Microsoft Events engineers present an in-depth overview of Azure’s supercomputing infrastructure, offering practical strategies and bottleneck detection for large-scale AI model training.

Pushing Limits of Supercomputing Innovation on Azure AI Infrastructure

Overview

This session from Microsoft Ignite 2025 explores the technical validation process underpinning Azure’s AI infrastructure. Focusing on large GPU clusters, the talk covers:

  • History of model evolution (2019 to present)
  • Fundamental AI infrastructure stack:
    • Compute, network, storage, managed services
  • GPU Generations:
    • GB200/GB300 vs H100 workloads
  • Data ingestion at scale in Azure Cloud
  • Performance growth & scale:
    • Azure’s track record in production-scale supercomputing
  • Validation processes:
    • Early detection of bottlenecks in GPU performance, large model throughput
  • Large-scale validation:
    • GRAC 314B Model case study

Key Takeaways

  • Azure enables efficient training for multi-billion parameter models
  • Early infrastructure validation reduces cost and accelerates time-to-results
  • New GB300 GPUs announced for general availability on Azure
  • Improvements in compute, networking, and storage year-over-year
  • Real-world methodology for maximizing AI training throughput

Chapter Highlights

  1. Model Evolution (2019-2025):
    • Rapid advances in model scale and complexity
  2. Core Infrastructure:
    • Azure’s AI infrastructure stack powering modern workloads
  3. GPUs on Azure:
    • From GB200 and GB300 to H100 workload support
  4. Large Language Models (LLM):
    • Pretraining LLAMA and similar architectures on Azure supercomputers
  5. Validation and Bottleneck Detection:
    • How engineers monitor, validate, and optimize large-scale GFP kernel operations
  6. GRAC 314B Model:
    • Validation processes for extremely large language models

Practical Insights

  • Strategies for predictable throughput and faster model training
  • Detailed monitoring methods for AI infrastructure health
  • How bottleneck detection drives performance and cost efficiency in cloud AI
  • Azure’s methodology for production-scale model deployment

Resources

Speakers

  • Hugo Affaticati
  • Nitin Nagarkatte

This session is recommended for engineers and technical leaders seeking hands-on knowledge about Azure’s approach to supercomputing for AI and ML workloads.