Video Decode GPU Requirements

Dynamo provides two video-input decode paths. CUDA runtime images can use both; vLLM CPU and XPU runtimes can use the frontend software path:

• H.264 and H.265 (HEVC) decode on the GPU using NVDEC, NVIDIA’s dedicated hardware video decoder, through PyNvVideoCodec.
• VP8 and VP9 decode on the CPU through Dynamo’s codec-limited, in-tree FFmpeg when frontend decoding is enabled on vLLM, or SGLang on CUDA.

The in-tree FFmpeg does not include H.264, H.265, or AV1 decoders. Without frontend decoding, video input remains owned by the backend and requires its Python decode carrier (OpenCV, PyAV, or decord). The images deliberately omit those wider software-decode carriers by default; vLLM does ship OpenCV, but it is built without a video backend for still-image work only.

This page covers which GPUs provide NVDEC, what the container must expose, and how Dynamo behaves when hardware decode is unavailable.

GPU support

NVDEC is a fixed-function decode engine, separate from the SMs used for inference, so decoding adds negligible load to the GPU beyond a small YUV-to-RGB conversion.

A common misconception is that datacenter GPUs have no video engines. That applies to NVENC, the hardware encoder, which NVIDIA omits from datacenter parts. The decoder is present:

Every GPU above decodes both codecs Dynamo routes to hardware, so H.264 and H.265 video input works across the datacenter lineup. Hopper’s NVDEC matches Turing’s feature set and does not decode AV1; Blackwell adds AV1 decode. The Ada parts (L4, L40S, RTX 6000 Ada) decode AV1 as well, and unlike the datacenter accelerators they also carry NVENC.

Because no datacenter GPU ships NVENC, Dynamo’s video generation path encodes with a CPU VP9 encoder rather than a hardware H.264 encoder.

Container requirements

NVDEC links libnvcuvid at runtime, which the NVIDIA container runtime only mounts when the video driver capability is requested. Without it, import PyNvVideoCodec fails and Dynamo falls back to software decode.

Dynamo’s runtime images already declare it:

ENV NVIDIA_DRIVER_CAPABILITIES=video,compute,utility

With Docker that is usually enough, because the toolkit reads the capability from the image. To be explicit, or when overriding the variable for other reasons:

docker run --gpus all -e NVIDIA_DRIVER_CAPABILITIES=video,compute,utility ...

Kubernetes

Add it to the container that runs the worker:

spec:
  containers:
    - name: worker
      env:
        - name: NVIDIA_DRIVER_CAPABILITIES
          value: video,compute,utility
      resources:
        limits:
          nvidia.com/gpu: "1"

If it still does not take effect, the capability is being dropped below the pod. Check supported-driver-capabilities in /etc/nvidia-container-runtime/config.toml on the node, and — if the cluster runs in CDI mode — whether the generated device spec includes the video libraries, since in that mode capabilities come from the spec rather than the environment variable.

Verifying hardware decode

python3 -c "
from dynamo.common.multimodal.nvdec_decoder import nvdec_available
print('NVDEC available:', nvdec_available())
"

False means Dynamo will not use hardware decode in that container. Check, in order: the video driver capability, that PyNvVideoCodec is installed, and that DYN_DISABLE_NVDEC is unset.

To distinguish “capability missing” from every other cause, look for the decode library itself. It is mounted by the container runtime, not installed by the image, so its absence points squarely at the capability:

ldconfig -p | grep -i nvcuvid

Expect libnvcuvid.so.1. Nothing means the video capability did not reach this container. If it is present on the node but not inside, the capability is being dropped between the two.

Behavior when NVDEC is unavailable

Hardware decode is additive and never blocks a request on its own: routing falls through to a software decode path where one exists. VP8 and VP9 frontend decoding on vLLM, or SGLang on CUDA, does not depend on NVDEC.

Installing a software decoder

To decode a format NVDEC does not cover — or H.264/H.265 on a host with no NVDEC — explicitly install the backend’s decode package at the validated version bounds:

# vLLM: audio input
pip install --no-deps 'av>=18.0.0,<19'
# SGLang: video input
pip install --no-deps 'decord2>=3.4.0,<4'
# TensorRT-LLM: video input
pip install --no-deps 'opencv-python-headless>=4.13.0.92,<5'

vLLM video input is the exception, because those images already ship OpenCV built without a video backend. Adding video decode there means replacing that build with the binary wheel of the same version, not installing a range:

VERSION=$(pip show opencv-python-headless | awk '/^Version:/{print $2}')
if [ -n "$VERSION" ]; then
  SPEC="opencv-python-headless==${VERSION}"
# Without metadata, match the library version plus its packaging revision.
elif VERSION=$(python -c 'import cv2; print(cv2.__version__)') && [ -n "$VERSION" ]; then
  SPEC="opencv-python-headless==${VERSION}.*"
else
  SPEC='opencv-python-headless>=4.13.0.92,<5'
fi
pip install --no-deps --force-reinstall --only-binary opencv-python-headless "$SPEC"

With neither pip metadata nor an importable cv2, this falls back to the bounded range instead of an unresolvable ==.* pin.

Nothing installs automatically — this is a deliberate operator step. The images also ship an installer with the same bounds plus idempotency and air-gap support (python -m dynamo.common.utils.install_media_decoders <backend>). For vLLM, it replaces codec-free OpenCV with a same-version binary wheel and installs missing PyAV. See Additional Media Decoders for the full workflow, including baking the install into an image layer for Kubernetes.

Hardware encode (NVENC)

There is nothing to enable. Dynamo does not use NVENC on any path.

Video output — the generation path — encodes VP9 on the CPU with the in-tree FFmpeg. That is deliberate and works everywhere: no datacenter GPU ships an encoder, so a hardware encode path would be unavailable on exactly the parts most deployments run. On workstation parts that do have NVENC (L4, L40S, RTX 6000 Ada) it simply stays unused.

The same video driver capability governs both engines, so a container configured for NVDEC as above needs no additional change. Encode performance does not depend on it.

Configuration

Sources

• NVIDIA Video Encode and Decode GPU Support Matrix
• NVDEC Application Note