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// Hardware Selector — Engine 01

Find the right hardware
for your deployment

Input your deployment constraints. The engine calculates compute requirements, power envelope, and returns the optimal Edge AI platform with purchase links.

// Define requirements

01
Deployment Scenario
// What are you building?
02
Primary Model Type
// Inference workload
03
Video Streams
// Concurrent camera inputs
// Higher stream counts require proportionally more compute
04
Power Constraint
// System power budget
05
Deployment Environment
// Physical installation context
// Select all parameters to continue
Select one option from each category above
// Calculating optimal configuration…

// Recommendation

// Primary Recommendation
—
—
—
MATCH CONFIDENCE —

// Alternative options

// machine-readable output — application/json

                    


                    

                    
                


                
                

                    

                        

                            
What this Hardware Selector decides

                            
This tool recommends the best-fit edge AI compute platform for a deployment based on five decision inputs: deployment scenario, model type, concurrent stream count, power budget, and installation environment. It is designed for engineers evaluating Jetson, Coral TPU, Hailo-8, RK3588, and AGX Orin-class hardware for real deployments.

                        

                    

                


                

                    
// Inputs considered

                    

                        

                            
01

                            
Scenario

                            
Single-camera vision, multi-camera analytics, industrial inspection, low-power sensor, edge server, or robotics.

                        

                        

                        

                            
02

                            
Model Type

                            
Classification, object detection, segmentation, pose estimation, or multi-model pipelines.

                        

                        

                        

                            
03

                            
Streams + Constraints

                            
Concurrent video streams, power envelope, and deployment environment determine realistic platform fit.

                        

                    

                


                

                    
// How recommendations are scored

                    
This decision engine weighs platform fit across compute capability, video stream capacity, power envelope, thermal and environmental suitability, and ecosystem alignment. A platform receives a higher match confidence when its realistic deployment characteristics align with the workload rather than just matching a single benchmark number.

                    
    
                        
  • Compute fit for the selected model type and stream count
    • 
                        
  • Power compatibility with the stated deployment budget
    • 
                        
  • Environmental suitability for fanless, industrial, mobile, or rack deployments
    • 
                        
  • Platform flexibility for single-model versus multi-model pipelines
    • 
                        
  • Alternative recommendations when the top choice is power-, cost-, or cooling-constrained
    • 
                    

                


                

                    
// What the output includes

                    
    
                        
  • Primary recommendation: the best-fit hardware platform for the selected constraints
    • 
                        
  • Match confidence: a percentage score representing how well the platform aligns with the full decision profile
    • 
                        
  • Alternatives: secondary options that remain viable for the same workload
    • 
                        
  • Deployment metrics: compute TOPS, power range, stream capacity, cooling assumptions, and estimated cost
    • 
                        
  • Machine-readable JSON: a structured result that can be copied, shared, or reused by downstream systems
    • 
                    

                


                

                    
// Worked examples

                    

                        

                            
// Example 01

                            
Battery-powered sensor node

                            
Single camera, classification, 1 stream, ultra-low power, fanless → Coral TPU is usually favored for per-watt efficiency and passive deployment.

                        

                        

                            
// Example 02

                            
Retail or warehouse analytics

                            
Multi-camera object detection, 4–8 streams, moderate power → Jetson Orin Nano or Hailo-8 often emerge as the best balance of throughput and deployment practicality.

                        

                        

                            
// Example 03

                            
High-throughput multi-model deployment

                            
Edge server, multi-model pipeline, 16 streams, high power → Jetson AGX Orin is typically recommended when maximum headroom and production-grade scaling are required.

                        

                    

                


                

                    
// Example machine-readable output

                    
{
  "schema": "edgeaistack/decision/v1",
  "inputs": {
    "scenario": "multi_camera",
    "model": "object_detection",
    "streams": 8,
    "power_constraint": "moderate",
    "environment": "industrial"
  },
  "recommendation": {
    "device": "Hailo-8",
    "device_id": "hailo8",
    "match_confidence": 89,
    "alternatives": ["jetson_orin_nano", "jetson_agx_orin"]
  }
}

                


                

                    
// FAQ


                    
When should I choose Jetson over Coral TPU?

                    
Choose Jetson when you need broader model support, CUDA-based workflows, robotics stack compatibility, or more general-purpose vision flexibility. Choose Coral TPU when ultra-low power and TensorFlow Lite deployment are the main constraints.


                    
Does higher stream count always require a larger platform?

                    
Usually yes. More concurrent streams increase compute requirements, sustained thermal load, and memory bandwidth pressure, which is why stream count is a primary sizing input.


                    
Is fanless deployment treated as a hard constraint?

                    
Yes. Fanless or sealed deployments favor platforms with lower sustained thermal output and more realistic passive-cooling envelopes.


                    
Does this tool assume quantized models?

                    
The recommendation logic assumes practical deployment fit by platform class and workload type. In real deployments, quantization strategy, framework support, and model conversion constraints should still be validated before final hardware purchase.