Retail 8-Camera Edge AI
Balanced retail analytics architecture using Jetson Orin NX for 8x1080p cameras, detection, tracking, local storage, and metadata-to-cloud workflows.
// Edge AI Decision Platform
Choose and size the right hardware for your edge AI deployment
Compare Jetson, Hailo, Coral and RK3588 using your cameras, AI models, latency, power and budget. Then size memory, storage, networking, power and total deployment cost — vendor-neutral, with every assumption shown.
Try a sized example:
4-camera NVR
8-camera retail
Jetson robotics
Low-power detection
Factory inspection
Traffic analytics
// Where do you want to start?
Choose hardware
I know my workload — pick the platform
Rank Jetson, Hailo, Coral and RK3588 against your cameras, model, power and environment.
Open Hardware Selector →
Design the full system
Size the whole deployment, end to end
Platform, compute, memory, storage, network, power and cost in one run — with a bottleneck stress test.
Open System Designer →
Calculate one constraint
I just need one number
Start from an architecture
Copy a proven build
Four reference deployments (4–16 cameras) with recommended hardware and the limiting constraint called out.
Browse reference architectures →
// Popular calculators
01 · most used
Hardware Selector
Rank the best-fit edge AI platform from your task, cameras, resolution, power and environment — with alternatives and a confidence label.
02
Module Power Calculator
Estimate module TDP and thermal headroom across Jetson power modes before you commit to an enclosure or cooling plan.
03
System Designer
Compute, memory, storage, network, power and cost sized together — then stress-tested to find what breaks first.
Design the full system →
See it applied: Retail 8-camera build
04
Power Budget
Size the PoE switch and total power draw for a multi-camera deployment, cameras and compute combined.
05
Storage Endurance
Match drive TBW to your write rate and retention window so the SSD survives 24/7 recording.
06
Memory Estimator
Check whether your model and pipeline fit in VRAM and system RAM on a given module before you buy.
// Jetson planning questions
The specific Jetson sizing questions engineers search for most — each answered with measured numbers, not marketing.
How much power does a Jetson Orin Nano draw?→
Power modes: 5W vs 7W vs 15W→
MAXN vs MAXN Super — what changes?→
Orin Nano vs Orin NX — which to buy?→
Orin Nano vs Orin Nano Super→
Best SSD for a Jetson Orin Nano→
How to install NVMe on Orin Nano→
YOLOv8 RAM requirements on Jetson→
Orin Nano thermal limits when fanless→
// Reference Architectures
Start with a proven edge AI deployment
Reference architectures for real-world camera AI systems — with recommended hardware, bottlenecks, power, storage, bandwidth, and direct links back to EdgeAIStack calculators.
SMB 4-Camera Budget Build
Low-cost small business architecture using Jetson Orin Nano Super for basic detection, people counting, event alerts, and short retention windows.
Smart City 16-Camera Traffic AI
High-density traffic analytics architecture using Jetson AGX Orin for intersections, vehicle detection, pedestrian zones, storage, and thermal planning.
Warehouse Safety AI
Industrial forklift and pedestrian monitoring architecture using Jetson Orin NX with local alerts, event clips, zone rules, and safety dashboard integration.
Not sure where to start?
// Bottleneck Workbench — live preview
INCLUDED IN SYSTEM DESIGNER
What breaks first when you
double the cameras?
After every recommendation, push the limits. Double cameras, switch codecs, change models — see compute, memory, power, and storage shift in real time. Find the bottleneck before you buy the hardware.
- Add cameras — watch compute and decode headroom shrink
- Switch H.264 → H.265 — see bandwidth and storage move
- Add a second model — find the memory ceiling
- Raise retention — resize storage before you buy the drive
BOTTLENECK WORKBENCH
// Platform Catalog — 10 embedded platforms indexed
| Platform | Best fit | Compute | Power | Est. planning capacity | Indicative price |
|---|---|---|---|---|---|
| Hailo-8L | Lowest-power detection | 13 TOPS | 1–1.5W | ~4 streams | ~$70 |
| Orin Nano Super | Flexible entry Jetson | 67 TOPS | 10–25W | ~8 streams | ~$399 |
| Hailo-8 | Efficient dedicated inference | 26 TOPS | 2.5–8W | ~8 streams | ~$200 |
| Orin NX 16GB | Multi-camera production | 157 TOPS | 10–40W | ~18 streams | ~$999 |
| AGX Orin 64GB | High-density pipelines | 275 TOPS | 15–60W | ~40 streams | ~$2,999 |
| Jetson Orin Nano | Entry CUDA development | 40 TOPS | 7–15W | ~4 streams | ~$399 |
| Hailo-10H | Higher-capacity M.2 accelerator | 20 TOPS | 1–5W | ~10 streams | ~$130 |
| Rockchip RK3588 | Budget Linux SBC | 6 TOPS | 5–20W | ~6 streams | ~$149 |
| Google Coral Edge TPU | Ultra-low-power classification | 4 TOPS | 2–2.5W | ~3 streams | ~$60 |
| Jetson Thor T5000 | Generative AI & very high density | 2,070 TOPS | 40–130W | ~60 streams | ~$4,999 |
| Jetson T4000 | Thor generation, lower envelope | ~1200 FP4 TFLOPs | 40–70W | — | ~$2,999 |
Compute (TOPS) and power draw are manufacturer specifications. Estimated planning capacity is an EdgeAIStack estimate, not a manufacturer figure — approximate concurrent 1080p streams for a YOLOv8-class object-detection model at a ~15 FPS target. Actual capacity varies with model, resolution, codec, and pipeline; treat these as planning starting points, not guarantees. Prices are indicative street pricing, not MSRP. Run the Hardware Selector for a sized recommendation against your workload.
