Deep X G20 Series Performance Benchmark Report

Test Date: May 2025

Test Version: v1.9

Testing Facility: DeepAll Technologies (Silicon Valley) Performance Lab

Executive Summary

The Deep X G20 series demonstrates exceptional AI computing capabilities in comprehensive performance testing, particularly the flagship G20 Pro Max model with its 1824 TOPS combined computing power, surpassing NVIDIA DGX Spark in multiple benchmarks. This report details performance metrics across various workloads.

Key Findings

Test Environment

Hardware Configuration

Test Platform CPU GPU Memory Storage
Deep X G20 Intel Core Ultra 7 265F (20-core) RTX RRO 2000 (8GB) 96GB DDR5-6400 2TB NVMe Gen4
Deep X G20 Pro Intel Core Ultra 7 265 (20-core) RTX RRO 4000 (16GB) 128GB DDR5-6400 4TB NVMe Gen4
Deep X G20 Pro Max Intel Core Ultra 9 285 (24-core) RTX RRO 5000 (24GB) 192GB DDR5-6400 4TB NVMe Gen4
NVIDIA DGX Spark 20-core ARM Processor Integrated GPU 128GB LPDDR5x 1TB NVMe

Software Environment

Performance Test Results

1. Large Language Model Inference Performance

LLaMA 70B Inference Test

Test Conditions: Batch size=1, Sequence length=2048, 4-bit quantization

Model Throughput (tokens/s) First Token Latency (ms) Relative Performance
NVIDIA DGX Spark 185 142 100% (Baseline)
Deep X G20 156 168 84%
Deep X G20 Pro 245 107 132%
Deep X G20 Pro Max 338 78 182%

GPT-Style Model Performance (13B Parameters)

Test Conditions: FP16 precision, Batch size=8

Model Batch Throughput (req/s) Average Latency (ms) GPU Utilization
NVIDIA DGX Spark 12.5 640 78%
Deep X G20 15.2 526 85%
Deep X G20 Pro 24.8 323 92%
Deep X G20 Pro Max 31.6 253 94%

2. Image Generation Performance

Stable Diffusion XL Test

Test Conditions: 1024×1024 resolution, 50 sampling steps

Model Generation Speed (img/min) Batch of 16 (seconds) VRAM Usage
NVIDIA DGX Spark 18 53.3 N/A
Deep X G20 12 80.0 7.2GB
Deep X G20 Pro 20 48.0 14.5GB
Deep X G20 Pro Max 30 32.0 21.8GB

3. Computer Vision Performance

YOLOv8 Real-time Detection

Test Conditions: 1080p video stream, YOLOv8x model

Model FPS Latency (ms) mAP@0.5
NVIDIA DGX Spark 67 14.9 0.89
Deep X G20 85 11.8 0.89
Deep X G20 Pro 142 7.0 0.89
Deep X G20 Pro Max 195 5.1 0.89

4. AI Training Performance

BERT-Large Fine-tuning

Test Conditions: Batch size=32, Sequence length=512

Model Training Speed (samples/s) Convergence Time (hours) Energy Consumption (kWh)
NVIDIA DGX Spark 320 8.2 1.39
Deep X G20 285 9.2 2.76
Deep X G20 Pro 480 5.5 1.65
Deep X G20 Pro Max 570 4.6 1.38

5. Multi-modal AI Performance

CLIP Model Inference

Test Conditions: Image-text matching task, Batch size=128

Model Throughput (pairs/s) CPU Utilization GPU Utilization
NVIDIA DGX Spark 850 45% 72%
Deep X G20 920 38% 88%
Deep X G20 Pro 1,450 42% 91%
Deep X G20 Pro Max 1,880 35% 93%

Energy Efficiency Analysis

Performance per Watt (TOPS/W)

Model Peak Power (W) AI Performance (TOPS) Efficiency (TOPS/W)
NVIDIA DGX Spark 170 1000 5.88
Deep X G20 300 798 2.66
Deep X G20 Pro 300 1334 4.45
Deep X G20 Pro Max 300 1824 6.08

Real Workload Energy Consumption

During 24-hour continuous inference testing:

Software Ecosystem Compatibility

Framework Support Test Results

Framework/Tool Deep X G20 Series NVIDIA DGX Spark
PyTorch ✅ 100% ✅ ARM version required
TensorFlow ✅ 100% ✅ Some features limited
ONNX Runtime ✅ 100% ✅ 100%
CUDA/cuDNN ✅ 100% ✅ 100%
OpenVINO ✅ Native support ❌ Not supported
DirectML ✅ Windows native ❌ Not supported
Docker/K8s ✅ 100% ✅ ARM images required

Development Toolchain

Real-World Application Testing

1. Smart Retail Scenario

Test Content: 32-channel 1080p surveillance video real-time analysis

Metric Deep X G20 Pro Max NVIDIA DGX Spark
Concurrent Processing Channels 32 20
Face Recognition Accuracy 99.2% 99.1%
Behavior Analysis Latency <50ms <80ms
24-hour Stability 100% 100%

2. Medical Imaging Analysis

Test Content: CT image 3D reconstruction and lesion detection

Metric Deep X G20 Pro Max NVIDIA DGX Spark
3D Reconstruction Speed 2.3 sec/scan 4.1 sec/scan
Detection Accuracy 96.8% 96.5%
Batch Processing Capacity 50 scans/hour 28 scans/hour

3. Industrial Quality Inspection

Test Content: PCB defect detection, 4K resolution

Metric Deep X G20 Pro Max NVIDIA DGX Spark
Inspection Speed 120 boards/min 72 boards/min
False Detection Rate 0.02% 0.03%
Minimum Defect Size 0.1mm 0.1mm

Stability and Reliability Testing

Long-term Operation Test (168 hours)

Test Item Deep X G20 Pro Max Test Result
Continuous Runtime 168 hours ✅ Pass
Performance Degradation <1% ✅ Excellent
Memory Leaks None detected ✅ Pass
Temperature Stability 65°C±3°C ✅ Stable
Error Rate 0 ✅ Perfect

Stress Test

Running continuously for 24 hours at 100% GPU load:

Conclusions and Recommendations

Performance Advantages Summary

  1. Deep X G20 Pro Max demonstrates significant performance advantages in most AI workloads
  2. x86 architecture provides clear software compatibility benefits, reducing migration costs
  3. 24GB memory enables more possibilities for large model deployment
  4. Energy efficiency reaches industry-leading levels

Application Scenario Recommendations

Scenario Recommended Model Rationale
Entry-level AI Development G20 Best value, meets basic requirements
Enterprise Deployment G20 Pro Balanced performance and cost
High-Performance Computing G20 Pro Max Maximum performance for critical tasks
Edge Inference G20/G20 Pro Controlled power consumption, sufficient performance
Large Model Serving G20 Pro Max Large memory, high throughput

Return on Investment Analysis

Based on current performance test results, Deep X G20 Pro Max compared to similar products:

Test Notes:

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