To use FFmpeg hardware encoding on Contabo, your VPS must expose a compatible NVIDIA GPU, the NVIDIA driver must work, and your FFmpeg binary must include NVENC support. Installing CUDA or FFmpeg on an ordinary shared-vCPU VPS does not add a GPU. Contabo documents GPU passthrough for its separate GPU VPS product; start by checking which instance you have.
1. Confirm that your Contabo VPS has a GPU
Contabo’s regular VPS documentation describes shared-vCPU VPS families, while its separate GPU VPS documentation describes a dedicated NVIDIA GPU connected by PCIe passthrough. The regular VPS plan description does not establish GPU access. If your instance is an ordinary VPS, stop here: these steps cannot enable NVENC without a GPU exposed to the server. See Contabo VPS documentation and Contabo GPU VPS documentation.
On a GPU VPS, connect to the server and run:
nvidia-smiA working driver and GPU should appear in the output. If the command is missing, reports that it cannot communicate with the driver, or shows no device, resolve GPU or driver access before troubleshooting FFmpeg. NVIDIA recommends this check to verify GPU and driver installation.
Contabo GPU VPS configuration and availability
As documented on October 3, 2026, Contabo lists one GPU VPS configuration: an NVIDIA RTX 6000 PRO Blackwell Server Edition with 96 GB of GPU memory, 18 vCPUs, 96 GB RAM, and 900 GB NVMe. Its Ubuntu 24.04 LTS CUDA image has the NVIDIA driver and CUDA toolkit preinstalled, and Contabo says the passed-through GPU is visible with nvidia-smi. The documentation lists EU and US Central availability, Ubuntu 24.04 LTS as the only operating system, no regional migration, and no upgrade or downgrade path. These are product details, not performance benchmarks; check the current GPU VPS page and configurator for current availability and terms.
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2. Check whether FFmpeg advertises NVENC
Run these checks on the same server and with the same FFmpeg binary you intend to use:
ffmpeg -hide_banner -encoders | grep -i nvenc
ffmpeg -hide_banner -decoders | grep -i cuvid
ffmpeg -hide_banner -hwaccelsThe encoder listing should include an encoder such as h264_nvenc if that binary advertises H.264 NVENC. The decoder and hardware-acceleration lists indicate what the build advertises for decoding and hardware acceleration. These listings do not prove that the device, driver, codec profile, or actual runtime operation will work. NVIDIA advises ensuring that precompiled FFmpeg binaries are built with NVENC/NVDEC support enabled; runtime availability also depends on supported hardware and a suitable driver. Consult NVIDIA’s FFmpeg with NVIDIA GPU Hardware Acceleration guide and FFmpeg’s hardware-acceleration documentation.
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3. Encode with NVENC
Start with an H.264 output
For a simple test that copies the audio and encodes the video with H.264 NVENC:
ffmpeg -i input.mp4 -c:v h264_nvenc -c:a copy output.mp4Replace the filenames with your own. This is an illustrative command, not a tested result. Run it first on a short, representative file and read the complete FFmpeg output for errors. Check that the resulting file plays, then compare its elapsed time, quality, and file size with your existing workflow.
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Choose HEVC or AV1 only when supported
FFmpeg may also advertise hevc_nvenc or av1_nvenc, but do not assume every GPU supports each codec, profile, or bit depth. Check the codec support matrix and capabilities for your exact GPU, FFmpeg build, and intended output format in NVIDIA’s versioned guide.
4. Decide whether to use hardware decoding
Encoding and decoding are separate choices. You can encode with NVENC while leaving input decoding on the CPU. If your input codec is supported and keeping frames on the GPU benefits your pipeline, try a command like this:
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ffmpeg -hwaccel cuda -hwaccel_output_format cuda -i input.mp4
-c:v h264_nvenc -c:a copy output.mp4Input options such as -hwaccel cuda belong before -i. Hardware decoding is useful only when the input codec and device support it and the rest of the pipeline can use the resulting frames effectively. Filters that are not compatible with GPU frames may require transfers between GPU and host memory, or may run on the CPU. In that case, the job is not a fully GPU-resident pipeline. See NVIDIA’s pipeline guidance and FFmpeg’s hardware-acceleration documentation.
5. Fix a missing NVENC encoder in FFmpeg
If nvidia-smi works but FFmpeg does not list an NVENC encoder, the binary may lack NVENC support. Prefer a suitable precompiled FFmpeg build when available. A custom build is a fallback, not an automatic requirement.
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NVIDIA’s Linux build guidance covers installing dependencies, obtaining the separate nv-codec-headers project (also called ffnvcodec), then configuring and compiling FFmpeg. Before following build commands, check compatibility among your operating-system image, FFmpeg branch, installed driver, codec SDK, and headers in the current NVIDIA instructions. NVIDIA’s guide notes that CUDA NPP is deprecated in FFmpeg for CUDA versions above 12.8 and recommends avoiding --enable-libnpp in that context. Do not copy an old build recipe without checking current requirements.
6. Validate the real workload and weigh the trade-offs
An encoder appearing in FFmpeg’s list establishes build capability, not successful performance on your media. Run a representative job and inspect FFmpeg’s logs; if useful, check nvidia-smi while it runs to see whether the GPU is being used. Compare the same source and intended settings across workflows, noting:
- Elapsed time and CPU/GPU utilization.
- Output playback and visible quality.
- Bitrate or resulting file size.
- Whether decoding, filters, and encoding stay on the GPU or incur host/device transfers.
GPU access does not guarantee a faster end-to-end job. Transfers, filters, CPU work, storage, initialization, and workload all affect results; FFmpeg’s documentation warns that frame copies can reduce performance. Do not assume NVENC is always faster or that GPU and CPU encodes will have identical quality or file size. Measure your own material and settings. NVIDIA also cautions that FFmpeg measurements can differ from standalone SDK performance in its pipeline guidance.
7. Troubleshoot common failures
| Symptom | Likely cause | What to check or do |
|---|---|---|
nvidia-smi cannot find or communicate with a device |
The instance may not expose a GPU, or driver/device access is not working. | Confirm the exact Contabo plan. On the GPU VPS, resolve the instance, image, or driver problem before changing FFmpeg. |
FFmpeg lists no *_nvenc encoder |
The FFmpeg binary may have been built without NVENC support. | Try a suitable precompiled build. If none fits, follow current NVIDIA build instructions and verify driver, headers, SDK, and FFmpeg compatibility. |
| FFmpeg lists an encoder but the command fails at runtime | The list shows build support, but hardware, driver, codec, profile, or bit-depth support may not match. | Read the full error output; confirm nvidia-smi works and check the exact GPU’s codec capabilities and driver requirements. |
| Hardware decoding or a filter reports an incompatibility | The input format may not be supported, or a filter may not accept GPU-resident frames. | Test encoding without hardware decode first. Then add GPU decoding or compatible filters one at a time, checking whether frames move between device and host memory. |
| The GPU command runs but the whole job is not faster | Transfers, filters, CPU work, storage, initialization, or the workload may dominate elapsed time. | Compare timing, utilization, output quality, and file size on the same representative source and settings. |
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Quick Recap
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