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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchFFmpeg hardware encoding on Contabo requires a Contabo instance that actually exposes a compatible NVIDIA GPU. Contabo describes its regular VPS plans as shared-vCPU servers, while its separate GPU VPS documentation specifies a dedicated NVIDIA GPU passed through to the virtual machine. Installing FFmpeg, CUDA, or an NVIDIA driver on an ordinary VPS does not create GPU access. If your instance has no accessible GPU, NVENC cannot run there.
1. Confirm that your Contabo VPS has GPU access
Check the exact product in your Contabo account before changing software. Contabo documents GPU access for its separate GPU VPS offering, not as a feature of its regular VPS plans. On the GPU VPS, the documented device is an NVIDIA RTX 6000 PRO Blackwell Server Edition passed through to the instance. The product documentation lists 18 vCPUs, 96 GB RAM, 96 GB of GPU memory, and a 900 GB NVMe drive, as documented on October 3, 2026. These are product specifications, not encoding benchmarks.
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Contabo documents an Ubuntu 24.04 LTS image with NVIDIA driver and CUDA toolkit preinstalled. Its GPU VPS 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. Availability and configurations can change, so verify the current product page and configurator before ordering.
Check the GPU and driver
On the GPU VPS, connect over SSH and run:
nvidia-smiA working setup should show the NVIDIA device and driver information. If the command is missing, reports no devices, or cannot communicate with the driver, resolve that at the instance, image, or driver level first. An FFmpeg build cannot compensate for a GPU that the operating system cannot see.
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2. Check whether your FFmpeg build advertises NVENC
Run these checks on the same machine where you intend to encode:
ffmpeg -hide_banner -encoders | grep -i nvenc
ffmpeg -hide_banner -decoders | grep -i cuvid
ffmpeg -hide_banner -hwaccelsThe first command looks for NVIDIA hardware encoders, the second for NVIDIA-related decoders, and the third prints hardware acceleration methods advertised by the binary. NVIDIA recommends checking the encoder and decoder listings when verifying an NVENC/NVDEC-enabled FFmpeg build. These lists show what the build supports; they do not guarantee that the installed driver and actual GPU can run every listed option. Runtime availability depends on the hardware and a suitable driver, as FFmpeg’s hardware-acceleration documentation explains.
3. Encode with NVENC
Start with a short H.264 test
For an H.264 output, try a short representative clip first:
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ffmpeg -i input.mp4 -c:v h264_nvenc -c:a copy output.mp4Replace the input and output filenames with your own. This selects NVIDIA’s H.264 encoder for video and copies the audio stream without re-encoding it. If you need a different output codec, FFmpeg also has encoder names such as hevc_nvenc and av1_nvenc in supported builds; use them only when your GPU, FFmpeg build, and intended playback format support the codec, profile, and bit depth you need. Consult NVIDIA’s FFmpeg with NVIDIA GPU Hardware Acceleration guide and its codec support information for compatibility.
Read the complete FFmpeg output for errors, then check that the resulting file plays as intended. Compare elapsed time, output quality, and file size against a CPU encode using the same source and comparable settings. Hardware and software encoders can differ in quality and bitrate behavior; NVENC does not promise an identical file or a universal speed advantage.
4. Decide whether to accelerate decoding too
NVENC can encode the output even if FFmpeg decodes the input on the CPU. If the input codec is supported and keeping frames on the GPU suits the rest of your workflow, test hardware decoding with input options before -i:
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ffmpeg -hwaccel cuda -hwaccel_output_format cuda -i input.mp4
-c:v h264_nvenc -c:a copy output.mp4Hardware decoding is a separate choice from hardware encoding. Check that the GPU and driver support the source codec and that your filters can operate on GPU-resident frames. Some filters require transferring frames between GPU and host memory or running on the CPU; those copies and conversions can reduce performance. A command using NVENC alone is not necessarily a fully GPU-resident pipeline.
5. If NVENC is missing from FFmpeg
First check whether a suitable precompiled FFmpeg binary is available for your Ubuntu image. NVIDIA’s guide states: “When using pre-compiled FFmpeg binaries, ensure they are built with NVENC/NVDEC support enabled.” If your binary does not advertise NVENC, installing a different build may be simpler than compiling FFmpeg yourself.
A source build is a fallback when you specifically need one. NVIDIA’s Linux instructions cover build dependencies, the separate nv-codec-headers project (also called ffnvcodec), and configuring and compiling FFmpeg. Before following build commands, verify that they match your FFmpeg branch, Ubuntu image, driver minimum, and codec-header/SDK compatibility. NVIDIA’s current 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 add it by habit.
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6. Verify the real workload and understand the trade-offs
A listed encoder proves that the binary advertises the feature; a successful representative transcode is the practical check. Run a clip representative of your actual files and filters, inspect FFmpeg’s logs, and, where useful, observe nvidia-smi while the job runs. Compare the same workload’s elapsed time, output quality, file size or bitrate, and CPU/GPU utilization.
End-to-end performance depends on more than the encoder. GPU-to-host frame copies, decoding, filters, CPU work, storage input/output, initialization, and the source material all affect results. FFmpeg’s hardware-acceleration documentation cautions that copying frames can reduce performance; NVIDIA also notes that FFmpeg pipeline measurements need not match standalone SDK performance. There is no universal speed, cost, or quality winner established for every workload.
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7. Troubleshoot common failures
| Symptom | Likely cause | What to check or do |
|---|---|---|
nvidia-smi is missing or shows no GPU |
The instance may not have an exposed GPU, or the driver/device access is not working. | Confirm the exact Contabo plan. If it is an ordinary VPS, the cited Contabo documentation does not describe it as GPU-enabled. On a GPU VPS, resolve device and driver visibility before debugging FFmpeg. |
No h264_nvenc appears in the encoder list |
The FFmpeg binary may have been built without NVENC support. | Try a suitable precompiled build. If compiling from source, follow current NVIDIA instructions and align the build with your driver, FFmpeg branch, and codec headers. |
| The encoder is listed but the command fails at runtime | The build listing does not establish compatibility with the actual GPU or driver. | Check nvidia-smi, the full FFmpeg error output, and the GPU/driver requirements for the codec and encoding options selected. |
| Hardware decoding fails although NVENC is available | Decode capability is separate; the input codec or GPU may not support the requested decode path. | Test output encoding without hardware decode, then check the input codec against the GPU’s supported capabilities. |
| The GPU encode is not faster, or filters fail | Frame transfers, unsupported GPU filters, CPU-side processing, or storage can limit the whole pipeline. | Compare a short representative job, inspect the filter path, and avoid assuming that adding -hwaccel cuda accelerates every stage. |
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