Microsoft is excited to attend NVIDIA GTC Berlin October 20 through 22, bringing news across the AI stack. We are expanding the infrastructure and platform capabilities organizations need to build and run AI across cloud and customer-controlled environments. This includes new capabilities for Microsoft Sovereign Cloud supported by NVIDIA accelerated computing, open-model choice with NVIDIA Nemotron models through Fireworks AI on Azure, and GPU-accelerated analytics in Microsoft Fabric. Find us at booth 3066 and off site at the JW Marriott.
What ties these areas together is a question customers in every industry are asking us: how can we use the latest models and infrastructure while retaining control over where our workloads run and who can access them? For many organizations, especially those in regulated industries, answering these questions is essential to expanding the use of AI and agentic workloads. Through our work with NVIDIA, these are some of the challenges we are helping customers address in Berlin.
Control and choice, wherever AI runs
Teams want the newest, most capable models running on the most performant hardware, all while keeping control over their data, models, and outputs. The public cloud is the right solution for many workloads.
Other workloads may need to run in specialized, secured environments because of requirements for data residency, operator access, connectivity, latency, or operational control. Microsoft is building solutions to meet customers across this range of needs.
Microsoft’s approach to sovereign AI starts with the level of control each workload requires. Depending on the workload, this can include control over where data and models reside, who can access and operate the environment, how AI workloads are governed, and whether critical operations can continue when cloud connectivity is restricted or unavailable. The goal is to help organizations meet these requirements while maintaining access to modern cloud and AI capabilities.
A consistent platform, from Cloud to the edge
Microsoft provides resilient infrastructure for AI demands across Azure and Azure Local, supported by NVIDIA accelerated computing. This hardware spans accelerated systems in Azure datacenters and NVIDIA GPUs in Azure Local environments that customers operate themselves. It connects two important needs: building and scaling AI in the cloud, and deploying and operating where latency, connectivity, data residency, or operational control matter.
This gives organizations more choice in where they build, deploy, and operate AI workloads, while applying consistent management and governance across their environments.
Sovereign AI on Azure Local with NVIDIA accelerated computing
With Sovereign Private Cloud, built on Azure Local, organizations can bring AI closer to sensitive data and physical operations across connected and disconnected environments. At Microsoft Ignite, Foundry Local on Azure Local will become generally available for local model deployment and inference. This enables organizations to run AI models within their own environments so that models and sensitive workload information can remain under local control. Microsoft is also expanding support for grounded and agentic AI scenarios that use enterprise data within the customer’s local environment.
NVIDIA accelerated computing supports these deployments. Looking ahead, Microsoft is working to bring support for NVIDIA HGX-based multi-GPU systems to Azure Local, providing the dense accelerated infrastructure needed to run larger models with Foundry Local in customer-controlled environments. With high-performance NVIDIA GPUs on Azure Local, organizations can build, test, and deploy AI workloads in sovereign and edge environments.
Beyond Azure Local, Microsoft is also a key partner in the NVIDIA OpenShell Reference Architecture for Safe AI Agents, supporting the extension of secure agentic AI from the datacenter to the edge, including the recently launched NVIDIA RTX Spark and DGX Station running Windows.
Confidential virtual machines on Azure Local are generally available, extending hardware-based data-in-use protection to customer-controlled infrastructure. Hardware-encrypted memory and attestation help protect supported workloads from the host operating environment and infrastructure administrators.
Microsoft is a key partner in the NVIDIA OpenShell Reference Architecture for Safe AI Agents, supporting the extension of secure agentic AI from the data center to the edge, including the recently launched NVIDIA RTX Spark and DGX Station running Windows.
For data in use, confidential computing adds protection enforced from the silicon up, so security leaders can explain where trust is enforced at each layer.
Open models you can choose, and change: NVIDIA Nemotron with Fireworks AI on Azure
Customers continue to prioritize avoiding model provider lock-in. Fireworks AI is bringing open models including the family of NVIDIA Nemotron models to Azure as managed endpoints, so teams can put open models into production without having to build and run the serving stack themselves. Fireworks serves these models on NVIDIA accelerated computing with TensorRT-LLM and Dynamo.
