SSD Endurance for Edge AI: TBW, Wear, and What to Buy

Last updated: February 2026

TL;DR

Edge AI nodes that record video, write inference logs, or buffer sensor data continuously will exhaust a consumer-grade NVMe SSD in months, not years. The key metrics are TBW (terabytes written), DWPD (drive writes per day), and write amplification. For 24/7 deployments, use SSD classes with at least 1 DWPD and design your write pipeline to minimize unnecessary writes. This guide explains the math, the failure modes, and how to select the right drive class for your workload.

Why SSD Endurance Matters at the Edge

In a laptop or workstation, an SSD is rarely the component that limits system lifespan. Write workloads are bursty and light — documents, software installs, browser caches. A consumer NVMe drive rated for 300 TBW will last a decade under typical desktop use.

Edge AI nodes are different. A node recording 1080p H.264 video at 4 Mbps from a single camera writes approximately 43 GB per day. With four cameras, that is 172 GB per day. Add inference log writing, model output storage, telemetry, and OS operations, and a high-write edge node can easily sustain 200–400 GB of writes per day. At 300 GB/day, a 300 TBW consumer SSD reaches its rated endurance limit in roughly three years — and that is before accounting for write amplification, which makes real NAND wear worse than the raw numbers suggest.

SSD failure at an unattended edge site is expensive: a service visit, lost footage, and potential liability. Sizing storage endurance correctly at design time costs nothing. Replacing drives in the field costs real money and operational disruption.

TBW and DWPD Explained

TBW (Terabytes Written) is the total amount of data a manufacturer guarantees can be written to the drive over its rated lifetime. A 1 TB NVMe SSD rated for 600 TBW can sustain 600 TB of host writes before the warranty no longer covers wear-related failures. Drives can and often do exceed their TBW rating before failing, but planning around the rated value is the correct engineering approach.

DWPD (Drive Writes Per Day) normalizes TBW to a daily write rate over a warranty period (typically 3 or 5 years). A 1 TB drive with 600 TBW rated for a 5-year warranty has a DWPD of: 

600 TBW ÷ (1 TB × 365 days × 5 years) = 0.33 DWPD

This means the drive is warranted for writing 33% of its capacity each day over five years. At 1 DWPD, you could write the entire drive capacity once per day. Enterprise SSDs are commonly rated at 1–3 DWPD; datacenter-grade drives reach 10+ DWPD.

For edge AI deployments, the target is typically 0.5–1 DWPD for moderate write workloads and 1–3 DWPD for high-write nodes (continuous video recording, high-frequency sensor logging).

Write Amplification

NAND flash cannot overwrite data in place. Before writing new data to a page, the SSD controller must erase an entire block (which contains many pages), copy the valid pages to a new location, then write the new data. This means that for every byte the host writes, the SSD may actually write significantly more bytes to the NAND — a ratio called write amplification factor (WAF).

A WAF of 1.0 is ideal (rare in practice). Real-world WAF for mixed workloads on consumer SSDs is typically 1.5–3x. For write-heavy, random-access workloads (frequent small writes like log files and metadata), WAF can reach 5–10x. This means a drive writing 300 GB/day of host data may actually wear the NAND at the rate of 600–900 GB/day of physical writes.

WAF is reduced by: sequential writes (video streaming to disk is naturally sequential), larger write block sizes aligned to NAND page boundaries, and leaving over-provisioned space on the drive (keeping 10–20% of capacity unpartitioned gives the controller room to manage blocks efficiently).

Video and Sensor Write Workloads

Video recording is the most predictable write workload in edge AI. Bitrate is known in advance, and writes are naturally sequential — which keeps WAF low (typically 1.1–1.3x for pure sequential video). The write rate calculation is straightforward: 

Daily write volume (GB) = Bitrate (Mbps) × 3600 × 24 ÷ 8 ÷ 1000 × Camera count

At 4 Mbps per camera:

Inference metadata and telemetry writes are typically small in volume but may involve frequent small writes that increase WAF. Design your logging pipeline to batch writes where possible — write log records in chunks rather than one entry at a time to keep write operations aligned and sequential.

