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// Decision Engine 02

Size the right GPU compute
for your inference workload

Input your model architecture, batch size, latency target, and throughput requirement. The engine calculates TOPS demand and returns the optimal edge platform with projected performance figures.

// Define requirements

01
Model Architecture
// Primary inference model family
YOLOv8n (nano)
YOLOv8s (small)
YOLOv8m (medium)
ResNet-50
MobileNetV3
SAM / ViT (large)
02
Precision / Quantization
// Model precision affects TOPS requirements directly
FP32 (full)
FP16 (half)
INT8 (quantized)
INT4 (aggressive)
03
Batch Size
// Concurrent inference batch — 1 = real-time, higher = throughput
1
2
4
8
16
32
// Larger batches improve throughput but increase latency
04
Latency Requirement
// Maximum acceptable inference latency per frame
< 33ms (real-time 30fps)
33–100ms (low latency)
100–500ms (moderate)
> 500ms (batch acceptable)
05
Throughput Target
// Required inferences per second across all streams
< 10 IPS
10–50 IPS
50–200 IPS
> 200 IPS
// Select all parameters to continue
// Calculating compute requirements…

// GPU Sizing Recommendation

// Primary Recommendation
—
—
—
COMPUTE MATCH—
← Hardware Selector

// Alternative platforms

// machine-readable output — application/json

    


    

    
  


  
  

    

      

        
What this GPU Sizing Calculator decides

        
This tool estimates the compute class required for an edge AI inference workload and recommends the best-fit platform based on five decision inputs: model architecture, precision or quantization level, batch size, latency requirement, and throughput target. It is intended for engineers choosing between Jetson, Hailo-8, Coral TPU, RK3588, and AGX Orin-class hardware.

      

    

  


  

    
// Inputs considered

    

      

        
01

        
Model + Precision

        
The model family and precision mode determine base compute demand. Larger models and higher precision require more TOPS.

      

      

      

        
02

        
Batch Size

        
Batch size changes throughput efficiency and latency pressure. Larger batches generally improve throughput but increase response time and memory demand.

      

      

      

        
03

        
Latency + Throughput

        
Real-time workloads need lower latency, while batched inference can trade latency for throughput. The engine weighs both when sizing the platform.

      

    

  


  

    
// How recommendations are scored

    
This engine estimates effective compute demand from the selected model, precision, batch size, latency class, and throughput target. It then compares that requirement against available platform capability and returns the best-fit recommendation with headroom, confidence, and alternatives.

    
    
      
  • Base model complexity and precision-adjusted TOPS requirement
    • 
      
  • Batch-size effect on throughput and practical deployment fit
    • 
      
  • Latency compatibility for real-time versus batch-friendly workloads
    • 
      
  • Platform headroom relative to required compute
    • 
      
  • Alternative recommendations when multiple platforms satisfy the workload
    • 
    

  


  

    
// What the output includes

    
    
      
  • Primary recommendation: the best-fit compute platform for the selected workload
    • 
      
  • Required TOPS: the estimated compute demand of the workload
    • 
      
  • Available TOPS: the platform capability used for the recommendation
    • 
      
  • Headroom: the safety margin between requirement and platform capability
    • 
      
  • Alternatives: other viable platforms that can support the workload
    • 
      
  • Machine-readable JSON: a structured result for copying, sharing, or downstream reuse
    • 
    

  


  

    
// Worked examples

    

      

        
// Example 01

        
Lightweight real-time model

        
MobileNetV3, INT8, batch 1, real-time latency, low throughput → Coral TPU or RK3588 often fit when ultra-low power or low cost is prioritized.

      

      

        
// Example 02

        
Mainstream detection workload

        
YOLOv8s, FP16, batch 4, low latency, medium throughput → Jetson Orin Nano or Hailo-8 usually provide the best balance of headroom and deployment practicality.

      

      

        
// Example 03

        
High-demand model or throughput target

        
YOLOv8m or SAM/ViT, larger batches, real-time or high throughput → Jetson AGX Orin is typically required when smaller edge platforms lack sufficient compute headroom.

      

    

  


  

    
// Example machine-readable output

    
{
  "schema": "edgeaistack/gpu-sizing/v1",
  "inputs": {
    "architecture": "yolov8s",
    "precision": "fp16",
    "batch_size": 4,
    "latency": "low",
    "throughput": "medium"
  },
  "computed": {
    "required_tops": 9.6
  },
  "recommendation": {
    "device": "Jetson Orin Nano",
    "device_id": "jetson_orin_nano",
    "available_tops": 40,
    "headroom_x": 4.2,
    "match_confidence": 88,
    "alternatives": ["hailo8", "rk3588"]
  }
}

  


  

    
// FAQ


    
Why does precision change the recommendation?

    
Precision directly changes compute demand. FP32 requires more compute than FP16, while INT8 and INT4 can dramatically reduce required TOPS when the model and deployment stack support quantization.


    
Does a larger batch size always improve the result?

    
No. Larger batches can improve throughput efficiency, but they also increase latency and memory pressure. The best result depends on whether the workload is latency-sensitive or throughput-oriented.


    
When is Jetson AGX Orin usually required?

    
Jetson AGX Orin is usually the best fit when the selected model is large, the throughput target is high, or the workload needs meaningful compute headroom for future scaling.


    
Is this tool only for GPUs?

    
No. The tool sizes edge inference compute across GPU-like and accelerator-class platforms, including Jetson modules, Hailo-8, Coral TPU, and RK3588-class NPUs.