Edge AI Storage Sizing Guide 2026 — How to Calculate Exact NVMe Capacity Per Camera & Tier Your 存储 Right

Most edge AI storage估算 starts with a guess: "4 cameras, let's put in 2 TB and call it a day." Six months later, the drive is full, inference logs are being silently overwritten, and the compliance audit asks for footage you no longer have. Getting 存储 sizing right isn't about putting in the biggest SSD you can afford — it's about matching data rate, retention policy, and access pattern to the right tier of storage.

This guide gives you the exact math for per-camera data rates, a hot/warm/cold tiering strategy that minimizes $/TB, and pre-configured QSCompute storage kits for 1–32 camera edge nodes.

Step 1: Calculate Per-Camera Data Rate

The storage demand of a single camera depends on four variables: resolution, frame rate, codec, and whether you're storing raw frames or compressed video.

Formula: DailyData(GB) = Bitrate(Mbps) × 3600 × HoursPerDay ÷ 8000

Raw frame capture for AI training datasets is the elephant in the room — one camera generating 8.5 TB/day demands a fundamentally different storage architecture than 30 cameras at 1.8 GB/hr each.

Step 2: Define Retention Policy Per Tier

Not all data needs to live on fast NVMe. A three-tier strategy matches access frequency to media cost:

Moving data from hot → warm → cold can reduce your effective $/TB by 85%. A 30-camera factory AOI system storing everything on NVMe costs $8,100/month in drives; the same system with tiered storage costs $1,260/month — an 84% reduction.

Step 3: Node-Level Storage Sizing Matrix

Here's the exact storage capacity needed for four common edge AI deployment patterns, assuming standard inspection cameras (1080p, 30 fps, H.265 @ 8 Mbps) and a 30-day warm retention + 90-day cold archive:

Step 4: SSD Endurance Requirements

Continuous 24/7 writes from multiple cameras will wear out drives faster than you think. Match DWPD to your write load:

Even the most demanding multi-camera deployment stays well under 1 DWPD — any enterprise-grade industrial SSD with 1+ DWPD rating will comfortably handle 3+ years of continuous recording. The bottleneck is capacity, not endurance.

Cost Comparison: All-NVMe vs Tiered Storage

Adding a cold tier via a small NAS or external HDD enclosure cuts storage costs by ~75% while preserving 90-day retention for compliance and analytics. The key insight: you don't need NVMe latency for month-old footage. A SATA HDD at 200 MB/s sequential read can stream 4K video faster than real-time playback speed.

Implementation: Tiering on Jetson Orin and x86 Edge Servers

Jetson Orin (1–4 cameras): The Orin's M.2 slot plus USB 3.2 gives you two tiers. Use the M.2 NVMe for hot storage (7-day rolling buffer of inference frames) and an external USB SSD or NAS mount for warm/cold offload. QSCompute's QS-Store-Nano kit includes a 256 GB Samsung PM9D3a with a pre-configured logrotate and tiering cron job.

x86 Edge Server (8–32 cameras): With multiple PCIe Gen5 slots, you can run hot NVMe (OS + active data), warm QLC U.2 (recent archives), and cold HDDs (long-term retention) all in one 2U chassis. We recommend ZFS with separate vdevs per tier and automatic snapshot promotion/demotion via a tiering daemon.

Networked Cold Tier: For multi-node deployments, centralize cold storage on a small NAS (Synology DS923+ or QNAP TS-464) with 4× 20 TB HDDs in RAID 5, providing ~54 TB usable. Edge nodes write to the NAS via NFS/SMB on a dedicated VLAN — 2.5GbE per node is sufficient for offloading 86 GB/day/camera at line rate.