Best Hardware for Jellyfin Media Server

Q: Do I need a GPU for Jellyfin?

Not if all your clients direct-play. If any client needs transcoding (web browsers, mobile on cellular, older devices), you need either an Intel CPU with QuickSync or a dedicated NVIDIA GPU.

Q: Is Intel or NVIDIA better for Jellyfin?

Intel QuickSync is better for most home users — it's built-in, power-efficient, and handles 2-5 streams easily. NVIDIA is better for high-stream scenarios (8+ concurrent transcodes) but costs more and uses more power.

Q: Can I run Jellyfin on a Raspberry Pi?

Yes, for direct play only. The Pi 5 can handle 1-2 direct-play 4K streams over a wired connection. It cannot do hardware transcoding — any transcode request will software-encode on the ARM CPU, which is painfully slow for anything above 720p.

Q: How much storage do I need?

A typical 4K movie is 20-60GB. A 1080p movie is 5-15GB. A 100-movie library at mixed quality needs roughly 1-3TB. Plan for growth — media libraries only get bigger.

Q: Should I store media locally or on a NAS?

A NAS is cleaner — it separates compute from storage, and you can share the same media with multiple services. If budget is tight, start with a local USB drive and migrate to a NAS later.

Quick Recommendation

For most households, an Intel N100 or N305 mini PC is the best Jellyfin hardware. Intel QuickSync handles hardware transcoding efficiently, power consumption stays under 15W, and the total cost is $150-350. If you only direct-play media (no transcoding), even a Raspberry Pi 5 works.

What Jellyfin Needs from Your Hardware

Jellyfin’s hardware requirements depend entirely on how your clients play media:

Direct play (client supports the video codec natively): Near-zero CPU usage. Almost any hardware works.
Transcoding (converting video format in real-time): Significant CPU/GPU usage. Hardware transcoding support is critical.

Most users need transcoding at least sometimes — when streaming to a phone on cellular, using a web browser that doesn’t support HEVC, or reducing quality for a slow connection.

Transcoding: CPU vs GPU

Software transcoding (CPU only) is slow, power-hungry, and limits you to 1-2 streams on consumer hardware. Avoid this.

Hardware transcoding (GPU-accelerated) is what you want. Three options:

Technology	GPUs	Performance	Jellyfin Support
Intel QuickSync	Intel iGPUs (6th gen+)	Excellent efficiency	Full (recommended)
NVIDIA NVENC	GeForce/Quadro GPUs	Highest throughput	Full
AMD AMF/VCN	Radeon iGPUs/dGPUs	Good (improving)	Partial (Linux drivers maturing)

Intel QuickSync is the default recommendation for Jellyfin. It’s built into every Intel CPU with integrated graphics, uses minimal power, and Jellyfin’s support is mature. NVIDIA NVENC is better for high-stream-count scenarios but costs more and draws more power.

Hardware Tiers

Tier 1: Direct Play Only — Under $100

If every client in your house can direct-play your media (modern smart TVs, Apple TV 4K, NVIDIA Shield), you barely need any compute power.

Recommended: Raspberry Pi 5 (8GB, ~$80) or any leftover PC.

Spec	Minimum
CPU	Any (quad-core ARM or x86)
RAM	2GB+
Storage	External USB HDD for media
Network	Gigabit Ethernet (1GbE)
Transcoding streams	0 (direct play only)

Tier 2: Light Transcoding (1-2 streams) — $150-250

For a household where 1-2 people might transcode simultaneously.

Recommended: Intel N100 mini PC (~$150-200)

Spec	Recommended
CPU	Intel N100 (4C/4T, QuickSync)
RAM	8-16GB
Storage	256GB NVMe (OS) + external/NAS for media
Network	2.5GbE preferred
Transcoding streams	1-2 simultaneous 4K, 3-4 simultaneous 1080p

The N100’s 24 GPU execution units handle QuickSync transcoding efficiently. At 6W TDP, it costs under $10/year in electricity.

Tier 3: Multi-Stream Transcoding (3-5 streams) — $250-400

For larger households or remote access with multiple concurrent viewers.

Recommended: Intel N305 mini PC (~$300-400)

Spec	Recommended
CPU	Intel N305 (8C/8T, 32 EU QuickSync)
RAM	16-32GB
Storage	512GB NVMe (OS) + NAS for media
Network	2.5GbE (dual preferred)
Transcoding streams	2-3 simultaneous 4K, 4-6 simultaneous 1080p

The N305’s 32 execution units and 8 cores give meaningful headroom over the N100 for concurrent transcoding.

Tier 4: Heavy Transcoding (5+ streams) — $400-800

For sharing your library with extended family/friends or running a large multi-user setup.

Recommended: Intel 12th/13th Gen i5 system or add a dedicated GPU

Spec	Recommended
CPU	Intel i5-12400 / i5-13500 (QuickSync)
GPU	Or NVIDIA Quadro P2000 / T400 (~$100 used)
RAM	16-32GB
Storage	NVMe OS + NAS for media
Network	2.5GbE or 10GbE
Transcoding streams	8-15+ simultaneous with NVENC

If you need 10+ streams, an NVIDIA GPU (even a used Quadro P2000 for ~$60-80) outperforms any iGPU. The P2000 handles 15-20 simultaneous 1080p transcodes. Consumer GeForce cards work too but are limited to 3 NVENC sessions unless you apply the driver patch.

