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This configuration requires [Nvidia GPU](https://developer.nvidia.com/cuda-gpus) with [NVENC](https://developer.nvidia.com/nvidia-video-codec-sdk) support.
This configuration requires [Nvidia GPU](https://developer.nvidia.com/cuda-gpus) with [NVENC](https://developer.nvidia.com/nvidia-video-codec-sdk) support and [Cuda](https://developer.nvidia.com/cuda-zone) support.
The base image is available at [`ghcr.io/m1k1o/neko/nvidia-base`](https://ghcr.io/m1k1o/neko/nvidia-base).
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The base image is available at [`ghcr.io/m1k1o/neko/nvidia-base`](https://ghcr.io/m1k1o/neko/nvidia-base). See [Examples](/docs/v3/installation/examples#nvidia) for more information and usage.
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## Nvidia GPU Acceleration {#nvidia}
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Neko supports hardware acceleration using Nvidia GPUs. To use this feature, you need to have the Nvidia Container Toolkit installed on your system. You can find the installation instructions [here](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html).
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Neko supports hardware acceleration using Nvidia GPUs. To use this feature, you need to have the Nvidia Container Toolkit installed on your system. You can find the installation instructions [here](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html). Check if your GPU supports hardware encoding with [this list](https://developer.nvidia.com/video-encode-decode-gpu-support-matrix).
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This example shows how to accelerate video encoding and as well the browser rendering using the GPU. You can test if the GPU is used by running `nvidia-smi`, which should show the GPU usage of both the browser and neko. In the browser, you can run the [WebGL Aquarium Demo](https://webglsamples.org/aquarium/aquarium.html) to test the GPU usage.
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This example shows how to accelerate video encoding and as well the browser rendering using the GPU. You can test if the GPU is used by running `nvtop` or `nvidia-smi`, which should show the GPU usage of both the browser and neko. In the browser, you can run the [WebGL Aquarium Demo](https://webglsamples.org/aquarium/aquarium.html) to test the GPU usage.
See available [Nvidia Docker Images](/docs/v3/installation/docker-images#nvidia).
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If you only want to accelerate the encoding, **not the browser rendering**, you can use the default image with additional environment variables:
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:::note
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If your Nvidia GPU does not support CUDA, you can use the pipeline below without `cudaupload` and `cudaconvert`. This should work with older GPUs, but the performance might be lower.
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:::
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If you only want to accelerate the encoding, **not the browser rendering**, and you do not need [Cuda library](https://gstreamer.freedesktop.org/documentation/cuda/index.html?gi-language=c), you can use the default image with additional environment variables:
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