Specs indicative · mid-2026
Not sure which platform fits?
Answer five questions — task, cameras, resolution, power, and environment — and get a ranked hardware recommendation with alternatives and a confidence score in seconds.
Open Hardware Selector →// Power, storage & thermal planning
Power & PoE
Storage & SSD
// API, MCP & OpenAPI
Every sizing engine is callable from code and AI agents — an OpenAPI 3.1 spec for GPTs and custom agents, plus an MCP server for Claude, Cursor and Windsurf. Read the API & MCP docs →
MCP
OpenAPI 3.1
// Compare before buying
VS
Jetson Orin Nano vs Orin NX
Stream capacity, memory headroom and price — which to buy for your workload.
VS
Jetson Orin Nano vs Hailo-8
CUDA flexibility vs per-watt accelerator efficiency for detection.
VS
Jetson vs Google Coral TPU
When an ultra-low-power TPU beats a general-purpose Jetson.
KITS
Best edge AI kits 2026
Ready-to-buy dev kits and bundles, sorted by budget and use case.
GUIDE
Edge AI hardware guide
The full decision framework across platforms, power, storage and cost.
SPECS
Specifications reference
TOPS, power, memory and interfaces for every indexed platform, side by side.
More comparisons: Nano vs Nano Super Thor vs AGX Orin Best for Frigate NVR NVMe vs SD card RAM sizing Thermal design
// Frequently Asked Questions
What edge AI hardware do I need for 4, 8, or 16 cameras?
As a mid-2026 planning guide: 4× 1080p cameras running object detection at ~15 FPS analyzed suit a Jetson Orin Nano Super or Hailo-8 class device; 8 cameras typically want a Jetson Orin NX 16GB; and 16 cameras or mixed 4K point to an AGX Orin. Run the Hardware Selector for a ranked shortlist against your exact workload, or start from the retail 8-camera reference architecture.
Jetson or Hailo — which should I choose?
Choose a Jetson when you need CUDA flexibility, multi-model pipelines, or larger models; choose a Hailo-8 class accelerator when power and per-watt efficiency dominate and the workload is mostly detection. As of mid-2026 a Hailo-8 draws roughly 2.5–8W versus 7–25W for entry Jetsons. Our Jetson Orin Nano vs Hailo-8 comparison covers the trade-offs.
Jetson Orin Nano vs Orin NX — which should I buy?
The Orin Nano (approx. $399 as of mid-2026, after the July 2026 repricing) handles roughly 4–8 light detection streams; the Orin NX 16GB (approx. $999) roughly doubles practical stream capacity and adds memory headroom for multi-model pipelines. Our Orin Nano vs Orin NX comparison covers the decision in detail.
How much power does a Jetson Orin deployment draw?
The module itself ranges from roughly 7W (Orin Nano low mode) to 60W (AGX Orin MAXN) as of mid-2026 — but cameras usually dominate the budget: 8 PoE cameras add roughly 100W. Our Jetson power comparison has measured figures across the family.
How much storage do I need for 24/7 recording?
Storage scales with bitrate, camera count and retention. As a mid-2026 rule of thumb, one 1080p H.265 stream recording continuously is roughly 5–15 GB per day, so 8 cameras at 30-day retention land in the 2–5 TB range. Size it precisely — and confirm the drive survives the write load — with the Storage Endurance calculator and our best-SSD guide.
What hardware runs Frigate NVR well?
Frigate offloads detection to an accelerator, so a Google Coral TPU or a Jetson handles several 1080p cameras comfortably; camera count, resolution and the number of detection zones drive the requirement. Our best hardware for Frigate guide maps common setups to hardware, or run the Hardware Selector with your camera count.
What assumptions do these estimates make?
Defaults as of mid-2026: 1080p H.264/H.265, ~15 FPS analyzed, INT8 precision, continuous recording and a YOLOv8-class detector unless you change them. Manufacturer figures (TOPS, power, price) are kept separate from EdgeAIStack planning estimates such as stream capacity, and every engine shows the assumptions it used. See the methodology for how the numbers are derived.
Describe your deployment. Get a defensible hardware plan.
Answer a few questions and get a ranked platform recommendation with sized power, storage, network and cost — every assumption shown. Prefer to read first? Start with the hardware guide or browse reference architectures.