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FFmpeg hardware encoding on Contabo requires a Contabo instance that actually exposes a compatible NVIDIA GPU. Contabo documents that capability on its separate GPU VPS, not as a feature of its regular shared-vCPU VPS plans. Installing CUDA or an NVENC-enabled FFmpeg build cannot add a GPU to an ordinary VPS. On a GPU VPS, confirm the device with nvidia-smi, check that your FFmpeg binary lists NVENC, then choose an encoder such as h264_nvenc for output. Contabo’s GPU VPS documentation and NVIDIA’s FFmpeg guide describe the relevant setup.
First confirm your Contabo VPS has a GPU
Check the exact instance in your Contabo account. The regular VPS documentation describes shared-vCPU VPS families; the separate GPU VPS documentation describes a dedicated NVIDIA GPU connected through PCIe passthrough. If you have an ordinary VPS, stop here: there is no NVENC device for FFmpeg to use. A driver installation, CUDA toolkit, or different FFmpeg binary does not change that.
Contabo’s GPU VPS documentation accessed in 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. The documented offer lists EU and US Central availability and Ubuntu 24.04 LTS as its only operating system; it also states there is no regional migration or upgrade/downgrade path. These are product details, not performance benchmarks, and availability or terms can change. Check the current GPU VPS product page and configurator before choosing an instance.
Verify the NVIDIA device and driver
On the GPU VPS, connect over SSH and run:
nvidia-smiA working setup should show the passed-through NVIDIA device and an installed driver. NVIDIA recommends this command to verify GPU and driver installation. If it reports that it cannot communicate with the driver or no device is present, resolve the instance, image, or driver access issue before troubleshooting FFmpeg. The Contabo GPU VPS documentation says its Ubuntu 24.04 LTS CUDA image includes the NVIDIA driver and CUDA toolkit preinstalled and that the passed-through GPU is visible with this command.
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Check whether your FFmpeg build advertises NVENC
Run these checks against the same ffmpeg binary you plan to use:
ffmpeg -hide_banner -encoders | grep -i nvenc
ffmpeg -hide_banner -decoders | grep -i cuvid
ffmpeg -hide_banner -hwaccelsh264_nvenc,hevc_nvenc, orav1_nvencin the encoder list indicates that the build advertises that encoder.- Entries in the decoder list show what decoder support the build advertises; they do not prove every input codec will work on your GPU.
cudain the hardware-acceleration list indicates that the build advertises CUDA acceleration.
These lists are a build-capability check, not proof that an encode will succeed. Runtime use also depends on a visible compatible GPU, a suitable driver, and support for the codec, profile, and bit depth you intend to use. NVIDIA’s guide puts it plainly: “When using pre-compiled FFmpeg binaries, ensure they are built with NVENC/NVDEC support enabled.” See the NVIDIA Video Codec SDK 13.1 FFmpeg guide for its codec support matrix and version requirements.
Encode output with NVENC
Start with a simple H.264 transcode
For an H.264 output, try a short representative input first:
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ffmpeg -i input.mp4 -c:v h264_nvenc -c:a copy output.mp4This asks FFmpeg to encode video with NVENC and copy the audio stream without re-encoding it. Replace the filenames with your own. The example is instructional, not a tested benchmark or a universal quality/size recommendation. Inspect FFmpeg’s output for errors, then verify that the result plays as expected and compare its file size and elapsed time with your needs.
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You can use hevc_nvenc or av1_nvenc only if the GPU, FFmpeg build, and target playback or delivery format support the desired codec and settings. Do not infer support for every profile or bit depth merely because an encoder name appears in the list. Check NVIDIA’s GPU/codec matrix and the FFmpeg build’s available options for the exact combination.
Driver and build compatibility matters: the installed driver must meet the requirements of the FFmpeg build and the codec SDK version it uses. A precompiled FFmpeg binary with the needed support is usually the simpler starting point; source compilation is a fallback when a suitable binary is unavailable.
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Decide whether to accelerate decoding too
NVENC can encode the output while FFmpeg decodes input on the CPU. Hardware decoding is a separate choice: the input codec must be supported, and the processing pipeline must benefit from keeping frames on the GPU. NVIDIA’s example for CUDA decoding followed by H.264 NVENC encoding is:
ffmpeg -hwaccel cuda -hwaccel_output_format cuda -i input.mp4
-c:v h264_nvenc -c:a copy output.mp4Input options such as -hwaccel belong before -i. This example requests CUDA hardware decoding and CUDA-resident frames for a compatible pipeline. Filters may require GPU-compatible implementations; other filters or processing steps can force frames to move between GPU and host memory. A pipeline that uses NVENC but decodes or filters on the CPU is not fully GPU-resident.
FFmpeg’s hardware acceleration documentation cautions that copying frames between device and system memory can reduce performance. GPU access therefore does not guarantee a faster end-to-end job: storage, CPU work, filters, initialization, and data transfers also matter.
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If FFmpeg does not show NVENC
Prefer a suitable precompiled binary
If nvidia-smi works but the encoder list has no NVENC entries, the likely issue is the FFmpeg binary, not absence of a GPU. Find a precompiled build appropriate for your operating system and driver that explicitly includes NVENC/NVDEC support. Re-run the encoder-list check with the exact binary you will use.
Build FFmpeg only when needed
If no suitable binary is available, NVIDIA’s Linux instructions describe installing build dependencies and the separate nv-codec-headers project (also known as ffnvcodec), then configuring and compiling FFmpeg. Do not paste old configure commands blindly: check the current FFmpeg branch, driver minimum, codec SDK/header compatibility, and your operating system first. NVIDIA’s current guide warns that CUDA NPP is deprecated in FFmpeg for CUDA versions above 12.8 and recommends avoiding --enable-libnpp. Follow the current NVIDIA build guidance rather than assuming a source build is always required.
Validate the real job and compare trade-offs
Encoder listing alone is not a successful transcode. Run a representative file and inspect FFmpeg’s logs for runtime errors. While it runs, nvidia-smi can help show whether the device is being used. Compare the same input and intended settings by elapsed time, output playback and quality, file size or bitrate, and CPU/GPU utilization. There is no universal speed or quality winner: workload and pipeline design affect the result, and FFmpeg measurements need not match standalone SDK performance.
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Common problems and fixes
| Symptom | Likely cause | What to check or do |
|---|---|---|
nvidia-smi cannot see a GPU |
The VPS does not expose a GPU, or the GPU/driver access is not working. | Confirm the exact Contabo plan. If it is an ordinary VPS, NVENC is unavailable there. On the GPU VPS, resolve the image, passed-through device, or driver issue before changing FFmpeg. |
No *_nvenc encoder appears |
The FFmpeg binary was built without NVENC support. | Use a suitable precompiled build or compile FFmpeg with compatible NVIDIA codec headers and SDK support; check the binary again. |
| Encoder is listed but encoding fails at runtime | Driver/build incompatibility, unsupported GPU codec settings, or no usable device at runtime. | Check nvidia-smi, driver requirements, FFmpeg logs, and the GPU’s codec/profile/bit-depth support. |
| Hardware decode option fails | The input codec or device path is unsupported, or the FFmpeg build/driver is unsuitable. | Test the same output with CPU decoding by removing the input hardware-acceleration options. If that works, investigate decode support and the CUDA frame path separately. |
| GPU encoding is not faster for the complete job | Frame transfers, filters, CPU work, storage, or initialization may dominate. | Measure a representative end-to-end job and keep frames on the GPU where supported and useful; compare identical settings and outputs. |
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