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GPU acceleration in FFmpeg enables faster video processing by offloading tasks like encoding, decoding, and filtering to the GPU. NVIDIA provides two main components for this: NVENC for video encoding and CUDA for hardware-accelerated filters and scaling. NVENC: NVIDIA Hardware Video Encoder NVENC is a dedicated encoding engine built into NVIDIA GPUs. It allows fast H.264 and H.265 encoding with minimal CPU load, making it ideal for high-performance transcoding and streaming workflows. Check NVENC Support This command checks whether NVENC support is available in your FFmpeg build. It filters for NVENC encoders (e.g., h264_nvenc and hevc_nvenc ). ffmpeg -encoders | grep nvenc H.264 Encoding with NVENC This command encodes the input video to H.264 using NVENC . This hardware-accelerated encoding significantly reduces the load on the CPU. ffmpeg -i input.mp4 -c:v h264_nvenc output.mp4 H.265 Encoding with NVENC This command encodes the video to H.265 (HEVC) using NVENC , offering better compression at the same quality compared to H.264. ffmpeg -i input.mp4 -c:v hevc_nvenc output.mp4 Constant Bitrate Encoding (CBR) This command encodes the video with a constant bitrate of 4 Mbps using NVENC . It is commonly used for streaming, where consistent video quality is needed. ffmpeg -i input.mp4 -c:v h264_nvenc -b:v 4M output.mp4 Constant Quality Encoding (QP-Based) This command performs constant quality encoding with a QP (Quantization Parameter) value of 23, using NVENC . The QP value controls the trade-off between quality and file size. ffmpeg -i input.mp4 -c:v h264_nvenc -rc constqp -qp 23 output.mp4 CUDA: GPU-Accelerated Filters and Processing CUDA is NVIDIA’s GPU compute framework. In FFmpeg, it speeds up decoding, scaling, and filters like deinterlacing or color adjustments. Upload Video Frames to GPU This command uploads video frames to the GPU for processing using CUDA . It is useful when applying hardware-accelerated filters to the video. ffmpeg -hwaccel cuda -hwaccel_output_format cuda -i input.mp4 -vf 'hwupload_cuda' -f null - Scaling with CUDA This command scales the video to 1280x720 using CUDA , then encodes it with NVENC . The GPU accelerates the scaling operation ffmpeg -hwaccel cuda -i input.mp4 -vf 'scale_cuda=1280:720' -c:v h264_nvenc output.mp4 Deinterlacing with CUDA This command uses CUDA to apply the yadif_cuda filter for deinterlacing and encodes the video with NVENC . ffmpeg -hwaccel cuda -i input.mp4 -vf 'yadif_cuda' -c:v h264_nvenc output.mp4 Decoding with CUVID This command uses CUVID for hardware-accelerated decoding with CUDA , followed by encoding with NVENC for faster processing. ffmpeg -hwaccel cuda -c:v h264_cuvid -i input.mp4 -c:v h264_nvenc output.mp4 Monitor GPU Activity This command monitors the GPU’s usage in real-time by running nvidia-smi every second. It helps in tracking GPU memory and performance while processing video. watch -n 1 nvidia-smi What’s Next? Planning to offload heavy video processing tasks to your GPU? Use Cincopa to integrate FFmpeg-based NVENC and CUDA workflows into your backend. Trigger hardware-accelerated encoding jobs, chain GPU filters, and scale live video processing without CPU bottlenecks—right from the cloud or on-prem.