In this episode, Brian Benz—joined by Ayan Gupta—shows developers how to build a generative AI image solution using GPU containers in Azure. The session highlights code integration with GitHub Copilot and practical scaling techniques.

Scaling Generative AI with GPU-Powered Containers on Azure

In this practical video masterclass, Brian Benz demonstrates how developers can run generative AI workloads at scale using GPUs, containerized applications, and Microsoft Azure. The session centers on creating watercolor-style images using Stable Diffusion, orchestrated through a Spring Boot application and accelerated by ONNX Runtime and Nvidia CUDA.

Key Highlights

• GPU Acceleration for AI: Brian compares CPU and GPU performance for Stable Diffusion image generation, showing a 5x speed improvement.
• Local and Cloud Deployment: The demo illustrates running AI workloads locally and within Azure Container Apps using Docker containers.
• AI Architecture Details:
  • ONNX Runtime: Model interoperability and deployment
  • Stable Diffusion Models: Pulled from Hugging Face’s ONNX Community
  • SD4J (Stable Diffusion for Java): Java bindings for model execution
  • Version Management: Ensuring ONNX Runtime, SD4J, and CUDA compatibility
• GitHub Copilot for Integration: Used in agent mode beside Claude Sonnet 4.5 to generate most of the integration code, drastically accelerating development.
• Text Embedding Similarity: Brief demo shows supplementary AI capabilities for text analysis.
• Performance Breakdown:
  • GPU: ~90 seconds/image
  • CPU: >5 minutes/image
• Challenges and Solutions: Managing library versions, container orchestration, and integration complexities.

Step-by-Step Topics

1. Understanding GPU Necessity: Why GPUs are critical for generative AI workloads
2. Spring Boot + Stable Diffusion Setup: Using Java and SD4J in production containers
3. Docker Container Deployment: Running AI locally and on Azure
4. Architecture Deep Dive: How ONNX, CUDA, and SD4J interact
5. Integration using Copilot Agent Mode: Leveraging AI-assisted code writing
6. Managing Compatibility: Synchronizing ONNX Runtime, SD4J, CUDA versions
7. When to Use Local vs External Services: Decision factors for AI hosting
8. Repository and Resources:
   • Sample code
   • Model sources (Hugging Face)
   • Guidance: aka.ms/JavaAndAIForBeginners

Technologies Featured

• Azure Container Apps
• Docker
• ONNX Runtime
• Nvidia CUDA
• Stable Diffusion (Hugging Face ONNX Community)
• SD4J (Stable Diffusion for Java)
• Spring Boot (Java)
• GitHub Copilot (agent mode)
• Claude Sonnet 4.5

Practical Insights

• Efficient architecture for running generative AI with GPU acceleration
• Tips for managing model versions and dependencies
• Real-world demonstration of AI-assisted coding workflows

Learn More

Visit aka.ms/JavaAndAIForBeginners for tutorials, code samples, and additional AI resources.

Author: Brian Benz (with Ayan Gupta, Microsoft Developer)