APAC 10x 8TB RAID-Z1 NAS Calculator

Estimate usable TB, parity, and fault tolerance for APAC homelab buyers using 10x 8TB in RAID-Z1.

Capacity Snapshot

Raw Capacity

80.00 TB

Usable Capacity

64.80 TB

Fault Tolerance

1 drive

Efficiency

90.0%

ZFS single-parity equivalent of RAID 5; common for smaller homelab pools. This scenario applies a 10% filesystem reserve.

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Alternative Mode Comparison

Mode	Usable	Tolerance	Efficiency
RAID 5	64.80 TB	1 drive	90.0%
RAID 6	57.60 TB	2 drives	80.0%
RAID 10	36.00 TB	1 drive per mirror pair*	50.0%
RAID-Z1	64.80 TB	1 drive	90.0%
RAID-Z2	57.60 TB	2 drives	80.0%

APAC Buyer Context

APAC builders frequently compare availability windows and procurement variance, which makes robust fallback capacity and staged scaling useful in practice.

• Plan a spare strategy alongside RAID level selection.
• Validate that chosen parity can tolerate delayed disk replacement windows.

Brand / Region Glossary

Procurement Variance

Regional differences in disk availability and replacement lead times.

Spare Strategy

Policy of keeping spare disks available to reduce recovery delay risk.

NAS Cluster Guides

NAS Hub RAID Level Compare RAID Capacity Matrix Planning Quick Sheet RAID Selector NAS FAQ

Related Long-Tail Calculators

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Sequential Long-Tail Navigation

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FAQ

How many disk failures can RAID-Z1 tolerate in this setup?

This setup can tolerate 1 drive. Real-world survivability depends on mirror placement, rebuild stress, and drive health.

Should I optimize this 10-drive plan for storage headroom or resiliency first?

For long-lived NAS pools, resiliency first is usually safer. Capacity can be expanded later, while a risky parity choice can force migration sooner.

Is RAID-Z1 still practical with 8TB drives?

It can be practical, but larger drives increase rebuild windows. Validate parity choice and backup policy before committing to the final layout.

Why include a 10% reserve when planning NAS capacity?

Keeping free space improves filesystem behavior for snapshots, metadata, and write performance. Full arrays often perform worse and rebuild more slowly.