The same emphasis on model choice carries into Microsoft Foundry, where NVIDIA open models are available alongside a broader model catalog. For workloads that need to run within a customer-controlled environment, Foundry Local provides a path to deploy supported models locally.
Faster data for AI: Microsoft Fabric with NVIDIA accelerated computing
As analytics become more conversational, the warehouse has to answer more queries, more interactively, and at greater scale. To meet that demand, GPU-accelerated query acceleration, powered by NVIDIA accelerated computing, is now in public preview. Based on award-winning research, it accelerates your analytical workloads without requiring changes to your data or your SQL queries, with benefits that become increasingly important under the high-concurrency patterns AI experiences create.
Our customers across industries are already seeing value from GPU-accelerated Fabric Data Warehouse:
“In healthcare, timely insights matter, and GPU-accelerated Fabric Data Warehouse will help ensure critical data is available when clinicians and leaders need it most. We’re seeing up to 5x improvement in our query speeds, which allows our teams to spend less time managing performance and more time delivering meaningful insights.”
– Shaun McDonald, IT Manager, UNC Health
“Our experience with GPU-accelerated Fabric Data Warehouse has been excellent. The capability integrates seamlessly into our existing architecture and has delivered meaningful performance improvements across a range of queries, with complex workloads running 3.4x faster at single concurrency and more consistently.
At WTW, we operate a shared data platform supporting multiple applications and reporting workloads. GPU-accelerated Fabric Data Warehouse enables us to serve high volumes of queries more efficiently, directly improving the responsiveness of our downstream analytics and reporting. We see the potential for this capability to help unify how we deliver data across our platform.”
– Andrew Bradbrook, Director – Systems Architecture, WTW
Better data for physical AI: NVIDIA Cosmos Data Factory on Azure
For teams building autonomous vehicles, robotics and vision AI agents, creating and improving physical systems intelligence depends on having the right training data not just more of it. Rare conditions are difficult to capture, and turning raw recordings into useful datasets often means moving between disconnected tools and manual processes. NVIDIA Cosmos Data Factory brings data curation, processing and synthetic data generation into a connected workflow. Its agentic blueprints and composable skills help developers prepare high-quality real and synthetic datasets, expanding coverage of the scenarios their models need to handle.
This week NVIDIA Cosmos Data Factory’s AV Dataset Generation blueprint becomes generally available on Azure. The workflow turns raw driving video and vehicle signals into curated, searchable, augmented and evaluated datasets, with engineers setting the target scenarios and data-quality criteria. A rare cut-in on a wet road, for example, can become the starting point for finding similar recordings and generating additional variations. NVIDIA supplies the data workflows and open models, while Azure supplies the compute to run them at scale helping teams spend less effort preparing data and more time developing and testing.
The next step is connecting that data foundation to model improvement. NVIDIA Alpamayo Agentic Factory, coming soon to Azure, builds on Cosmos Data Factory to connect targeted dataset creation with driving-model post-training and simulation evaluation. Engineers define the goal, review outputs and guide subsequent experiments, while agents plan and execute the development steps. The aim is a repeatable path from a known driving failure to an evaluated model improvement, with each iteration informing what to test next.
From announcements to availability
Across these announcements, Microsoft and NVIDA are expanding the choices available to originations building expand the options available to organizations building AI across cloud and customer-controlled environments. Confidential virtual machines on Azure Local are generally available today. At Microsoft Ignite, Foundry Local on Azure Local will become generally available for local model deployment and inference across connected and disconnected environments.
What’s next
Berlin is the first stop in Microsoft’s FY27 NVIDIA GTC series. Next is NVIDIA GTC Washington, D.C., November 30 to December 3, followed by GTC San Jose, March 15 to 18, 2027. There is more on Azure AI infrastructure coming at Microsoft Ignite, hosted in San Francisco November 16 – 20.
If you are in Berlin, stop by booth 3066 on October 21 and 22, or join our 15-minute theater session, “Sovereign AI, From Azure to the Edge,” on Thursday, October 22 at 11:00 CEST or find us at our space at the JW Marriott. Bring your hard-hitting and pressing questions!
Learn more: Microsoft Sovereign Cloud · Azure AI infrastructure, silicon to systems · Next in the series: Microsoft at NVIDIA GTC Washington, D.C.