Using more than 2 NV_ENC at a time with FFMPEG

Question

I'm currently generating timelapse videos using a thread on my CPU with fluent-ffmpeg running on nodejs. It takes roughly 1 minute to generate a 10 second timelapse. I'm generating many at the same time (basically one per thread) such that I tend to get the best performance at 8 worker threads. ... overall system throughput is about one video per 12 seconds.

GPU processing using h264_nvenc takes the single-thread time to about 3-4 seconds. Yippie. I went out and bought some nVidia 1660's to take advantage.

Unfortunately, when I go to generate the 3rd simultaneous video, I get "Conversion Failed." error from FFMPEG.

Some basic research seems to show you can only 2 at a time. Perhaps 3 with updated drivers.

Is there a method around this? Posts from here indicates this limit is artificial and can be worked around: https://www.techpowerup.com/268495/nvidia-silently-increases-geforce-nvenc-concurrent-sessions-limit-to-3

Perhaps a way to use all the cuda/tensor/etc cores to render timelapse videos instead of just relying on the limited nv_enc?

Answer 1

Current limit of 3 renders on both my GTX 1060 and my RTX 2080 Ti. Other post says GTX 1660 is same. So this is obviously an artificial limit. Looking at the nVidia link posted above, which has a list of cards and their NVENC/NVDEC capabilities, it looks like most nVidia gaming cards themselves have this 3-render limit. However, most of the modern (Pascal and up) Quadro cards allow unlimited renders per card. As another workaround, you can put multiple gaming cards in a system. FFmpeg has a function to send a particular job to the card of your chosing. The same encoder module is in the GTX1660 as is in the RTX 2080 Ti, so there shouldn't be much speed difference between low-end and high-end cards. Maybe some minor difference from the memory bus width, but I haven't compared 1660/2080Ti directly to each other. What I'm saying is: if you need more encoding horsepower, just buy another couple low-end cards and divide up the workload using FFmpeg's builtin functionality.