US 10x 8TB RAID-Z1 NAS Calculator

Estimate usable TB, parity, and fault tolerance for US 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%

US Buyer Context

US buyers often evaluate frequent pricing swings and promo cycles. Capacity planning should account for staged purchases instead of one-time all-disk upgrades.

• Use parity layouts that remain valid during phased expansions.
• Compare total usable-per-dollar across multiple disk sizes, not only one SKU.

Brand / Region Glossary

Cost-per-Usable-TB

Planning metric that compares real usable capacity after reserve and parity.

Promo Window

Seasonal pricing period used for phased drive purchasing strategies.

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

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.

Should I optimize this 10-drive plan for capacity 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.

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.

Why include a 10% reserve when planning NAS available space?

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