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To check the GPU Usage run nvidia-smi in another terminal to confirm GPU utilization.

More sources:

nvidia-smi (NVIDIA System Management Interface)

To check the GPU Usage and other informations run nvidia-smi in another terminal to confirm GPU utilization.

No one-size-fits-all solution

This issue often comes down to how your software, hardware, drivers, and system settings interact.

The mismatch between hardware acceleration and scaling depends on your specific setup, like your GPU, installed drivers, FFmpeg configuration, and even environment variables.

There’s no one-size-fits-all solution, you’ll need to experiment with different settings (e.g. switching between hardware and software decoding) to see what works for your system and your setup.

There are solutions for "Using FFmpeg with full hardware-accelerated decoding via NVDEC" and "Using software-based decode fallback", along with other relevant links.

Other relevant links from Convert ffmpeg encoding from libx264 to h264_nvenc:

There are solutions for "Using FFmpeg with full hardware-accelerated decoding via NVDEC" and "Using software-based decode fallback", along with other relevant links in this Post.

1. Using FFmpeg with Full hardware-accelerated decoding via nvdec:...

(a). Scaling being done with the scale_npp filer, available when FFmpeg is built with the proprietary CUDA SDK (when the flags --enable-nonfree --enable-cuda-nvcc --nvccflags="-gencode arch=compute_52,code=sm_52 -O2" are passed to ./configure on build time)

2. Using software-based decode fallback:

Note that GPU allocation is done via the filter hwupload_cuda=0 which initializes a CUDA HWContext bound to GPU 0 for all scaling operations, and for the h264_nvenc encoder wrapper, the private option -gpu:v 0 follows.

(a). Using the scale_npp filter:

Other relevant links from Convert ffmpeg encoding from libx264 to h264_nvenc:...