Quick wins for a faster PC:
Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →FFmpeg hardware encoding on Contabo requires a Contabo VPS that actually exposes a compatible NVIDIA GPU. Contabo describes GPU access on its separate GPU VPS product, not its ordinary shared-vCPU VPS plans; installing CUDA or FFmpeg on an ordinary VPS will not create GPU access. If you have the GPU VPS, confirm the device with nvidia-smi, check that your FFmpeg build advertises NVENC, then test a short encode before relying on the setup.
First check whether your Contabo VPS has a GPU
In the Contabo control panel, identify the exact instance and plan. Contabo’s regular VPS documentation describes shared-vCPU VPS families, while its separate GPU VPS documentation describes an NVIDIA GPU attached through PCIe passthrough. NVENC is not enabled merely by installing a driver, CUDA, or FFmpeg on an ordinary VPS.
Contabo’s GPU VPS documentation, accessed October 3, 2026, lists one NVIDIA RTX 6000 PRO Blackwell Server Edition with 96 GB of GPU memory, 18 vCPUs, 96 GB RAM, and 900 GB NVMe. It lists an Ubuntu 24.04 LTS CUDA image with the NVIDIA driver and CUDA toolkit preinstalled, and says the passed-through GPU is visible with nvidia-smi. Availability and terms can change; check the [Contabo GPU VPS](https://contabo.com/en/gpu-vps/) product page and configurator before ordering. The listed locations are EU and US Central; the documentation says there is no regional migration or upgrade/downgrade path and Ubuntu 24.04 LTS is the only operating system.
Verify GPU and driver access
On the GPU VPS, connect over SSH and run:
nvidia-smiA successful result should show the NVIDIA device and driver information. If the command is missing, cannot communicate with the driver, or shows no GPU, resolve the instance, image, or driver-access issue before changing FFmpeg. NVIDIA recommends this command to verify GPU and driver installation; it cannot make unsupported hardware available.
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Check that your FFmpeg build supports NVIDIA acceleration
A working GPU driver is only one requirement. FFmpeg must also be built with NVENC/NVDEC support, and the driver must meet the requirements for the FFmpeg build and codec SDK. NVIDIA’s [Video Codec SDK 13.1 FFmpeg guide](https://docs.nvidia.com/video-technologies/video-codec-sdk/13.1/ffmpeg-with-nvidia-gpu/index.html) says: “When using pre-compiled FFmpeg binaries, ensure they are built with NVENC/NVDEC support enabled.” A custom source build is a fallback, not automatically necessary.
Check what the installed binary advertises:
ffmpeg -hide_banner -encoders | grep -i nvenc
ffmpeg -hide_banner -decoders | grep -i cuvid
ffmpeg -hide_banner -hwaccels- An encoder listing that includes
h264_nvencmeans the binary advertises that output encoder. - The decoder listing and hardware-acceleration list indicate advertised decode/acceleration support, not proof that every codec or GPU works at runtime.
- If NVENC is absent, first look for a suitable precompiled FFmpeg build. If none is appropriate, you may need to compile FFmpeg with NVIDIA codec support.
Encode with NVENC
Start with a simple H.264 output
For an H.264 encode with audio copied from the input, use:
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ffmpeg -i input.mp4 -c:v h264_nvenc -c:a copy output.mp4This is an illustrative command, not a guarantee that a given input, output container, or GPU configuration will succeed unchanged. Replace the filenames with your own. Try a short representative clip first, inspect FFmpeg’s output for errors, and play the result before running a long job.
Choose a codec the whole setup supports
Other NVIDIA encoder names include hevc_nvenc and av1_nvenc, but do not assume your GPU supports every codec, profile, or bit depth. Check the [NVIDIA guide’s codec support information](https://docs.nvidia.com/video-technologies/video-codec-sdk/13.1/ffmpeg-with-nvidia-gpu/index.html) against your exact GPU and the output format you need. The encoder list alone does not establish support for every combination.
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Decide whether to use hardware decoding as well
NVENC can encode output even when FFmpeg decodes on the CPU. Hardware decoding is a separate decision: use it only if the input codec is supported and the rest of the pipeline benefits from GPU processing. NVIDIA’s examples use input options before -i:
ffmpeg -hwaccel cuda -hwaccel_output_format cuda -i input.mp4
-c:v h264_nvenc -c:a copy output.mp4Keeping decoded frames in GPU memory can help avoid unnecessary copies, but filters must also be compatible with that path. A CPU-only filter, or a transfer between GPU and host memory, can add overhead. If you use NVENC for output but decode or filter on the CPU, the job is not a fully GPU-resident pipeline.
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When FFmpeg lacks NVENC support
Prefer a suitable precompiled binary where possible. If the available binary lacks NVENC/NVDEC, NVIDIA’s Linux instructions describe installing build dependencies, obtaining the separate nv-codec-headers project (also known as ffnvcodec), then configuring and compiling FFmpeg. Follow the current [NVIDIA FFmpeg build instructions](https://docs.nvidia.com/video-technologies/video-codec-sdk/13.1/ffmpeg-with-nvidia-gpu/index.html) rather than copying commands from an older guide: driver minimums, SDK/header compatibility, FFmpeg branches, and operating-system packages matter. NVIDIA’s current guide also warns that CUDA NPP is deprecated in FFmpeg for CUDA versions above 12.8 and recommends avoiding --enable-libnpp.
Validate the real workload and weigh the trade-offs
An encoder appearing in FFmpeg’s list proves build capability, not that a real transcode succeeded or will be faster end to end. Run the same representative material and compare elapsed time, output playback, quality, file size or bitrate, and CPU/GPU utilization. Check FFmpeg’s logs and, if useful, run nvidia-smi during the job.
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GPU acceleration can be offset by transfers between GPU and host memory, CPU-side filters, storage speed, initialization, and other workload details. FFmpeg’s [hardware-acceleration documentation](https://ffmpeg.org/ffmpeg.html#Hardware-acceleration) cautions that copying frames can reduce performance. Do not assume NVENC is always faster or that it produces the same quality and file size as a CPU encoder at comparable settings; measure your own pipeline.
Troubleshooting common NVENC setup failures
| Symptom | Likely area to check | Next step |
|---|---|---|
nvidia-smi is missing or cannot see a GPU |
Instance type, image, or driver access | Confirm the instance is Contabo’s GPU VPS and that its driver is installed and functioning before troubleshooting FFmpeg. |
Unknown encoder 'h264_nvenc' |
FFmpeg build | Check ffmpeg -encoders; use a suitable build with NVENC support or build FFmpeg following current NVIDIA instructions. |
| FFmpeg lists NVENC, but the encode fails at runtime | Hardware visibility, driver compatibility, or codec support | Check nvidia-smi, driver/build compatibility, and whether the exact GPU supports the requested codec, profile, or bit depth. |
| Hardware decoding or a filter fails with a CUDA/device-frame error | Decode support, option placement, or filter compatibility | Confirm the input codec is supported, place -hwaccel options before -i, and check whether the filter accepts GPU frames or requires a transfer. |
| The job runs but is not faster | Whole-pipeline overhead | Measure transfers, CPU filters, storage, and utilization; compare identical input and output settings rather than judging by encoder choice alone. |
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Quick Recap
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