blog post jellyfin
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_posts/2025-12-26-how-to-install-jellyfin-with-podman.md
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_posts/2025-12-26-how-to-install-jellyfin-with-podman.md
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layout: post
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author: Sam Hadow
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tags: podman sysadmin
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---
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In this blog post I'll show you how to run jellyfin with podman and how to use hardware acceleration with a NVIDIA GPU and without disabling SELinux isolation for the container.
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# Steps
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## 1. switch the required booleans
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For hardware acceleration we need to let containers access devices in /dev/dri. DRI means Direct Rendering Infrastructure, the devices there are all the GPUs.
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By default containers are not allowed access to these devices, to allow it we need to change a boolean:
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```bash
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sudo setsebool -P container_use_dri_devices 1
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```
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## 2. container creation
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I created a pod for the logic and then the jellyfin container inside it, with the systemd services it looks like this:
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#### pod
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```ini
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# pod-jellyfin.service
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[Unit]
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Description=Podman pod-jellyfin.service
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Documentation=man:podman-generate-systemd(1)
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Wants=network-online.target
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After=network-online.target
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RequiresMountsFor=/run/user/1000/containers
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Wants=container-jellyfin-app.service
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Before=container-jellyfin-app.service
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[Service]
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Environment=PODMAN_SYSTEMD_UNIT=%n
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Restart=on-failure
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TimeoutStopSec=70
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ExecStartPre=/usr/bin/podman pod create \
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--infra-conmon-pidfile %t/pod-jellyfin.pid \
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--pod-id-file %t/pod-jellyfin.pod-id \
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--exit-policy=stop \
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--name jellyfin \
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-p 8096:8096/tcp \
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--replace
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ExecStart=/usr/bin/podman pod start \
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--pod-id-file %t/pod-jellyfin.pod-id
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ExecStop=/usr/bin/podman pod stop \
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--ignore \
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--pod-id-file %t/pod-jellyfin.pod-id \
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-t 10
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ExecStopPost=/usr/bin/podman pod rm \
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--ignore \
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-f \
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--pod-id-file %t/pod-jellyfin.pod-id
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PIDFile=%t/pod-jellyfin.pid
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Type=forking
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[Install]
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WantedBy=default.target
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```
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The port `8096` inside the container is used for the web interface (http).
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#### container
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```ini
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# container-jellyfin-app.service
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[Unit]
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Description=Podman container-jellyfin-app.service
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Documentation=man:podman-generate-systemd(1)
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Wants=network-online.target
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After=network-online.target
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RequiresMountsFor=%t/containers
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BindsTo=pod-jellyfin.service
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After=pod-jellyfin.service
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[Service]
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Environment=PODMAN_SYSTEMD_UNIT=%n
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Restart=on-failure
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TimeoutStopSec=70
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ExecStart=/usr/bin/podman run \
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--cidfile=%t/%n.ctr-id \
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--cgroups=no-conmon \
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--rm \
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--pod-id-file %t/pod-jellyfin.pod-id \
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--sdnotify=conmon \
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--replace \
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-d \
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--name=jellyfin-app \
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--device nvidia.com/gpu=0 \
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-v /home/data/podman/jellyfin/cache:/cache:Z \
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-v /home/data/podman/jellyfin/config:/config:Z \
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-v /home/data/movies/:/media/movies:ro,z \
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-v /home/data/music:/media/music:ro,z \
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--label io.containers.autoupdate=registry docker.io/jellyfin/jellyfin:latest
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ExecStop=/usr/bin/podman stop \
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--ignore -t 10 \
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--cidfile=%t/%n.ctr-id
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ExecStopPost=/usr/bin/podman rm \
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-f \
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--ignore -t 10 \
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--cidfile=%t/%n.ctr-id
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Type=notify
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NotifyAccess=all
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[Install]
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WantedBy=default.target
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```
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Of course you'll want to adapt these volumes with your own media directories:
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```ini
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-v /home/data/movies/:/media/movies:ro,z \
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-v /home/data/music:/media/music:ro,z \
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```
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Then enable and start the container:
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```bash
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systemctl --user daemon-reload
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systemctl --user enable --now pod-jellyfin.service
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```
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### note:
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for hardware acceleration I used `--device nvidia.com/gpu=0` as I have a NVIDIA GPU
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To check that it's working you can run:
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```bash
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podman exec -it jellyfin-app nvidia-smi
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```
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I should output something like this:
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```bash
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Fri Dec 26 13:48:07 2025
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+-----------------------------------------------------------------------------------------+
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| NVIDIA-SMI 580.95.05 Driver Version: 580.95.05 CUDA Version: 13.0 |
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+-----------------------------------------+------------------------+----------------------+
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| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
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| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
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| | | MIG M. |
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|=========================================+========================+======================|
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| 0 NVIDIA GeForce GTX 1050 Off | 00000000:01:00.0 On | N/A |
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| 45% 28C P8 N/A / 75W | 11MiB / 2048MiB | 0% Default |
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| | | N/A |
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+-----------------------------------------+------------------------+----------------------+
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+-----------------------------------------------------------------------------------------+
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| Processes: |
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| GPU GI CI PID Type Process name GPU Memory |
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| ID ID Usage |
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|=========================================================================================|
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| No running processes found |
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+-----------------------------------------------------------------------------------------+
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```
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If instead you get:
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```
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Failed to initialize NVML: Insufficient Permissions
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```
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You'll need to install the container toolkit and generate a CDI specification file:
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```bash
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sudo dnf install cuda-toolkit nvidia-container-toolkit-base
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sudo nvidia-ctk cdi generate --output=/etc/cdi/nvidia.yaml
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```
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If this still doesn't work you might need to also turn another boolean on:
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```bash
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sudo setsebool -P container_use_xserver_devices 1
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```
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### note 2:
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Jellyfin documentation recommends using `--device /dev/dri/:/dev/dri/` in their podman example but this doesn't seem to work, at least not for NVIDIA GPUs. and the method described in the hardware acceleration part of the [wiki](https://jellyfin.org/docs/general/post-install/transcoding/hardware-acceleration/) is also different.
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## 3. enabling hardware acceleration in jellyfin
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In jellyfin web interface after setting up the server, you'll want to go to your dashboard (click on the hamburger menu in the top left and then dashboard), then playback, then transcoding and from there enable hardware acceleration, with a NVIDIA GPU you should choose `Nvidia NVENC`.
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