Edge AI Hardware Built for Local Intelligence
From AI cameras and embedded modules to compact appliances and multi-accelerator servers — the hardware that runs AI inference on-site, sized to your workload, environment and budget. Not tied to a single chip, board or model vendor.
A Platform Approach, Not a Product List
Every deployment starts with the workload. The hardware is then selected around it — accelerated, networked and packaged for where it will actually run.
GPU Edge AI Server
Demanding vision models, high camera counts and multi-model pipelines.
View platformEdge AI Hardware Categories, Explained
"Edge AI hardware" covers everything from a camera with a built-in NPU to a rack server in a site server room. Each class trades compute against power, size and cost — this is how they compare.
| Category | What it is | Typically used for | Watch out for |
|---|---|---|---|
| AI cameras | IP cameras with an on-board NPU or vision processor that run a model on the camera itself. | A few fixed analytics per camera — line crossing, intrusion, counting. | Models are limited to what the camera vendor supports; hard to change later. |
| Embedded AI modules & gateways | Small boards or boxes built around an NPU SoC or an embedded GPU module (Jetson-class), often fanless. | A handful of camera streams or sensor feeds close to the machine or entrance. | Limited memory; check the vendor's model and runtime support before choosing. |
| M.2 / PCIe accelerators | Add-in NPU or AI accelerator cards for an existing PC, industrial PC or server. | Adding inference to hardware you already run. | Driver and OS support; slot, power and cooling in the host machine. |
| Compact edge appliances | A small, quiet, purpose-built inference box for a back-office, cabinet or small rack. | One site's CCTV analytics, retail counting, small pilots. | Stream count per box — size it on real footage, not a spec sheet. |
| Industrial edge PCs | Fanless, wide-temperature systems for DIN-rail, wall or cabinet mounting. | Visual inspection and monitoring beside production lines. | Thermal limits in hot cabinets; I/O such as serial or fieldbus. |
| Edge servers (multi-NPU or GPU) | Rack servers with several accelerators in a site server room. | Many cameras, heavy models or several AI applications per site. | Power, cooling and noise; plan rack space and UPS capacity. |
Choosing between them starts with the workload. See how much TOPS a system needs and NPU vs GPU for edge inference.
Compact · 0–2UCompact Edge AI Appliance
A quiet, power-efficient appliance that brings AI inference to store back-offices, reception desks and small server cabinets. Point your cameras and data at it — the intelligence stays on-site.
- Application
- Retail · offices · CCTV analytics
- Processor & accelerator
- NPU options available
- AI performance
- Sized per workload
- Networking
- Multi-GbE · PoE options
- Deployment
- Desk · wall · small rack
- Single-site CCTV and video analytics
- Retail footfall, queue and occupancy insights
- Small-site sensor and machine analytics
- Pilots that can scale into multi-site rollouts
Final specifications are configured per project.
Multi-NPU Edge Server
When one appliance is not enough, a multi-NPU server runs several AI applications side by side — vision analytics on one accelerator, private AI on another — from a single rack chassis.
- Application
- Concurrent AI services · analytics
- Processor & accelerator
- Multi-NPU configurations
- AI performance
- Sized per stream & model mix
- Networking
- GbE / 10GbE options
- Deployment
- 19″ rack · server room
- Multiple concurrent AI applications on one server
- Multi-stream video analytics across many cameras
- Site-level aggregation of camera and sensor analytics
- Departmental AI platforms with headroom to grow
Final specifications are configured per project.
Multi-accelerator
High performanceGPU Edge AI Server
For workloads that need serious throughput — demanding vision models, large camera fleets and multi-model pipelines — a GPU edge server delivers data-centre class inference inside your own site.
- Application
- Heavy vision · many streams · multi-model
- Processor & accelerator
- GPU options available
- AI performance
- Sized per model class
- Networking
- 10GbE+ options
- Deployment
- Rack · on-premise
- Demanding vision models and high camera counts
- Vision-language models and on-site language models where a site needs them
- One server per site serving several AI applications
- High-throughput, always-on inference
Final specifications are configured per project.
Industrial Edge AI
Fanless, rugged systems built for dust, heat and vibration — mounted beside the line, in cabinets and on walls, wherever ordinary servers don't survive.
- Application
- Factory · automation · harsh sites
- Processor & accelerator
- NPU / CPU+accelerator
- AI performance
- Sized per line or cell
- Networking
- GbE · serial-fieldbus options
- Deployment
- DIN · wall · cabinet
- Visual inspection and defect detection on lines
- Equipment monitoring in harsh environments
- Silent, fanless operation beside sensitive equipment
- Cabinet, DIN-rail and wall installations
Final specifications are configured per project.
Fanless · ruggedOne Platform. Multiple AI Accelerators.
Hardware selection depends on the workload. EdgeAIIndia can specify NPU, GPU or combined platforms — never locked to one chip vendor.
How we select your hardware
- Model requirements — vision, LLM, embeddings or custom models
- Inference performance targets
- Power consumption and cooling constraints
- Number of concurrent camera streams
- Model size and quantisation strategy
- Required latency
- Operating environment — office, factory or field
- Budget and ownership model
Deployment in Five Stages
Understand the Workload
Identify the AI application, data source and performance requirements.
Select the Hardware
Choose CPU, NPU, GPU or accelerator according to the workload.
Deploy the AI Stack
Install Linux, drivers, AI runtime, containers and required models.
Integrate Your Data
Connect cameras, sensors, documents, network systems or business applications.
Run AI Locally
AI inference operates on your edge infrastructure — on your terms.
Have an edge AI workload? Let's find the right hardware for it.
Tell us what you want AI to analyse on-site — cameras, machines, sensors or another workload — and we'll help map the infrastructure.