Sensor data (LiDAR point clouds, accelerometer streams, environmental sensors) varies widely. Profile the actual write rate of your sensor pipeline in development before committing to a storage specification.

Ring Buffers and Write Reduction

A ring buffer (circular buffer) is a fixed-size storage region where new data overwrites the oldest data once the buffer is full. For edge AI nodes that do not need to retain all captured footage indefinitely, ring buffers are the standard design pattern for managing storage endurance.

Instead of writing video indefinitely until the drive is full, partition a fixed region for video storage and let the recording software overwrite the oldest segments first. A 500 GB ring buffer on a 1 TB drive retains approximately 1–3 days of footage at typical bitrates while leaving the drive at 50% utilization, which provides free space for the FTL (flash translation layer) to operate efficiently and reduces WAF.

Event-triggered recording (writing only when a detection event occurs) can dramatically reduce total daily writes on nodes with low event frequency. On a node that triggers recording for 2 hours per day instead of 24, write volume drops by 92%.

For more on deployment design patterns, see the Edge AI Stack homepage or the blog index for related guides.

Sizing Examples

Example 1: 4-camera retail node, motion-triggered recording

Example 2: 8-camera warehouse node, continuous recording

SSD Class Comparison

SSD Class Typical DWPD TBW (1 TB example) NAND Type Relative Cost Edge AI Suitability
Consumer NVMe (QLC) 0.1–0.2 150–300 TBW QLC Low Light workloads only; avoid for continuous video write
Consumer NVMe (TLC) 0.2–0.4 300–600 TBW TLC Low–Medium Suitable for triggered recording or low-frequency logging
Prosumer / Creator NVMe (TLC) 0.5–1.0 600–1200 TBW TLC Medium Good fit for most edge AI nodes with moderate write loads
Enterprise NVMe (TLC) 1–3 1400–3000 TBW TLC High Continuous multi-camera recording; high-frequency sensor logging
Datacenter NVMe (3D TLC / SLC cache) 3–10 3000+ TBW TLC / SLC Very High Only justified for extreme write workloads; rare in edge deployments
Industrial MLC/SLC eMMC Variable Variable MLC/SLC Medium Suitable for OS drive; not ideal as primary video storage

What to Look for When Buying

Common Pitfalls

FAQ

What SMART attributes should I monitor on edge AI SSDs?

For NVMe drives, monitor Percentage Used (attribute 5), Available Spare (attribute 3), and Media and Data Integrity Errors. For SATA drives, watch Reallocated Sector Count, Wear Leveling Count, and Power-Off Retract Count. Set alerts at 80% usage consumed.

Is eMMC storage suitable for edge AI nodes?

eMMC is acceptable for the OS partition on low-write nodes. For nodes with significant log or video write loads, an NVMe SSD via M.2 is strongly preferred. eMMC lacks the endurance and write performance needed for sustained AI workloads.

How does over-provisioning help SSD endurance?

Over-provisioning reserves flash capacity outside the visible LBA space, giving the FTL extra blocks for wear leveling and garbage collection. This reduces WAF and extends drive life. Leaving 10–20% of capacity unpartitioned achieves a similar effect without requiring drive-specific configuration tools.

Should I use RAID across two SSDs for reliability at the edge?

Software RAID (RAID 1) adds write overhead and complexity. For edge nodes, a better approach is reliable drives with SMART monitoring and automated replacement scheduling. RAID is more appropriate at server-class edge nodes with many drives and dedicated RAID controllers.

Can I predict when an SSD will fail?

Not precisely, but SMART Percentage Used provides a reliable indicator of remaining rated endurance. Many drives continue operating beyond 100% TBW, but warranty coverage ends and the probability of failure increases. Plan replacement before drives reach the rated limit.

What is the difference between SLC, MLC, TLC, and QLC NAND?

These refer to how many bits are stored per NAND cell: SLC (1 bit), MLC (2 bits), TLC (3 bits), QLC (4 bits). More bits per cell increases density and lowers cost but reduces write endurance and adds latency. SLC lasts longest; QLC has the highest capacity at the lowest cost per GB but the worst endurance.

See the about page for how this site evaluates hardware, or browse the full blog index for related guides on edge AI infrastructure.