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Learn how Azure Storage powers AI inference at scale. This session explores how to securely bring enterprise data to AI models, accelerate AI workloads with high-performance storage, and reduce GPU idle time through faster model loading and optimized data access. See how Azure Storage integrates with Microsoft and open-source AI frameworks to improve performance, lower costs, and enable scalable agent-based applications.
𝗦𝗽𝗲𝗮𝗸𝗲𝗿𝘀:
* Saurabh Sensharma
* Vishnu Charan TJ
𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗜𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻:
This is one of many sessions from the Microsoft Build 2026 event. View even more sessions on-demand and learn about Microsoft Build at https://build.microsoft.com
OD870 | English (US) | Cloud platform & data
Pre-recorded
#MSBuild
Chapters:
0:00 – Introduction to Azure Storage for AI Workloads
00:01:21 – Overview of Storage for AI and AI for Storage
00:02:05 – Azure Storage Integration Across AI Stack and Infrastructure
00:03:07 – Azure Storage Clients and Tools for AI Workloads
00:05:03 – Paths to Run AI Workloads: Foundry, AKS, and IaaS with Storage
00:06:20 – Storage Requirements for Agentic Inference and Their Roles
00:07:11 – Inference Optimization through Prompt Caching
00:10:05 – Explicit Caching with Azure Blob and NIXL Integration Demo
00:14:01 – Fast Model Loading and Distribution with Run:AI Streamer and Distributed Cache
00:19:00 – Bringing Enterprise Data to AI via Azure Integrations and Foundry IQ
00:24:03 – Introducing Storage Center and Session Recap Read More Microsoft Developer
