What does the GPU Sizing Calculator decide?

The GPU Sizing Calculator estimates required compute for an edge AI inference workload and recommends the best-fit edge platform based on model architecture, precision, batch size, latency target, and throughput requirement.

Does a larger batch size always mean a better platform match?

Not always. Larger batch sizes increase throughput but also increase latency and memory requirements, so the best platform depends on the balance between throughput target and acceptable latency.

// Decision Engine 02

Size the right GPU compute for your inference workload

Estimate compute requirements for YOLO, ResNet, segmentation, and ViT models on NVIDIA Jetson, Google Coral, and Hailo accelerators. Input model architecture, precision, batch size, and throughput target to get required TOPS, platform recommendation with headroom, and ranked alternatives.

// Define requirements

Model Architecture

// Primary inference model family

YOLO Detection

YOLO12n

YOLO12s

YOLO12m

YOLO12l

YOLO12x

YOLO11n

YOLO11s

YOLO11m

YOLO11l

YOLO11x

YOLOv8n

YOLOv8s

YOLOv8m

YOLOv8l

YOLOv8x

Classification

MobileNet V3-Small

MobileNet V3-Large

MobileNet V2

MobileNet V1

EfficientNet B0

EfficientNet B1

EfficientNet B2

ResNet-50

ResNet-101

ResNet-152

Lightweight Detection

EfficientDet D0

EfficientDet D1

EfficientDet D2

EfficientDet D3

SSD MobileNet V1

SSD MobileNet V2

Segmentation

SAM ViT-B

SAM ViT-L

SAM ViT-H

DeepLabV3 MobileNet

Precision / Quantization

// Model precision affects TOPS requirements directly

FP32 (full)

FP16 (half)

BF16 (brain float)

INT8 (quantized)

INT4 (aggressive)

Batch Size

// Concurrent inference batch — 1 = real-time, higher = throughput

// Larger batches improve throughput but increase latency

Latency Requirement

// Maximum acceptable inference latency per frame

< 33ms (real-time 30fps)

33–100ms (low latency)

100–500ms (moderate)

> 500ms (batch acceptable)

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…

// Results Summary

// Primary Recommendation

—

Confidence—

← Hardware Selector

// Alternatives

// 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

Model + Precision

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

Batch Size

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

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
Compute estimate: the planning-envelope compute requirement for the workload (TOPS)
Compute envelope: the platform's peak TOPS capability at selected precision
Planning headroom: the ratio of platform envelope to compute estimate
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.