected Video Card FPS table

Video Card Name	VRAM*	FPS	OS	Py	PyTorch	Driver**
Nvidia GeForce RTX 3070	8 GB	33	Win10 Pro	3.8.5	1.7.1
Nvidia GeForce RTX 2080		21
Nvidia GeForce RTX 2070	8 GB	23	Win10	3.7.6	1.0.0	441.22
Nvidia GeForce RTX 2060	8 GB	19	Win10	3.7.5	1.0.0
Nvidia GeForce RTX 2060	6 GB	15	Win10	3.7.5	1.0.0
Nvidia GeForce GTX 1650	4 GB	11	Win10	3.7.x	1.0.0
Nvidia GeForce GTX 1650M	4 GB	18	Arch Linux	3.7.7	1.0.0
Nvidia GeForce GTX 1650 Ti	8 GB	21	Linux Mint 20	3.7.9	1.7.1	450.102
Nvidia GeForce GTX 1660 SUPER	6 GB	25	Ubuntu 20.04	3.7.9	1.0.0
Nvidia GeForce GTX 1070	7 GB	15
Nvidia GeForce GTX 1070	8 GB	28	Manjaro 20.0.1	3.7.7	1.0.0
Nvidia GeForce GTX 1060	6 GB
Nvidia GeForce GTX 1050ti	4 GB	7	Win10	3.7.x	1.0.0
Nvidia GeForce GTX 950	2 GB	9	Win10	3.7.7	1.0.0
Nvidia GeForce GTX 860M	4 GB	5+	Ubuntu 20.04	3.7.7	1.0.0
Nvidia GeForce GTX 850M	4 GB	3+	Win8.1	3.8	1.5.0
Nvidia GeForce GTX 850M	4 GB	5+	Win8.1	3.7.7	1.0.0
Nvidia GeForce GTX TITAN	4 GB	7+	Win10	3.7.7	1.0.0
Nvidia Jetson TX2		25

*VRAM is you cards video RAM in GB.
**Driver is your nVidia Driver Version

To get Video card info:

# For Windows Powershell, use: 
(Get-CimInstance -ClassName CIM_VideoController) |Select-Object Name, @{Name="VRAM"; Expression={[math]::round($_.AdapterRAM/1GB, 0)}},DeviceID,AdapterDACType

# For Windows CMD, use:
wmic path win32_VideoController get name

# For Windows/*nix with Nvidia tools in the Path, use: 
nvidia-smi.exe -q
nvidia-smi.exe --format=csv --query-gpu=name,"memory.total",pstate,count,driver_version

# For Linux (general), use:
lspci -v
sudo lshw -numeric -C display
# This require the "mesa-utils" package, but may not be available on headless cloud installs.
glxinfo

You can also search this website for your exact video card, although it may vary sometimes with HW revisions, not easily found.

To get your active Python and PyTorch version, make sure to activate the conda environment as the python version may be different, in different environments.

conda activate avatarify
python -V
pip show torch

[jgomez696]

Video Card Name (nvidia geforce rtx 2060 )
VRAM (8 GB )
FPS (19)
OS (Win10 home)
Python (3.7.5 )
PyTorch (1.0.0 )

[alievk]

@E3V3A it's impossible for 1060 to run 40+ FPS, that's most probably was "40ms" model inference time. I also doubt 1070 is able to perform at 30 FPS, I have 15 on mine.

[E3V3A]

@alievk

it's impossible

Good to know. I got all those numbers from random posts in the slack channels...

[MBadberg]

Video Card Name (nvidia geforce rtx 2060)
VRAM (6 GB )
FPS (15,8)
OS (Win10 Pro)
Python (3.7.5 )
PyTorch (1.0.0 )

[EinderJam]

Video Card Name (nvidia geforce gtx 1070)
VRAM (8 GB)
FPS/Model/Pre/Post (28/27.5/1.5/5.0) (seems too high for a 1070)
OS (Manjaro 20.0.1)
Python (3.7.7)
PyTorch (1.0.0)

[YosAwed]

Video Card Name (NVIDIA GeForce GTX TITAN)
VRAM 4GB
FPS 7.8
OS Windows10
Python 3.7.7
Pytorch 1.0.0

[sirdrakeistda]

Video Card Name: Nvidua geforce GTX 860M
FPS 5.8
OS Ubuntu Budgie 20.04
Python 3.7.7
Pytorch 1.0.0

[clippycoder]

Video Card Name: None (Processor: i7 5557U)
FPS: 0.3
OS: Macos Catalina 10.15.4
Python 3.7.7
PyTorch 1.5.0

[clippycoder]

Video Card Name: Nvidia GTX 950
VRAM: 2GB
FPS: 9.1
OS: Windows 10
Python 3.7.7
PyTorch 1.0.0

[clippycoder]

I'm not sure how much of a difference the video card makes. You can see just in these comments that it varies wildly even amongst similar cards, and while my GTX 950 got ~9 FPS, I noticed it was only under 20% load. Something doesn't seem to be optimized.

[E3V3A]

@clippycoder

my GTX 950 got ~9 FPS

Actually that sound even too much. For a videocard with only 2GB, that is awesome!
Can you please check again and post the nvidia-smi -q output.

Don't worry about the load. Win10 does super shit confusing job in showing GPU usage in the taskmanager. I believe the 20% you see is an artifact of task manager averaging over a number of CUDA cores or processing units. So to see true usage you need to run the following command:

nvidia-smi --query-gpu=pci.bus_id,pstate,temperature.gpu,utilization.gpu,utilization.memory,memory.free,memory.used --format=csv -l 5

This should keep updating while you're running.

[clippycoder]

@E3V3A
I'm quite sure it's a GTX 950, having purchased it 'bout a week ago. But, I double checked for you, and yes, it's still a GTX 950, still 2GB, with about 9 FPS. I'd say I'm pretty satisfied. I wouldn't call the output smooth by any measure, but still acceptable for videoconferencing. And thanks for the tip about GPU usage, from the command prompt, it actually appears to be about 70-80%; much more realistic. Task manager reads 15%.

[sovanyio]

Video Card Name: GeForce GTX 1650 Mobile
VRAM: 4GB
FPS: 18
OS: Arch Linux
Python 3.7.7
PyTorch 1.0.0

[QingpingMeng]

Video Card Name: Geforce GTX 2070 (Remote GPU in my local network)
VRAM: 8GB
FPS 19 without CUDA, 23 with CUDA
OS: Win10
Python: 3.7.6
PyTorch: 1.0.0

[alievk]

Video Card Name: Geforce GTX 2070 (Remote GPU in my local network)
VRAM: 8GB
FPS 19 without CUDA, 23 with CUDA
OS: Win10
Python: 3.7.6
PyTorch: 1.0.0

Did CUDA installator update the driver? Installing CUDA alone in the system is not supposed to affect the performance..

@E3V3A I think mentioning NVIDIA driver could be informative in these reports.