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
Edge AI hardware sizing, solved in one prompt.
Describe your cameras, models, and constraints in plain language. EdgeAIStack's engines return a recommended platform, resource utilization, bottleneck analysis, and a BOM your procurement team can use. Vendor-neutral.
Power user? Open the System Designer →
10 platforms indexed·
8 sizing engines·
2,721-vector knowledge base·
MCP + OpenAPI·
Methodology·
About
// Start here
MOST POPULAR
Which edge AI platform fits?
Answer five questions — task, cameras, resolution, power, environment — and get a
ranked hardware recommendation with alternatives and a confidence score in seconds.
The fastest path from requirements to a shortlist.
Open Hardware Selector →
FULL DEPLOYMENT
Design the whole system
Need the full picture? Platform selection, compute sizing, storage, network, power,
and cost in one run — then stress-test it: double the cameras, switch codecs, and
see which resource hits the wall first.
Open System Designer →
COST PLANNING
Estimate deployment cost
Compute + storage + power + network + enclosure → itemized BOM with
cost-per-camera breakdown. Share with procurement or export as JSON.
Estimate Cost →
// 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?
I need the lowest-cost setupSMB 4-Camera Budget Build
I have a retail storeRetail 8-Camera Edge AI
I have 16 cameras or mixed 4KSmart City 16-Camera Traffic AI
I need low-latency safety alertsWarehouse Safety AI
// 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.
- Live compute, memory, power, network, and storage gauges
- One-click stress scenarios — 2× cameras, worst case, H.265
- Baseline vs modified comparison
- Engine-validated with confidence scoring
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 | ~$249 |
| 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 | ~$599 |
| AGX Orin 64GB | High-density pipelines | 275 TOPS | 15–60W | ~40 streams | ~$1,599 |
| Jetson Orin Nano | Entry CUDA development | 40 TOPS | 7–15W | ~4 streams | ~$299 |
| 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 | ~$3,499 |
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 →
// Agent-Ready
Every sizing engine is callable by AI agents — an MCP server for Claude, Cursor, and Windsurf, plus an OpenAPI 3.1 spec for GPTs and custom agents. Explore the engines →
MCP
OpenAPI 3.1
RAG · 2,721 vectors
// Featured Guides
01
Orin Nano vs Orin NX — which should you buy?
Side-by-side comparison for real deployment workloads.
02
How much power does Jetson actually draw?
Idle, throttled, and full-load measurements across power modes.
03
PoE switch sizing for 8 AI cameras
Port allocation, power budget, and switch recommendations.
04
Best SSD for 24/7 edge recording
TBW ratings, endurance testing, and drive selection for continuous write.
05
Can you run edge AI fanless?
When passive cooling works, when it doesn't, and how to decide.
06
Storage layout for continuous recording
Ring buffers, retention sizing, and write patterns for edge deployments.
07
Jetson Thor vs AGX Orin for edge LLMs
Which flagship Jetson fits local LLM and VLM workloads in 2026.
08
The edge LLM runtime stack
Runtimes, quantization, and serving choices for LLMs on edge hardware.
09
Which VLM fits on your Jetson?
Vision-language model memory footprints matched to Jetson tiers.
Still researching?
The engines give you answers. The guides give you context — Jetson power modes, storage endurance, PoE budgeting, thermal constraints, and deployment checklists. Start with the hardware guide, browse all 35+ engineering guides, or start from a deployment-ready reference architecture.
// Frequently Asked Questions
What edge AI hardware do I need for 8 cameras?
For 8× 1080p cameras running object detection at around 15 FPS analyzed, a Jetson Orin NX 16GB-class module is the typical fit as of mid-2026, with roughly 130–150W of PoE switch budget and 2–5 TB of storage depending on retention. The retail 8-camera reference architecture walks through a complete sized build.
Jetson Orin Nano vs Orin NX — which should I buy?
The Orin Nano (approx. $249–299 as of mid-2026) handles roughly 4–8 light detection streams; the Orin NX 16GB (approx. $599) 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.
Can I run a VLM on a Jetson?
Yes — as of mid-2026, quantized vision-language models in the roughly 2–8B parameter class run on Orin NX and AGX Orin, while smaller Jetsons are generally limited to compact VLMs. Which VLM fits which Jetson tier is covered in our VLM sizing guide.
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.
Can I run edge AI hardware fanless?
Often, below roughly 10–15W sustained load — Orin Nano low power modes and accelerator cards like the Hailo-8 are fanless-viable as of mid-2026, while NX and AGX-class modules generally need active cooling. Our guide to when passive cooling works covers the decision criteria.