Transcoding Performance by Hardware

Hardware	4K HDR → 1080p	1080p → 720p	Max Concurrent	Power Draw
Raspberry Pi 5	Not possible	Software only (1 max)	1	5-8W
Intel N100	1-2 streams	3-4 streams	~4 total	6-15W
Intel N305	2-3 streams	4-6 streams	~6 total	10-25W
Intel i5-12400	4-6 streams	8-10 streams	~12 total	30-65W
NVIDIA Quadro P2000	3-5 streams	15-20 streams	~20 total	75W GPU
NVIDIA T400	2-4 streams	8-12 streams	~15 total	30W GPU

HDR Tone Mapping

4K HDR content needs tone mapping to look correct when transcoded to SDR (for clients that don’t support HDR). Without it, colors look washed out.

Jellyfin supports hardware tone mapping on:

Intel QuickSync (10th gen+): Full support via VPP. This is the easiest path.
NVIDIA NVENC: Supported via OpenCL/CUDA filters.
AMD: Limited support, improving with recent drivers.

For the best HDR tone mapping experience, use Intel 10th gen or newer. The N100 and N305 both support this.

Storage Recommendations

OS Drive

256-512GB NVMe SSD. Jellyfin’s metadata and database live here. An SSD makes library scans and UI navigation snappy.

Media Storage

Library Size	Recommended Storage
Under 2TB	USB 3.0 external HDD
2-8TB	Internal SATA HDD (if your case has a bay)
8-20TB	DAS enclosure or 2-bay NAS
20TB+	4+ bay NAS or DIY NAS

Use NAS-rated drives (WD Red Plus, Seagate IronWolf) for 24/7 operation. Desktop drives work but have shorter lifespans under continuous use.

Network Storage (NAS)

Jellyfin can read media from NFS or SMB network shares. This is the cleanest setup: keep your Jellyfin compute node separate from your storage. Mount the NAS share and point Jellyfin at it.

# Docker Compose with NFS media volume
services:
  jellyfin:
    image: jellyfin/jellyfin:10.10.6
    volumes:
      - jellyfin-config:/config
      - type: volume
        source: media
        target: /media
        volume:
          nocopy: true
    devices:
      - /dev/dri:/dev/dri  # Intel QuickSync
    restart: unless-stopped

volumes:
  jellyfin-config:
  media:
    driver: local
    driver_opts:
      type: nfs
      o: addr=192.168.1.100,nfsvers=4,rw
      device: ":/volume1/media"

RAM Requirements

Jellyfin itself uses 200-500MB of RAM. The rest depends on your library and concurrent users:

Scenario	RAM Needed
Small library (<1,000 items), 1-2 users	4GB total system
Medium library (1,000-10,000 items), 2-4 users	8GB total system
Large library (10,000+ items), 5+ users	16GB total system
Running alongside other containers	16-32GB total system

Networking

Streaming 4K requires roughly 80-120 Mbps per stream (original bitrate). For multiple concurrent streams:

1GbE (125 MB/s): Fine for 1-2 4K streams or 5-8 1080p streams
2.5GbE (312 MB/s): Comfortable for 3-5 4K streams
10GbE: Overkill for most home setups, useful for NAS-to-server connections with large libraries

If your media lives on a NAS, the NAS-to-Jellyfin link is the bottleneck. Use at least 2.5GbE between the NAS and the Jellyfin server.

Recommended Builds

Budget Build (~$200)

Component	Choice	Price
Computer	Intel N100 mini PC (16GB/512GB)	~$180
Media storage	Existing USB HDD or NAS	$0+
Total		~$180

Handles a household of 2-3 with occasional transcoding. Silent, tiny, draws 8-10W.

Mid-Range Build (~$400)

Component	Choice	Price
Computer	Intel N305 mini PC (16GB/500GB)	~$350
Media storage	4TB WD Red Plus (internal SATA bay)	~$90
Total		~$440

Handles 3-5 concurrent streams, runs 10+ other containers alongside Jellyfin.

Power User Build (~$600-800)

Component	Choice	Price
Computer	Used Dell OptiPlex Micro (i5-12500T)	~$200-300
GPU	NVIDIA Quadro P2000 (used) or T400	~$60-120
RAM	32GB DDR4	~$60
Media storage	Synology DS923+ or NFS share	~$600+
Total		~$600-1,000

Handles 10-15+ concurrent transcodes. Dedicated GPU removes any transcoding ceiling. Pair with a NAS for storage.

Enabling Hardware Transcoding in Jellyfin

Intel QuickSync (Docker)

Pass through the GPU device and ensure the container user has access:

# Check that your Intel GPU is detected
ls -la /dev/dri
# Should show renderD128

# Add the Jellyfin user to the render group
# In Docker, set JELLYFIN_PublishedServerUrl and device passthrough

# docker-compose.yml snippet
services:
  jellyfin:
    image: jellyfin/jellyfin:10.10.6
    devices:
      - /dev/dri:/dev/dri
    environment:
      - JELLYFIN_PublishedServerUrl=http://your-server-ip
    restart: unless-stopped

In Jellyfin’s Dashboard → Playback → Transcoding, select Intel QuickSync (QSV) and enable hardware decoding for all supported codecs.

NVIDIA (Docker)

Install the NVIDIA Container Toolkit, then:

services:
  jellyfin:
    image: jellyfin/jellyfin:10.10.6
    runtime: nvidia
    environment:
      - NVIDIA_VISIBLE_DEVICES=all
    restart: unless-stopped

Select NVIDIA NVENC in Jellyfin’s transcoding settings.

FAQ

Do I need a GPU for Jellyfin?

Not if all your clients direct-play. If any client needs transcoding (web browsers, mobile on cellular, older devices), you need either an Intel CPU with QuickSync or a dedicated NVIDIA GPU.

Is Intel or NVIDIA better for Jellyfin?

Intel QuickSync is better for most home users — it’s built-in, power-efficient, and handles 2-5 streams easily. NVIDIA is better for high-stream scenarios (8+ concurrent transcodes) but costs more and uses more power.

Can I run Jellyfin on a Raspberry Pi?