Artificial Intelligence

Azure Delivers Strong MLPerf Inference v6.1 Results with NVIDIA GB300 & GB200

Azure / By / Published / Updated 09/16/2026 / 2 minutes of reading

By Mark Gitau and Azin Heidarshenas In the MLPerf Inference v6.1 round, Azure demonstrated strong Deepseek-R1 performance on GB300 NVL72 and GB200 NVL72 platforms. Azure submitted GB200 results at 18-node (1-rack) and 72-node (4-rack) scale, and GB300 results at 18 nodes (1 rack), marking our first rack-scale and cross-rack MLPerf Inference submissions. According to results published […]

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Scaling AI Storage: Azure Managed Lustre excels in MLPerf Storage v3.0

Azure / By / Published / Updated 09/02/2026 / 6 minutes of reading

In large scale AI clusters storage is responsible for delivering training data fast enough to keep accelerators busy and completing checkpoint saves and recoveries quickly enough to avoid costly stalls. MLPerf Storage is an industry-standard benchmark developed by MLCommons that reproduces the I/O patterns of different stages of AI pipelines covering training, checkpointing, context cache

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Grok 4.6 comes to Microsoft Foundry Models: Built for long-horizon reasoning and complex workflows

Artificial Intelligence / By / Published / Updated 08/26/2026 / 4 minutes of reading

The next wave of AI applications won’t be defined by how quickly models answer questions. They’ll be defined by how much work they can complete. Whether resolving repository issues, navigating codebases, generating engineering designs, or executing multi-step enterprise workflows, developers are increasingly building systems that need models capable of staying on task across complex, long-running

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New Azure SQL Foundations video series with GitHub samples

SQL Server / By / Published / Updated 08/26/2026 / 1 minute of reading

Bob Ward and I recently released a series of videos, read more in the original blog post or go directly to the series on YouTube. The Azure SQL Database Foundations series are four videos that take you from your first Hyperscale database to AI features running against your own operational data. We also included how to assess

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Consume Dataverse MCP server in Microsoft Foundry-Bringing business data to AI Agents

Artificial Intelligence / By / Published / Updated 08/25/2026 / 4 minutes of reading

Introduction Building AI agents that can actually do things in your business systems—create records, update opportunities, trigger workflows—typically requires weeks of custom integration work. The Dataverse MCP Server changes that. In this tutorial, you’ll connect a Microsoft Foundry agent to your Dataverse environment in under 30 minutes, giving your AI agent secure, real-time access to

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Benchmark Retrieval Patterns for Copilot Studio and Foundry IQ

Artificial Intelligence / By / Published / Updated 08/24/2026 / 16 minutes of reading

Part 2 of a series. Part 1, Grounding Copilot Studio Agents with Azure AI Search and Foundry IQ, defined five grounding patterns. This article gives you a reproducible way to compare them without mixing latency boundaries, billing units, or quality signals. Reference implementation, not a production template. This article is for architects, agent developers, and

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Enabling A2A endpoint and Agent Card for a Hosted Agent

Artificial Intelligence / By / Published / Updated 08/24/2026 / 6 minutes of reading

Building an agent shouldn’t require you to reinvent platform capabilities. With Microsoft Foundry, hosted agents inherit essential capabilities such as authentication, ingress management, A2A transport, and discovery endpoints, freeing developers to focus on business logic and user value. This article demonstrates the quickest route from a local containerized agent to a fully discoverable A2A endpoint

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Building Production-Ready AI Agents in Microsoft Foundry: 10 Lessons Learned

Artificial Intelligence / By / Published / Updated 08/24/2026 / 7 minutes of reading

A Real-World Scenario Imagine a customer support agent that answers invoice questions. During testing, everything works perfectly. But in production: The Finance API occasionally times out. The knowledge base contains outdated information. Tool calls fail during peak traffic. Token consumption rises unexpectedly. The result isn’t a broken AI model. It’s an unreliable system. This is

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Expanding Open Model Choice in Microsoft Foundry with New DeepSeek and NVIDIA Nemotron Models

Artificial Intelligence / By / Published / Updated 08/21/2026 / 5 minutes of reading

Open models are driving a new wave of AI innovation, giving organizations more freedom to choose the models that best fit their requirements for performance, cost, latency, customization, and governance. As the open model ecosystem evolves rapidly, developers want access to the latest innovations without being locked into a single model provider or deployment approach.

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Model router updates: new regions, a refreshed model pool, and understanding the hill climb

Artificial Intelligence / By / Published / Updated 08/21/2026 / 6 minutes of reading

Across Microsoft, “hill climbing” has become shorthand for how real AI progress happens: not in one dramatic leap, but through a disciplined loop. Microsoft AI defines the hill climb as an organization that continuously improves, cycle after cycle, through more compute, better data, and sharper evaluation. Reinforcement fine-tuning in Foundry defines it as improving the deployable model package one

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