Real-Debrid Torrent Client Hardware Requirements: RAM, CPU, Storage & Download & Unpack
Plan Real-Debrid Torrent Client hardware for RAM, CPU, local downloads, unpacking, SQLite, and practical ZimaOS deployment recommendations.
Real-Debrid Torrent Client hardware requirements at a glance
Size Real-Debrid Torrent Client from verified upstream requirements first, then add headroom for the workload and persistent data.
- RAM
- No numerical official minimum published
- CPU
- No numerical official minimum published
- Storage
- Separate /data/downloads from /data/db
- Database
- SQLite
- Runtime / Port
- .NET 10; port 6500
- Best Zima starting point
- ZimaBoard 2 832
From official requirements to the right setup
Start with verified upstream facts for Real-Debrid Torrent Client, then size the workload rather than copying a container or app-store resource label.
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Official requirements
-
Confirm your needs
-
Leave room to grow
-
Run it on ZimaOS
Check every playback client
- Debrid provider
- Concurrent downloads
- Local download vs symlink mode
- Automatic unpacking
- Download path mapping
- SQLite DB path
- Sonarr/Radarr integration
- Retention/backups
Official minimum requirements
The current rdt-client project documents runtime, Docker, SQLite persistence, ports, and architectures but no numerical whole-host CPU or RAM minimum.
Do not infer a RAM/CPU floor from debrid-provider limits; local downloads, unpacking, storage mapping, and Arr integrations determine host sizing.
| Requirement | Official minimum | What this supports |
|---|---|---|
| No numerical official minimum published | Unpacking and concurrent local downloads drive CPU. | |
| No numerical official minimum published | Depends on local download/unpack and shared-host apps. | |
| Angular 21 + .NET 10 | Current repository facts. | |
| SQLite rdtclient.db | Persist database path. | |
| 6500 | Web UI/qBittorrent-compatible API. | |
| x86-64, arm64, armhf | Multi-arch image tags are documented. | |
| Not required | No GPU requirement. |
When to upgrade your hardware
Unpacking bottleneck
Storage growth
Automation-stack contention
Plan hardware growth with confidence
Fast download/extract storage
Use SSD for active downloads/unpacking.
SSD/NVMeMore shared-host RAM
Useful with Arr stack.
ZimaBoard 2 1664Large local download pool
Use multi-bay storage.
ZimaCube 2 StandardStronger extraction CPU
Useful for repeated archive extraction.
ZimaCube 2 ProCan it run on ZimaOS?
Install Real-Debrid Torrent Client from the ZimaOS App Store
A specific public rdtclient page is verified.
Open Real-Debrid Torrent Client in the ZimaOS App Store โUse current rdt-client project
Current repo documents .NET 10, port 6500, SQLite, providers, and API emulation.
Open rdt-client source โPreserve path mapping
Download paths must resolve consistently across connected apps.
Open rdt-client Docker guidance โZima hardware for Real-Debrid Torrent Client
RDT Client is not a conventional torrent engine: the debrid provider performs remote torrent work while the host handles API orchestration, optional local downloads, unpacking, and SQLite state.
Choose by workload
ZimaBoard 2 832 with durable DB/download paths is sufficient.
Storage throughput and CPU become primary.
Do not merge remote debrid processing with local host requirements.
| Zima hardware | Best for | Example workload | Core configuration | Recommended boundary | Next step |
|---|---|---|---|---|---|
| ZimaBoard 2 832 | Light self-hosted services and small download/transfer workloads | One light app or a small home-server stack |
|
8 GB RAM and a 4-core N150 can limit CPU-heavy extraction, AI, and large multi-service stacks | Get Now |
| ZimaBoard 2 1664 | Memory-heavier download automation and multi-container workloads | Apps benefiting from 16 GB RAM without needing a much faster CPU |
|
CPU throughput is unchanged from the 832 model | Get Now |
| ZimaCube 2 Standard | Storage-heavy downloads and media libraries | Large persistent data sets where drive bays matter most |
|
8 GB RAM can be less suitable than ZimaBoard 2 1664 for memory-heavy services | Get Now |
| ZimaCube 2 Pro | CPU-heavier extraction and higher-concurrency workloads | Stronger CPU plus expanded storage and networking |
|
16 GB RAM can still constrain large local AI workloads | Get Now |
What the Press Says
Highlights from trusted reviewers worldwide.
โZimaCube 2: Not just another NAS, tested with 25TB storage, local AI agents, 4K transcoding, and real homelab workflows.โRead full review
โThe ZimaBoard 2 is a compact x86 server board that can be turned into a mini NAS, home server, media box, or self-hosting hub.โRead full review
โZimaCube 2: A modern, high-performance NAS with plenty of room to growโbuilt for users who want more than basic storage.โRead full review
โCoverage focused on ZimaCube 2's open hardware foundation, no monthly fee, and self-hosting flexibility.โRead full review
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Stories and reviews from people who build with Zima every day.
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Maybe I am not digital natives but I live with PCs since 12 years old in 1984 when IBM PC clone come to my home. Many years have passed and many operating system I've tried. For me Zima blade and CasaOS was a quantum leap for home PC enthusiast and server lab machine to make me stay curious and relevant for this era.
Very good!!
I use ZimaCube Pro as 5th Proxmox cluster node. It runs several VMs and containers, including a VM with GPU passthrough to run a self-hosted LLM. A specific LXC container runs a Samba server for NAS capabilities using four of six RAID 6 SATA HDDs with ZFS.
Great innovation for mini server!
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Frequently asked questions
How much RAM does RDT Client need?
No numerical whole-host RAM minimum is published.
How many CPU cores does it need?
No numerical official CPU minimum is published.
Which runtime does the current project use?
Angular 21 and .NET 10.
Which database does it use?
SQLite rdtclient.db.
What port does it use?
6500.
Which Docker architectures are documented?
x86-64, arm64, and armhf.
Can ZimaBoard 2 832 run it?
Yes for moderate debrid management/local downloads.
When should I choose ZimaCube 2 Pro?
For frequent unpacking, high throughput, or a larger Arr stack.
